From b1c43c116f4e9189a8cd34b58782916402f14def Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 18 May 2026 09:59:51 +0900 Subject: [PATCH 01/42] [feat] initial implement for z3-solver opt pipeline --- .gitignore | 1 + docker-run.sh | 338 ++++++++++++++++++ input/z3-bench/README.md | 183 ++++++++++ input/z3-bench/evolve/build_stage1_sample.py | 73 ++++ input/z3-bench/evolve/config.yaml | 72 ++++ input/z3-bench/evolve/extract_best.py | 65 ++++ .../evolve/phase1_opt_sls/initial_program.py | 71 ++++ .../evolve/phase2_sat/initial_program.py | 178 +++++++++ .../evolve/phase3_smt/initial_program.py | 164 +++++++++ .../evolve/phase4_unified/initial_program.py | 36 ++ input/z3-bench/evolve/prepare_phase4.py | 62 ++++ input/z3-bench/evolve/run_phase.sh | 72 ++++ .../z3-bench/evolve/shared/baseline_params.py | 33 ++ input/z3-bench/evolve/shared/evaluator.py | 206 +++++++++++ input/z3-bench/evolve/shared/score.py | 49 +++ .../z3-bench/evolve/shared/stage1_sample.json | 39 ++ input/z3-bench/evolve/shared/z3_runner.py | 91 +++++ input/z3-bench/problems.csv | 51 +++ input/z3-bench/problems.jsonl | 50 +++ scripts/z3_bench_collect.py | 284 +++++++++++++++ 20 files changed, 2118 insertions(+) create mode 100755 docker-run.sh create mode 100644 input/z3-bench/README.md create mode 100644 input/z3-bench/evolve/build_stage1_sample.py create mode 100644 input/z3-bench/evolve/config.yaml create mode 100644 input/z3-bench/evolve/extract_best.py create mode 100644 input/z3-bench/evolve/phase1_opt_sls/initial_program.py create mode 100644 input/z3-bench/evolve/phase2_sat/initial_program.py create mode 100644 input/z3-bench/evolve/phase3_smt/initial_program.py create mode 100644 input/z3-bench/evolve/phase4_unified/initial_program.py create mode 100644 input/z3-bench/evolve/prepare_phase4.py create mode 100755 input/z3-bench/evolve/run_phase.sh create mode 100644 input/z3-bench/evolve/shared/baseline_params.py create mode 100644 input/z3-bench/evolve/shared/evaluator.py create mode 100644 input/z3-bench/evolve/shared/score.py create mode 100644 input/z3-bench/evolve/shared/stage1_sample.json create mode 100644 input/z3-bench/evolve/shared/z3_runner.py create mode 100644 input/z3-bench/problems.csv create mode 100644 input/z3-bench/problems.jsonl create mode 100644 scripts/z3_bench_collect.py diff --git a/.gitignore b/.gitignore index 338d65a218..597934fc78 100644 --- a/.gitignore +++ b/.gitignore @@ -56,3 +56,4 @@ htmlcov/ # For SR secrets.yaml problems +input/z3-bench/raw-data \ No newline at end of file diff --git a/docker-run.sh b/docker-run.sh new file mode 100755 index 0000000000..26fac275a3 --- /dev/null +++ b/docker-run.sh @@ -0,0 +1,338 @@ +#!/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="" + +# 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 " -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 "" +} + +# 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="$2" + shift 2 + ;; + *) + echo -e "${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 "$DOCKER_PERSIST_DIR/.bash_history" 2>/dev/null || true + +# Configure Docker run options based on Docker mode (rootless vs root) +if [ "$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 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" + "-w" "/app" + "-e" "TZ=Asia/Seoul" + "-e" "HOST_PROJECT_DIR=$SCRIPT_DIR" + ) +fi + +# Add options based on execution mode +if [ "$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 + +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: $DOCKER_PERSIST_DIR (bash history, gitconfig)" + echo -e " Docker Credentials: ~/.docker/config.json (read-only)" +fi +echo "" + +# Run Docker container +if docker run "${DOCKER_RUN_OPTS[@]}" "$LOCAL_IMAGE"; then + echo "" + if [ "$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/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/evolve/build_stage1_sample.py b/input/z3-bench/evolve/build_stage1_sample.py new file mode 100644 index 0000000000..ec8b77b3af --- /dev/null +++ b/input/z3-bench/evolve/build_stage1_sample.py @@ -0,0 +1,73 @@ +""" +Generate stage1_sample.json: 5 problems stratified by baseline elapsed_ms quantile. + +Fixed seed (42) for reproducibility. Sort by baseline_ms, split into 5 equal-size +buckets, pick one from each bucket. Result written to shared/stage1_sample.json. +""" +import json +import pathlib +import random + +ROOT = pathlib.Path(__file__).resolve().parent +PROBLEMS = ROOT.parent / "problems.jsonl" +OUT = ROOT / "shared" / "stage1_sample.json" + +NUM_BUCKETS = 5 +SEED = 42 + + +def main(): + rows = [] + with open(PROBLEMS) as f: + for line in f: + d = json.loads(line) + rows.append( + ( + d["problem_sha256"], + d["z3_status"]["elapsed_ms"], + d["z3_status"]["result"], + ) + ) + rows.sort(key=lambda r: r[1]) + + n = len(rows) + if n < NUM_BUCKETS: + raise SystemExit(f"need >= {NUM_BUCKETS} problems, got {n}") + + rng = random.Random(SEED) + picked = [] + for i in range(NUM_BUCKETS): + lo = i * n // NUM_BUCKETS + hi = (i + 1) * n // NUM_BUCKETS + bucket = rows[lo:hi] + if bucket: + picked.append(rng.choice(bucket)) + + OUT.parent.mkdir(parents=True, exist_ok=True) + OUT.write_text( + json.dumps( + { + "seed": SEED, + "num_buckets": NUM_BUCKETS, + "source": str(PROBLEMS.relative_to(ROOT.parent.parent)), + "sha256": [r[0] for r in picked], + "summary": [ + { + "sha": r[0][:12], + "baseline_ms": r[1], + "baseline_result": r[2], + } + for r in picked + ], + }, + indent=2, + ) + + "\n" + ) + print(f"wrote {OUT.relative_to(ROOT.parent.parent)} ({len(picked)} problems)") + for s in picked: + print(f" {s[0][:12]} {s[1]:>8} ms {s[2]}") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml new file mode 100644 index 0000000000..e0a7bf09c0 --- /dev/null +++ b/input/z3-bench/evolve/config.yaml @@ -0,0 +1,72 @@ +# Shared OpenEvolve config for all Z3 tuning phases. +# Per-phase iteration count is set in run_phase.sh. + +max_iterations: 100 +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: + primary_model: "gemini-2.5-flash" + primary_model_weight: 0.8 + secondary_model: "gemini-2.5-pro" + secondary_model_weight: 0.2 + api_base: "https://generativelanguage.googleapis.com/v1beta/openai/" + temperature: 0.7 + max_tokens: 16000 + timeout: 180 + +prompt: + system_message: | + You tune Z3 SMT solver parameters for a MaxSMT/optimization workload (50 SMT2 instances + with ~13k Int / 19k Bool / 40 Real vars, ~2k soft + 105k hard constraints, opt.* engaged). + + The initial_program.py exposes an EVOLVE-BLOCK dict (per phase: opt.*+sls.*, sat.*, smt.*, + or unified). Your job is to mutate this dict to MINIMIZE wall-clock time across the + benchmark while PRESERVING correctness (Sat/Unsat must match baseline). + + 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: 1800 # 30 min cap per variant (stage2 fallback) + cascade_thresholds: [0.3] # need stage1 score >= 0.3 to enter stage2 + parallel_evaluations: 1 # z3 memory-heavy (up to 4 GB observed); raise carefully diff --git a/input/z3-bench/evolve/extract_best.py b/input/z3-bench/evolve/extract_best.py new file mode 100644 index 0000000000..bb0a47c160 --- /dev/null +++ b/input/z3-bench/evolve/extract_best.py @@ -0,0 +1,65 @@ +""" +After phase N completes, extract get_phase_overrides() from its best_program.py +and write shared/phaseN_best.json. Phase N+1 auto-loads it. + +Usage: python extract_best.py +""" +import importlib.util +import json +import pathlib +import sys + +ROOT = pathlib.Path(__file__).resolve().parent +SHARED = ROOT / "shared" + +PHASE_DIRS = { + 1: "phase1_opt_sls", + 2: "phase2_sat", + 3: "phase3_smt", +} + + +def main(): + if len(sys.argv) != 2: + print("usage: python extract_best.py <1|2|3>", file=sys.stderr) + sys.exit(2) + + n = int(sys.argv[1]) + if n not in PHASE_DIRS: + print(f"phase must be 1, 2, or 3 (got {n})", file=sys.stderr) + sys.exit(2) + + phase_dir = ROOT / PHASE_DIRS[n] + 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) + sys.exit(1) + + # Make shared/ importable for the module under test + 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__}, expected dict", file=sys.stderr) + sys.exit(1) + + out = SHARED / f"phase{n}_best.json" + out.write_text(json.dumps(overrides, indent=2, sort_keys=True) + "\n") + print(f"wrote {out.relative_to(ROOT)} ({len(overrides)} keys)") + + +if __name__ == "__main__": + main() 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..82f58d6e4a --- /dev/null +++ b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py @@ -0,0 +1,71 @@ +""" +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": False, + "opt.lns.threshold": 4, + "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": 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/phase2_sat/initial_program.py b/input/z3-bench/evolve/phase2_sat/initial_program.py new file mode 100644 index 0000000000..7b3ce87a72 --- /dev/null +++ b/input/z3-bench/evolve/phase2_sat/initial_program.py @@ -0,0 +1,178 @@ +""" +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": True, + + # Preprocessing / simplification + "sat.scc": True, + "sat.scc.tr": True, + "sat.elim_vars": True, + "sat.elim_vars_bdd": True, + "sat.elim_vars_bdd_delay": 3, + "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": False, + "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": 30000, + + # 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/phase3_smt/initial_program.py b/input/z3-bench/evolve/phase3_smt/initial_program.py new file mode 100644 index 0000000000..1a2b1ad3aa --- /dev/null +++ b/input/z3-bench/evolve/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": 1.1, + "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": 4294967295, + "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": True, + "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/phase4_unified/initial_program.py b/input/z3-bench/evolve/phase4_unified/initial_program.py new file mode 100644 index 0000000000..988488b0ba --- /dev/null +++ b/input/z3-bench/evolve/phase4_unified/initial_program.py @@ -0,0 +1,36 @@ +""" +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 +# Seeded with baseline only. Run prepare_phase4.py after phases 1-3 finish +# to replace this dict with the union of their winners. +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/input/z3-bench/evolve/prepare_phase4.py b/input/z3-bench/evolve/prepare_phase4.py new file mode 100644 index 0000000000..6583121468 --- /dev/null +++ b/input/z3-bench/evolve/prepare_phase4.py @@ -0,0 +1,62 @@ +""" +Materialize phase4_unified/initial_program.py with a literal dict containing +the union of phase 1/2/3 winners (loaded from shared/phase{1,2,3}_best.json). + +Run AFTER phases 1, 2, 3 have completed (and extract_best.py ran for each). + +This rewrites only the EVOLVE-BLOCK section of phase4_unified/initial_program.py. +""" +import json +import pathlib +import pprint +import re +import sys + +ROOT = pathlib.Path(__file__).resolve().parent +SHARED = ROOT / "shared" +PHASE4_FILE = ROOT / "phase4_unified" / "initial_program.py" + +_EVOLVE_BLOCK_RE = re.compile( + r"(# EVOLVE-BLOCK-START\n).*?(# EVOLVE-BLOCK-END)", + re.DOTALL, +) + + +def _load(name): + f = SHARED / name + if not f.exists(): + print(f"missing: {f}", file=sys.stderr) + sys.exit(1) + return json.loads(f.read_text()) + + +def main(): + p1 = _load("phase1_best.json") + p2 = _load("phase2_best.json") + p3 = _load("phase3_best.json") + merged = {} + merged.update(p1) + merged.update(p2) + merged.update(p3) + print(f"merged keys: p1={len(p1)} p2={len(p2)} p3={len(p3)} union={len(merged)}") + + src = PHASE4_FILE.read_text() + dict_repr = pprint.pformat(merged, width=100, sort_dicts=True) + new_block_body = ( + "# Auto-generated by prepare_phase4.py from union of phase 1/2/3 best.\n" + f"UNIFIED_OVERRIDES = {dict_repr}\n" + ) + new_src, n = _EVOLVE_BLOCK_RE.subn( + lambda m: m.group(1) + new_block_body + m.group(2), + src, + count=1, + ) + if n != 1: + print(f"EVOLVE-BLOCK markers not found in {PHASE4_FILE}", file=sys.stderr) + sys.exit(1) + PHASE4_FILE.write_text(new_src) + print(f"wrote {PHASE4_FILE.relative_to(ROOT)} ({len(merged)} keys in EVOLVE-BLOCK)") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/run_phase.sh b/input/z3-bench/evolve/run_phase.sh new file mode 100755 index 0000000000..40e739a261 --- /dev/null +++ b/input/z3-bench/evolve/run_phase.sh @@ -0,0 +1,72 @@ +#!/bin/bash +# Run one phase of Z3 parameter evolution. +# Usage: ./run_phase.sh {1|2|3|4} [extra openevolve-run.py flags...] +# +# Phase iterations are set per-phase below. Override via --iterations . +# OpenEvolve outputs land in /openevolve_output/. + +set -euo pipefail + +if [ $# -lt 1 ]; then + echo "usage: $0 {1|2|3|4} [extra flags]" >&2 + exit 2 +fi + +PHASE="$1" +shift + +case "$PHASE" in + 1) DIR="phase1_opt_sls"; ITERS=80 ;; + 2) DIR="phase2_sat"; ITERS=150 ;; + 3) DIR="phase3_smt"; ITERS=120 ;; + 4) DIR="phase4_unified"; ITERS=60 ;; + *) echo "phase must be 1, 2, 3, or 4 (got $PHASE)" >&2; exit 2 ;; +esac + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$ROOT/../../.." && pwd)" +RUNNER="$REPO_ROOT/openevolve-run.py" + +if [ ! -f "$RUNNER" ]; then + echo "openevolve-run.py not found at $RUNNER" >&2 + exit 1 +fi + +if [ ! -f "$ROOT/shared/stage1_sample.json" ]; then + echo "stage1_sample.json missing — running build_stage1_sample.py first..." + python "$ROOT/build_stage1_sample.py" +fi + +if ! command -v z3 >/dev/null 2>&1; then + echo "warning: z3 binary not on PATH. install: apt-get install -y z3 or pip install z3-solver" >&2 +fi + +if [ "$PHASE" -eq 4 ]; then + if [ ! -f "$ROOT/shared/phase1_best.json" ] \ + || [ ! -f "$ROOT/shared/phase2_best.json" ] \ + || [ ! -f "$ROOT/shared/phase3_best.json" ]; then + echo "phase 4 requires phase 1/2/3 best.json files in shared/." >&2 + echo "run phases 1-3 first." >&2 + exit 1 + fi + echo "[run_phase] materializing phase4 EVOLVE-BLOCK..." + python "$ROOT/prepare_phase4.py" +fi + +cd "$ROOT/$DIR" +echo "[run_phase] phase=$PHASE dir=$DIR iters=$ITERS cwd=$(pwd)" + +python "$RUNNER" \ + initial_program.py \ + "$ROOT/shared/evaluator.py" \ + --config "$ROOT/config.yaml" \ + --iterations "$ITERS" \ + "$@" + +echo "[run_phase] phase $PHASE finished." + +if [ "$PHASE" -lt 4 ]; then + echo "[run_phase] extracting best overrides..." + python "$ROOT/extract_best.py" "$PHASE" + echo "[run_phase] next: $0 $((PHASE + 1))" +fi diff --git a/input/z3-bench/evolve/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py new file mode 100644 index 0000000000..0fae6a93cf --- /dev/null +++ b/input/z3-bench/evolve/shared/baseline_params.py @@ -0,0 +1,33 @@ +""" +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, +} + +LOCKED = { + "sat.random_seed": 0, + "smt.random_seed": 0, + "sls.random_seed": 0, + "parallel.enable": False, +} diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py new file mode 100644 index 0000000000..c66d191788 --- /dev/null +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -0,0 +1,206 @@ +""" +OpenEvolve evaluator for Z3 parameter tuning. + +Cascade: + stage1: 5 stratified problems (stage1_sample.json), per-problem 15s + stage2: full 50 problems, per-problem 120s + +Score: geomean(speedup) * solved_rate^2. + +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_STAGE1_TIMEOUT int sec — per-problem timeout in stage1 (default 15) + OPENEVOLVE_STAGE2_TIMEOUT int sec — per-problem timeout in stage2 (default 120) + OPENEVOLVE_Z3_BIN str — z3 binary path (default "z3") +""" +import importlib.util +import json +import os +import pathlib +import sys +import traceback + +_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 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" + +_Z3_BIN = os.environ.get("OPENEVOLVE_Z3_BIN", "z3") + + +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(): + rows = [] + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + rows.append( + { + "sha": d["problem_sha256"], + "smt2": d["smt2_filename"], + "baseline_ms": d["z3_status"]["elapsed_ms"], + "baseline_result": d["z3_status"]["result"], + } + ) + 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 _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, per_problem_timeout_s, 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"])] + + results = [] + 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}, + ) + r = run_z3(smt2_path, params, per_problem_timeout_s, z3_bin=_Z3_BIN) + if "invalid_param" in 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().", + }, + ) + results.append( + { + **p, + "result": r["result"], + "elapsed_ms": r["elapsed_ms"], + "timeout": bool(r.get("timeout")), + } + ) + + metrics = score(results) + metrics["stage"] = stage_name + + 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"], + } + 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"score={metrics['combined_score']:.3f}" + ), + "per_problem": sample[:20], + } + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + +def evaluate_stage1(program_path): + timeout = int(os.environ.get("OPENEVOLVE_STAGE1_TIMEOUT", "15")) + problems = _filter_stage1(_load_problems()) + return _evaluate(program_path, problems, timeout, "stage1") + + +def evaluate_stage2(program_path): + timeout = int(os.environ.get("OPENEVOLVE_STAGE2_TIMEOUT", "120")) + problems = _load_problems() + return _evaluate(program_path, problems, timeout, "stage2") + + +def evaluate(program_path): + return evaluate_stage2(program_path) diff --git a/input/z3-bench/evolve/shared/score.py b/input/z3-bench/evolve/shared/score.py new file mode 100644 index 0000000000..3f73f5f4dd --- /dev/null +++ b/input/z3-bench/evolve/shared/score.py @@ -0,0 +1,49 @@ +""" +Scoring: geomean(speedup) * solved_rate^2. +- 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 +""" +import math + + +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, + } + + 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 + combined = geomean * (solved_rate**2) + + return { + "combined_score": float(combined), + "geomean_speedup": float(geomean), + "solved_rate": float(solved_rate), + "regressions": int(regressions), + "solved": int(solved), + "total": int(n), + } diff --git a/input/z3-bench/evolve/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json new file mode 100644 index 0000000000..8191306e7a --- /dev/null +++ b/input/z3-bench/evolve/shared/stage1_sample.json @@ -0,0 +1,39 @@ +{ + "seed": 42, + "num_buckets": 5, + "source": "z3-bench/problems.jsonl", + "sha256": [ + "ac90ca97ff993ebed2e7b1dfdd4e670b0bac1c67d04ac3d592d58456dc4425d9", + "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", + "29efe6d38d7b8fcf41ed69b73f5a0bb370f782ab61ffe564fbe41512ea75ce5f", + "86468fd861ffa391b567ca57dc57c9b95b8577942d3fd2587e244073bdee43ef", + "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44" + ], + "summary": [ + { + "sha": "ac90ca97ff99", + "baseline_ms": 239, + "baseline_result": "Unsat" + }, + { + "sha": "133383a624ef", + "baseline_ms": 480, + "baseline_result": "Unsat" + }, + { + "sha": "29efe6d38d7b", + "baseline_ms": 12712, + "baseline_result": "Unsat" + }, + { + "sha": "86468fd861ff", + "baseline_ms": 15671, + "baseline_result": "Sat" + }, + { + "sha": "3854194b901b", + "baseline_ms": 66100, + "baseline_result": "Sat" + } + ] +} diff --git a/input/z3-bench/evolve/shared/z3_runner.py b/input/z3-bench/evolve/shared/z3_runner.py new file mode 100644 index 0000000000..4cfccebbfe --- /dev/null +++ b/input/z3-bench/evolve/shared/z3_runner.py @@ -0,0 +1,91 @@ +""" +Run Z3 CLI on a single SMT2 file with parameter dict. +Subprocess isolation: each run a fresh process with -T: wall-clock cap. +""" +import re +import shutil +import subprocess +import time + +_RESULT_RE = re.compile(r"^(sat|unsat|unknown)\b", re.MULTILINE) +_INVALID_PARAM_RES = [ + re.compile(r"unknown\s+parameter\s+'?([\w.\-]+)'?", re.IGNORECASE), + re.compile(r"invalid\s+parameter\s+'?([\w.\-]+)'?", re.IGNORECASE), + re.compile(r"unknown\s+option\s+'?([\w.\-]+)'?", re.IGNORECASE), + re.compile(r"error\s+setting\s+'?([\w.\-]+)'?", re.IGNORECASE), +] + + +def _format_value(v): + if isinstance(v, bool): + return "true" if v else "false" + return str(v) + + +def detect_invalid_param(stderr): + for rx in _INVALID_PARAM_RES: + m = rx.search(stderr) + if m: + return m.group(1) + if any(tok in stderr.lower() for tok in ("unknown parameter", "invalid parameter", "unknown option")): + return "" + return None + + +def run_z3(smt2_path, params, timeout_s, z3_bin="z3"): + """ + Returns dict: + success: {"result": "Sat"|"Unsat"|"Unknown", "elapsed_ms": int} + timeout: {"result": "Unknown", "elapsed_ms": int, "timeout": True} + invalid: {"invalid_param": str, "stderr": str, "result": "Unknown", "elapsed_ms": int} + crash: {"result": "Unknown", "elapsed_ms": int, "error": str, "stderr": str} + """ + if shutil.which(z3_bin) is None: + return {"result": "Unknown", "elapsed_ms": 0, "error": f"z3 binary not found: {z3_bin}"} + + args = [z3_bin, f"-T:{int(timeout_s)}", "-smt2"] + for k, v in params.items(): + args.append(f"{k}={_format_value(v)}") + args.append(str(smt2_path)) + + t0 = time.monotonic() + try: + proc = subprocess.run( + args, + capture_output=True, + text=True, + timeout=timeout_s + 10, + ) + except subprocess.TimeoutExpired: + return { + "result": "Unknown", + "elapsed_ms": int(timeout_s * 1000), + "timeout": True, + } + + elapsed_ms = int((time.monotonic() - t0) * 1000) + stdout = proc.stdout or "" + stderr = proc.stderr or "" + + bad = detect_invalid_param(stderr) + if bad: + return { + "invalid_param": bad, + "stderr": stderr[-2000:], + "result": "Unknown", + "elapsed_ms": elapsed_ms, + } + + m = _RESULT_RE.search(stdout) + if not m: + return { + "result": "Unknown", + "elapsed_ms": elapsed_ms, + "error": f"no result token (rc={proc.returncode})", + "stderr": stderr[-1000:], + } + + return { + "result": {"sat": "Sat", "unsat": "Unsat", "unknown": "Unknown"}[m.group(1)], + "elapsed_ms": elapsed_ms, + } diff --git a/input/z3-bench/problems.csv 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"3600.0", "tech": "sf4lpp", "unsat_debug": "False", "use_reboot": "False"}, "features": {"num_bool": 3222, "num_hard_constraints": 24936, "num_int": 2520, "num_maximize_objectives": 0, "num_minimize_objectives": 0, "num_real": 10, "num_soft_constraints": 287, "num_variables": 5752}, "meta_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078__543b29ed8f75ba2d__seed0.meta.jsonl", "path": "primary", "problem_sha256": "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", "seed": 0, "smt2_filename": "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078.smt2", "smt2_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078.smt2", "solver": "z3", "z3_applied_params": {"opt.enable_core_rotate": true, "opt.enable_sat": true, "opt.enable_sls": true, "opt.maxres.hill_climb": true, "opt.maxsat_engine": "wmax", 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"unsat_debug": "False", "use_reboot": "False"}, "features": {"num_bool": 2116, "num_hard_constraints": 14932, "num_int": 1673, "num_maximize_objectives": 0, "num_minimize_objectives": 0, "num_real": 8, "num_soft_constraints": 215, "num_variables": 3797}, "meta_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3__543b29ed8f75ba2d__seed0.meta.jsonl", "problem_sha256": "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", "seed": 0, "smt2_filename": "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3.smt2", "smt2_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3.smt2", "z3_applied_params": {"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}, "z3_statistics": {"max memory": 25.25, "memory": 22.82, "num allocs": 49404449, "rlimit count": 828317, "solve-eqs-elim-vars": 3365, "solve-eqs-steps": 7223, "time": 0.221}, "z3_status": {"elapsed_ms": 221, "result": "Unsat"}, "z3_version": "4.13.3.0"} +{"applied_params_hash": "543b29ed8f75ba2d0c54a5409cce4ee23f2d54d221a09c0cca667e1b31ffcec6", "cli_params": {"effort": "low", "m2_offset": "zero", "num_of_heights": "1", "optimize_m1": "False", "optimize_m2": "False", "process": "sf4lpp", "seed": "0", "solver": "z3", "solver_iter_timeout": "3600.0", "tech": "sf4lpp", "unsat_debug": "False", "use_reboot": "False"}, "features": {"num_bool": 3222, "num_hard_constraints": 24936, "num_int": 2520, "num_maximize_objectives": 0, "num_minimize_objectives": 0, "num_real": 10, "num_soft_constraints": 287, "num_variables": 5752}, "meta_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b__543b29ed8f75ba2d__seed0.meta.jsonl", "path": "primary", "problem_sha256": "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", "seed": 0, "smt2_filename": "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b.smt2", "smt2_path": "/Users/heedeok.son/workspace/openevolve/input/z3-bench/raw-data/fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b.smt2", "solver": "z3", "z3_applied_params": {"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}, "z3_statistics": {"added eqs": 5141, "arith eq adapter": 357, "arith-bound-propagations-lp": 483, "arith-conflicts": 5, "arith-diseq": 592, "arith-fixed-eqs": 309, "arith-lower": 2136, "arith-make-feasible": 416, "arith-max-columns": 309, "arith-max-rows": 191, "arith-offset-eqs": 291, "arith-upper": 2122, "binary propagations": 53999, "conflicts": 103, "decisions": 964, "del clause": 482, "final checks": 6, "max memory": 88.65, "memory": 86.87, "minimized lits": 242, "mk bool var": 7079, "mk clause": 6437, "mk clause binary": 282306, "num allocs": 863687625, "num checks": 7, "pb conflicts": 30, "pb predicates": 260, "pb propagations": 45, "pb resolves": 28, "propagations": 76908, "rlimit count": 1730326, "solve-eqs-elim-vars": 4574, "solve-eqs-steps": 13111, "time": 0.258}, "z3_status": {"elapsed_ms": 258, "result": "Sat"}, "z3_version": "4.13.3.0"} 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() From 8b15494c21454c8d21c09d6dd881a1a48cfb4288 Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 18 May 2026 10:35:44 +0900 Subject: [PATCH 02/42] [feat] add claude code sdk backend --- configs/claude_code_example.yaml | 54 +++++ input/z3-bench/evolve/README.md | 398 +++++++++++++++++++++++++++++++ openevolve/config.py | 10 + openevolve/llm/__init__.py | 10 +- openevolve/llm/claude_code.py | 161 +++++++++++++ openevolve/llm/ensemble.py | 18 +- pyproject.toml | 3 + 7 files changed, 649 insertions(+), 5 deletions(-) create mode 100644 configs/claude_code_example.yaml create mode 100644 input/z3-bench/evolve/README.md create mode 100644 openevolve/llm/claude_code.py diff --git a/configs/claude_code_example.yaml b/configs/claude_code_example.yaml new file mode 100644 index 0000000000..e9c2247bc6 --- /dev/null +++ b/configs/claude_code_example.yaml @@ -0,0 +1,54 @@ +# 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: 180 + retries: 2 + retry_delay: 10 + # Optional Claude Code-specific knobs: + reasoning_effort: "medium" # one of: low, medium, high, xhigh, max + max_thinking_tokens: null # cap extended thinking budget + # 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: 120 + + evaluator_models: + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 1.0 + timeout: 120 + +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/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md new file mode 100644 index 0000000000..a2c2ac28af --- /dev/null +++ b/input/z3-bench/evolve/README.md @@ -0,0 +1,398 @@ +# Z3 Parameter Tuning via OpenEvolve + +OpenEvolve를 사용해 Z3 SMT 솔버의 파라미터를 진화적으로 탐색한다. 입력은 `input/z3-bench/` 데이터셋(50개 SMT2 인스턴스 + baseline 실행 로그). 목표는 baseline 대비 wall-clock 시간 단축, 단 정답(Sat/Unsat) 보존. + +--- + +## 0. OpenEvolve 빠른 소개 (처음 보는 사람용) + +### 0.1 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. + +### 0.2 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. + +### 0.3 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. + +### 0.4 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 +``` + +### 0.5 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. + +### 0.6 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 | + +### 0.7 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) + +--- + +## 1. 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 + +## 2. 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 `extract_best.py N` 자동 호출 → `shared/phase{N}_best.json` 작성 → P{N+1}이 import. + +P4 시작 시 `prepare_phase4.py` 자동 실행 → `phase4_unified/initial_program.py`의 EVOLVE-BLOCK을 union dict literal로 머터리얼라이즈 (LLM이 diff 편집 가능하도록). + +## 3. 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` + +## 4. 디렉토리 구조 + +``` +input/z3-bench/ +├── README.md # 데이터셋 스키마 설명 +├── problems.jsonl # baseline 실행 결과 50행 +├── problems.csv # 평탄화 버전 +├── raw-data/ # 원본 SMT2 + meta jsonl +└── evolve/ + ├── README.md # 이 파일 + ├── config.yaml # 공유 OpenEvolve config + ├── run_phase.sh # 1/2/3/4 phase 실행 진입점 + ├── build_stage1_sample.py # stage1 sample 생성 스크립트 + ├── extract_best.py # phase N 종료 후 best 추출 + ├── prepare_phase4.py # phase4 EVOLVE-BLOCK 머터리얼라이즈 + ├── shared/ + │ ├── baseline_params.py # BASELINE 19키, LOCKED 4키 + │ ├── score.py # geomean × solved_rate^2 + │ ├── z3_runner.py # subprocess z3 CLI 호출 + │ ├── evaluator.py # cascade stage1/stage2 + │ ├── stage1_sample.json # 5문제 stratified sample (seed=42) + │ └── phase{1,2,3}_best.json # 각 phase 종료 후 생성됨 + ├── phase1_opt_sls/ + │ └── initial_program.py # EVOLVE-BLOCK: OPT_SLS_OVERRIDES (~34키) + ├── phase2_sat/ + │ └── initial_program.py # EVOLVE-BLOCK: SAT_OVERRIDES (~121키) + ├── phase3_smt/ + │ └── initial_program.py # EVOLVE-BLOCK: SMT_OVERRIDES (~97키) + └── phase4_unified/ + └── initial_program.py # EVOLVE-BLOCK: UNIFIED_OVERRIDES (union) +``` + +## 5. 평가 흐름 (cascade) + +``` +LLM 변이된 initial_program.py + ↓ +evaluator.py: + 1. get_params() 호출 → dict + 2. LOCKED 위반 체크 → 위반 시 0점 + locked_violated artifact + 3. stage1 (5문제, per-problem 15s timeout): + for each problem in stage1_sample: + run_z3(smt2, params, timeout=15s) + invalid_param 감지 시 즉시 0점 + 어떤 키인지 artifact + score → cascade_threshold 0.3 통과 시 stage2 진입 + 4. stage2 (50문제, per-problem 120s timeout): + 동일 방식, 전수 + 5. 최종 metrics + per_problem artifacts (상위 20개) 반환 +``` + +### Stage1 sample (stratified by baseline elapsed_ms, seed=42) + +``` +ac90ca97ff99 239 ms Unsat (fast) +133383a624ef 480 ms Unsat (fast) +29efe6d38d7b 12,712 ms Unsat (medium) +86468fd861ff 15,671 ms Sat (medium) +3854194b901b 66,100 ms Sat (slow) +``` + +5분위 버킷에서 하나씩 → Sat/Unsat + 빠름/느림 골고루. + +## 6. Initial program 표준 형태 + +각 phase의 `initial_program.py`는 동일 패턴: + +```python +import pathlib, sys +_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" +sys.path.insert(0, str(_SHARED)) + +from baseline_params import BASELINE + +# (phase 2-3-4는 이전 phase best.json 로드) +import json +_PHASE1 = json.loads((_SHARED / "phase1_best.json").read_text()) \ + if (_SHARED / "phase1_best.json").exists() else {} + +# EVOLVE-BLOCK-START +PHASE_OVERRIDES = { + "opt.priority": "pareto", + "opt.maxsat_engine": "wmax", + # ... +} +# EVOLVE-BLOCK-END + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) # 누적 (phase 2+) + p.update(PHASE_OVERRIDES) # 현재 phase + return p + +def get_phase_overrides(): + """extract_best.py가 사용 — 현재 phase의 dict만 반환.""" + return dict(PHASE_OVERRIDES) +``` + +evaluator는 phase를 모름 — `get_params()` 결과만 받음. `extract_best.py`는 `get_phase_overrides()`만 호출 → phase별 dict 분리 유지. + +## 7. 실행 절차 (Docker) + +### Host에서 + +```bash +export OPENAI_API_KEY="..." # config.yaml의 api_base에 맞는 키 + # (현재 gemini-2.5-flash → Google AI Studio key) +./docker-run.sh dev -s z3evo # interactive shell +``` + +`OPENAI_API_KEY`라는 이름은 OpenEvolve가 OpenAI 호환 SDK를 쓰기 때문. 실제 라우팅은 `config.yaml`의 `api_base`가 결정. + +### Container 안 + +```bash +cd $SCRIPT_DIR # rootless: 호스트 경로 그대로 / root: /app + +# 1회 셋업 +pip install -e ".[dev]" +apt-get install -y z3 # 또는: pip install z3-solver (CLI 동반) +export OPENAI_API_KEY="..." # 셸 안에서도 export 필요 + +# (이미 생성됨, 재생성 원할 때만) +python input/z3-bench/evolve/build_stage1_sample.py + +# 순차 실행 — 각 phase 종료 시 extract_best.py 자동 호출 +./input/z3-bench/evolve/run_phase.sh 1 +./input/z3-bench/evolve/run_phase.sh 2 +./input/z3-bench/evolve/run_phase.sh 3 +./input/z3-bench/evolve/run_phase.sh 4 +``` + +### 체크포인트 재개 + +OpenEvolve가 `phase{N}_*/openevolve_output/checkpoints/checkpoint_K/`에 자동 저장. 재개: + +```bash +cd input/z3-bench/evolve/phase2_sat/ +python /app/openevolve-run.py \ + initial_program.py \ + ../shared/evaluator.py \ + --config ../config.yaml \ + --checkpoint openevolve_output/checkpoints/checkpoint_50 \ + --iterations 100 +``` + +### Detached 장시간 실행 + +```bash +./docker-run.sh dev -s z3evo -d +docker exec -it axion-cell-container-dev-$USER-z3evo bash +nohup ./input/z3-bench/evolve/run_phase.sh 1 \ + &> /app/logs/phase1.log & +``` + +## 8. 환경 변수 + +| 변수 | 기본 | 용도 | +|---|---|---| +| `OPENAI_API_KEY` | — | LLM API 키 (api_base에 맞는 것) | +| `OPENEVOLVE_MAX_PROBLEMS` | 50 | stage2 문제수 상한 (테스트용 축소) | +| `OPENEVOLVE_STAGE1_TIMEOUT` | 15 | stage1 문제당 초 | +| `OPENEVOLVE_STAGE2_TIMEOUT` | 120 | stage2 문제당 초 | +| `OPENEVOLVE_Z3_BIN` | `z3` | z3 바이너리 경로 | + +## 9. 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 | + +## 10. 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) + +## 11. 도커 안에서 추가 검증 필요 + +- `z3 -pmd | less` 출력으로 4.13.3.0의 실제 키 검증 (일부 키명/타입이 마이너 버전마다 다를 수 있음) +- baseline 변이로 stage1 1회 평가 직접 호출 → z3 binary 동작/타임아웃 검증 +- LLM API 호출 sanity check (`config.yaml`의 api_base + 키 매칭) + +## 12. 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↑ + +## 13. 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/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/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..7050e68214 --- /dev/null +++ b/openevolve/llm/claude_code.py @@ -0,0 +1,161 @@ +""" +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: + if attempt < retries: + logger.warning( + f"[claude_code] Timeout {attempt + 1}/{retries + 1}, retrying." + ) + await asyncio.sleep(retry_delay) + continue + logger.error(f"[claude_code] All {retries + 1} attempts timed out") + 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, + ) + + opts_kwargs: Dict[str, Any] = { + "max_turns": 1, + "allowed_tools": [], + "disallowed_tools": [], + "permission_mode": "bypassPermissions", + } + if system_message: + opts_kwargs["system_prompt"] = system_message + if self.model: + opts_kwargs["model"] = self.model + if self.max_thinking_tokens is not None: + opts_kwargs["max_thinking_tokens"] = self.max_thinking_tokens + 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 + opts_kwargs.update(self.extra_sdk_options) + + options = ClaudeAgentOptions(**opts_kwargs) + + text_chunks: List[str] = [] + result_text: Optional[str] = None + + 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 isinstance(msg, ResultMessage): + result_text = getattr(msg, "result", None) or result_text + + final = result_text if result_text else "".join(text_chunks) + logger.debug(f"[claude_code] response chars={len(final)}") + 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/pyproject.toml b/pyproject.toml index f95cd439c1..a36690f4a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,9 @@ dev = [ "requests>=2.28.0", "pre-commit>=4.5.1", ] +claude-code = [ + "claude-agent-sdk>=0.2.82", +] [tool.black] line-length = 100 From 626e54c24b331ab1646cf56286b8f007494fb6cc Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 18 May 2026 14:00:25 +0900 Subject: [PATCH 03/42] [feat] upgrade optimize loop --- docker-run.sh | 65 + input/z3-bench/evolve/README.md | 41 + input/z3-bench/evolve/build_stage1_sample.py | 55 +- input/z3-bench/evolve/config.yaml | 41 +- input/z3-bench/evolve/final_verify.py | 215 +++ .../evolve/phase1_opt_sls/initial_program.py | 5 +- .../evolve/phase2_sat/initial_program.py | 9 +- .../evolve/phase3_smt/initial_program.py | 6 +- input/z3-bench/evolve/probe_cli_keys.py | 105 ++ input/z3-bench/evolve/rebaseline_local.py | 142 ++ input/z3-bench/evolve/run_phase.sh | 6 + .../evolve/shared/_z3_solve_worker.py | 154 ++ .../z3-bench/evolve/shared/baseline_params.py | 127 ++ input/z3-bench/evolve/shared/evaluator.py | 198 ++- .../evolve/shared/local_baseline.json | 806 +++++++++++ input/z3-bench/evolve/shared/runtime.py | 49 + input/z3-bench/evolve/shared/score.py | 58 +- .../z3-bench/evolve/shared/stage1_sample.json | 39 +- input/z3-bench/evolve/shared/z3_runner.py | 120 +- .../z3-bench/evolve/shared/z3_valid_keys.json | 1266 +++++++++++++++++ input/z3-bench/evolve/validate_keys.py | 160 +++ .../z3-bench/evolve/verify_stage1_baseline.py | 123 ++ 22 files changed, 3646 insertions(+), 144 deletions(-) create mode 100644 input/z3-bench/evolve/final_verify.py create mode 100644 input/z3-bench/evolve/probe_cli_keys.py create mode 100644 input/z3-bench/evolve/rebaseline_local.py create mode 100644 input/z3-bench/evolve/shared/_z3_solve_worker.py create mode 100644 input/z3-bench/evolve/shared/local_baseline.json create mode 100644 input/z3-bench/evolve/shared/runtime.py create mode 100644 input/z3-bench/evolve/shared/z3_valid_keys.json create mode 100644 input/z3-bench/evolve/validate_keys.py create mode 100644 input/z3-bench/evolve/verify_stage1_baseline.py diff --git a/docker-run.sh b/docker-run.sh index 26fac275a3..6b428aafe8 100755 --- a/docker-run.sh +++ b/docker-run.sh @@ -221,6 +221,56 @@ 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 [ "$ROOTLESS_DOCKER" = true ]; then # Rootless Docker: Mount user's home directory as per the guide @@ -245,6 +295,10 @@ if [ "$ROOTLESS_DOCKER" = true ]; then "-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 @@ -272,10 +326,16 @@ else "-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 # Add options based on execution mode @@ -306,6 +366,11 @@ else 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' "${CLAUDE_ENV_OPTS[@]}" | sed 's/-e //g' | sed 's/=[^ ]*/=***/g')" fi echo "" diff --git a/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md index a2c2ac28af..ec96ccabf0 100644 --- a/input/z3-bench/evolve/README.md +++ b/input/z3-bench/evolve/README.md @@ -299,6 +299,47 @@ export OPENAI_API_KEY="..." # config.yaml의 api_base에 맞는 키 `OPENAI_API_KEY`라는 이름은 OpenEvolve가 OpenAI 호환 SDK를 쓰기 때문. 실제 라우팅은 `config.yaml`의 `api_base`가 결정. +### Claude Code 백엔드 사용 시 + +`config.yaml`에서 `provider: claude_code`로 모델을 정의하면 (`configs/claude_code_example.yaml` 참고) API 키 대신 Claude Code 구독 인증을 쓸 수 있다. Docker 안에서 쓰려면: + +1. **Host에서 (1회만)**: long-lived OAuth 토큰 생성 + ```bash + claude setup-token # 출력된 토큰 복사 + export CLAUDE_CODE_OAUTH_TOKEN="sk-..." + ``` + macOS는 OAuth credential을 Keychain에 저장하므로 `~/.claude/` 마운트만으로는 인증 안 됨. 토큰 방식이 필수. + +2. **docker-run.sh 실행**: 위 env var가 export 되어 있으면 자동 전달 + `~/.claude/` 마운트 (settings/sessions 공유). + ```bash + ./docker-run.sh dev -s z3evo + ``` + +3. **Container 안 (1회만)**: `claude` CLI 설치. axion 이미지에는 Node.js/npm 없음 → Anthropic 공식 standalone installer 사용 (Node 번들, 시스템 의존성 없음). + ```bash + curl -fsSL https://claude.ai/install.sh | bash + # 설치 위치: ~/.local/bin/claude + export PATH="$HOME/.local/bin:$PATH" # ~/.bashrc에 영구 추가 권장 + claude --version # sanity check + pip install -e ".[claude-code]" # claude-agent-sdk + ``` + SDK 탐색 순서: `~/.npm-global/bin/claude` → `/usr/local/bin/claude` → `~/.local/bin/claude` → `~/.claude/local/claude` → PATH. 위 경로 그대로 작동. + + **설치 영속화**: 컨테이너는 `--rm`이라 종료 시 사라지지만 docker-run.sh가 `~/.axion-docker-persist/claude-local/`을 `/root/.local`로 마운트 → 한 번 설치하면 다음 컨테이너에서도 그대로 사용 가능. + + **host 차이**: + - Linux host: docker-run.sh가 host `claude` 바이너리도 자동 마운트 (`/usr/local/bin/claude` ro) → installer 생략 가능. + - Mac host: cross-OS 불가 → 위 installer 필수. + + **HOME 분리**: `~/.claude/`가 host에서 마운트되므로 host config 공유됨. 충돌 우려 시 컨테이너 안에서 `CLAUDE_CONFIG_DIR` 등 별도 path 지정. + +4. **체크**: 인증 작동 여부 + ```bash + python -c "from openevolve.llm.claude_code import ClaudeCodeLLM; print('ok')" + ``` + +**주의**: Claude Pro/Max 구독은 5시간 윈도우 rate limit 있음. 큰 evolution run은 빠르게 막힘. 작은 iteration으로 검증 먼저. + ### Container 안 ```bash diff --git a/input/z3-bench/evolve/build_stage1_sample.py b/input/z3-bench/evolve/build_stage1_sample.py index ec8b77b3af..ba6e421b80 100644 --- a/input/z3-bench/evolve/build_stage1_sample.py +++ b/input/z3-bench/evolve/build_stage1_sample.py @@ -1,19 +1,30 @@ """ -Generate stage1_sample.json: 5 problems stratified by baseline elapsed_ms quantile. +Generate stage1_sample.json: 5 fastest Sat problems (fallback: fastest Unsat +to fill remainder). -Fixed seed (42) for reproducibility. Sort by baseline_ms, split into 5 equal-size -buckets, pick one from each bucket. Result written to shared/stage1_sample.json. +Rationale: stage1 has tight per-problem timeout (15s default). Including slow +baseline problems (>15s) guarantees timeout regardless of variant quality, +making stage1 score uninformative. Sat is preferred since LLM is more likely +to preserve Sat than Unsat under aggressive param changes (Unsat proofs depend +on completeness of preprocessing pipeline). + +No randomness — deterministic pick of fastest N by result class. """ import json import pathlib -import random ROOT = pathlib.Path(__file__).resolve().parent PROBLEMS = ROOT.parent / "problems.jsonl" OUT = ROOT / "shared" / "stage1_sample.json" -NUM_BUCKETS = 5 -SEED = 42 +NUM_PROBLEMS = 5 +# Stage1 wall-clock budget: 60-120s per variant. +# - good variant (baseline-like): 5 × ~12s = 60s lower bound +# - bad variant (all timeout): 5 × 24s = 120s upper bound +# Pick problems whose baseline elapsed_ms falls in [MIN_MS, MAX_MS]. +# Per-problem timeout defaults to 24s in evaluator.py. +MIN_MS = 4000 +MAX_MS = 15000 def main(): @@ -28,27 +39,29 @@ def main(): d["z3_status"]["result"], ) ) - rows.sort(key=lambda r: r[1]) + rows.sort(key=lambda r: r[1]) # ascending by elapsed_ms - n = len(rows) - if n < NUM_BUCKETS: - raise SystemExit(f"need >= {NUM_BUCKETS} problems, got {n}") + in_range = [r for r in rows if MIN_MS <= r[1] <= MAX_MS] + sat_band = [r for r in in_range if r[2] == "Sat"] + unsat_band = [r for r in in_range if r[2] == "Unsat"] - rng = random.Random(SEED) - picked = [] - for i in range(NUM_BUCKETS): - lo = i * n // NUM_BUCKETS - hi = (i + 1) * n // NUM_BUCKETS - bucket = rows[lo:hi] - if bucket: - picked.append(rng.choice(bucket)) + picked = sat_band[:NUM_PROBLEMS] + if len(picked) < NUM_PROBLEMS: + picked.extend(unsat_band[: NUM_PROBLEMS - len(picked)]) + if len(picked) < NUM_PROBLEMS: + # fallback: widen below MIN_MS toward 0, prefer Sat + below = [r for r in rows if r[1] < MIN_MS] + sat_below = sorted((r for r in below if r[2] == "Sat"), key=lambda r: -r[1]) + picked.extend(sat_below[: NUM_PROBLEMS - len(picked)]) + if len(picked) < NUM_PROBLEMS: + remaining = [r for r in rows if r not in picked] + picked.extend(remaining[: NUM_PROBLEMS - len(picked)]) OUT.parent.mkdir(parents=True, exist_ok=True) OUT.write_text( json.dumps( { - "seed": SEED, - "num_buckets": NUM_BUCKETS, + "selection": f"{NUM_PROBLEMS} Sat-preferred with baseline_ms in [{MIN_MS}, {MAX_MS}] (Unsat / sub-MIN_MS fallbacks)", "source": str(PROBLEMS.relative_to(ROOT.parent.parent)), "sha256": [r[0] for r in picked], "summary": [ @@ -66,7 +79,7 @@ def main(): ) print(f"wrote {OUT.relative_to(ROOT.parent.parent)} ({len(picked)} problems)") for s in picked: - print(f" {s[0][:12]} {s[1]:>8} ms {s[2]}") + print(f" {s[0][:12]} {s[1]:>6} ms {s[2]}") if __name__ == "__main__": diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index e0a7bf09c0..ac45024557 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -1,7 +1,16 @@ # Shared OpenEvolve config for all Z3 tuning phases. # Per-phase iteration count is set in run_phase.sh. +# +# === 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: 100 +max_iterations: 20 checkpoint_interval: 10 log_level: "INFO" random_seed: 42 @@ -14,14 +23,18 @@ convergence_threshold: 0.001 early_stopping_metric: "combined_score" llm: - primary_model: "gemini-2.5-flash" - primary_model_weight: 0.8 - secondary_model: "gemini-2.5-pro" - secondary_model_weight: 0.2 - api_base: "https://generativelanguage.googleapis.com/v1beta/openai/" - temperature: 0.7 - max_tokens: 16000 - timeout: 180 + models: + - name: "claude-sonnet-4-6" + provider: "claude_code" + weight: 0.8 + timeout: 180 + retries: 2 + retry_delay: 10 + reasoning_effort: "medium" + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 120 prompt: system_message: | @@ -67,6 +80,10 @@ database: similarity_threshold: 0.95 evaluator: - timeout: 1800 # 30 min cap per variant (stage2 fallback) - cascade_thresholds: [0.3] # need stage1 score >= 0.3 to enter stage2 - parallel_evaluations: 1 # z3 memory-heavy (up to 4 GB observed); raise carefully + timeout: 300 # 5 min cap per variant (stage1 only: 5 problems × 24s) + cascade_evaluation: false # stage2 reserved for final_verify.py, not per-variant search + 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.py b/input/z3-bench/evolve/final_verify.py new file mode 100644 index 0000000000..4fe7920bef --- /dev/null +++ b/input/z3-bench/evolve/final_verify.py @@ -0,0 +1,215 @@ +""" +Final verification: on a final-test sample, measure LOCAL baseline elapsed_ms +and then run the optimized program. Report per-problem speedup using the +fresh local baseline (not the raw-data baseline which was recorded on a +different machine). + +Usage: + python final_verify.py + +Sample selection (in priority order): + 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. + Hand-edit or generate this to pin a specific subset for verification. + 2. Fall back to ALL problems in problems.jsonl (50). + +Order of operations: + for each problem p in final sample: + run BASELINE on p → record base_ms_local + run params on p → record variant_ms + speedup = base_ms_local / variant_ms (when result matches) + +Baseline + variant are run back-to-back per problem so they share the same +warm cache / system noise. Concurrency = config parallel_solvers (one z3 +process pair per problem; each problem runs baseline then variant serially +inside its slot to keep timing apples-to-apples). +""" +import importlib.util +import json +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 / "shared")) + +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 # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" + +TIMEOUT_S = 120 + + +def _load_get_params(program_path): + spec = importlib.util.spec_from_file_location("program", program_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if not hasattr(module, "get_params"): + print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) + sys.exit(2) + return module.get_params() + + +def _load_problem_index(): + idx = {} + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + idx[d["problem_sha256"]] = { + "sha": d["problem_sha256"], + "smt2": d["smt2_filename"], + "raw_ms": d["z3_status"]["elapsed_ms"], + "raw_result": d["z3_status"]["result"], + } + return idx + + +def _resolve_sample(idx): + """Pick the SHA list for final verification.""" + if _FINAL_SAMPLE.exists(): + shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) + source = f"shared/final_sample.json ({len(shas)} SHAs)" + else: + shas = list(idx.keys()) + source = f"problems.jsonl (full {len(shas)})" + metas = [] + for sha in shas: + meta = idx.get(sha) + if meta is None: + print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) + sys.exit(2) + smt2 = _RAW_DIR / meta["smt2"] + if not smt2.exists(): + print(f"ERROR: missing {smt2}", file=sys.stderr) + sys.exit(2) + metas.append((meta, smt2)) + return metas, source + + +def main(): + if len(sys.argv) != 2: + print(__doc__, file=sys.stderr) + return 2 + + program_path = pathlib.Path(sys.argv[1]).resolve() + if not program_path.exists(): + print(f"ERROR: {program_path} not found", file=sys.stderr) + return 2 + + variant_params = _load_get_params(program_path) + violations = {k: variant_params.get(k) for k in LOCKED + if variant_params.get(k) != LOCKED[k]} + if violations: + print(f"ERROR: locked params violated: {violations}", file=sys.stderr) + return 2 + + idx = _load_problem_index() + metas, source = _resolve_sample(idx) + n_parallel = min(parallel_solvers(default=1), len(metas)) + + print(f"final verify: {program_path}") + print(f" sample : {source}") + print(f" params : {len(variant_params)} keys, " + f"{sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} differ from BASELINE") + print(f" parallel solvers : {n_parallel} (taskset core pin)") + print(f" per-problem timeout : {TIMEOUT_S}s × 2 (baseline + variant)") + print() + + def _measure(idx_meta): + i, meta, smt2 = idx_meta + core = (i % n_parallel) if n_parallel > 1 else None + # Baseline first, then variant — back-to-back so system noise affects + # both equally and speedup ratio cancels it out. + b = run_z3(smt2, BASELINE, TIMEOUT_S, cpu_core=core) + v = run_z3(smt2, variant_params, TIMEOUT_S, cpu_core=core) + return i, meta, b, v + + tasks = [(i, meta, smt2) for i, (meta, smt2) in enumerate(metas)] + t_start = time.monotonic() + completed = [] + if n_parallel == 1: + for t in tasks: + completed.append(_measure(t)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_measure, t) for t in tasks] + for fut in as_completed(futures): + completed.append(fut.result()) + completed.sort(key=lambda x: x[0]) + elapsed = time.monotonic() - t_start + + results = [] + for i, meta, b, v in completed: + base_ms_local = int(b.get("elapsed_ms", 0)) + base_result = b.get("result", "Unknown") + var_ms = int(v.get("elapsed_ms", 0)) + var_result = v.get("result", "Unknown") + var_invalid = v.get("invalid_param") + # Speedup uses LOCAL baseline (this run), not raw_ms. + if var_invalid: + speedup = 0.0 + flag = f" invalid={var_invalid}" + elif var_result != base_result: + speedup = 0.0 + flag = f" MISMATCH (base={base_result} variant={var_result})" + else: + speedup = base_ms_local / max(var_ms, 1) + flag = "" + print( + f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} " + f"base_local={base_result:<7}/{base_ms_local:>7}ms " + f"variant={var_result:<7}/{var_ms:>7}ms " + f"speedup={speedup:.2f}x{flag}", + flush=True, + ) + results.append({ + "sha": meta["sha"], + "smt2": meta["smt2"], + "baseline_ms": base_ms_local, + "baseline_result": base_result, + "result": var_result, + "elapsed_ms": var_ms, + "timeout": bool(v.get("timeout")), + "raw_baseline_ms": meta["raw_ms"], + }) + + metrics = score(results) + print() + print(f"== summary (speedup vs fresh LOCAL baseline) ==") + print(f" solved : {metrics['solved']}/{metrics['total']}") + print(f" regressions : {metrics['regressions']}") + print(f" geomean_speedup : {metrics['geomean_speedup']:.3f}") + print(f" solved_rate : {metrics['solved_rate']:.3f}") + print(f" combined_score : {metrics['combined_score']:.3f}") + print(f" wall-clock : {elapsed:.1f}s") + + out_path = program_path.parent / "final_verify.json" + out_path.write_text(json.dumps({ + "program": str(program_path), + "sample_source": source, + "metrics": metrics, + "per_problem": [ + { + "sha": r["sha"][:12], + "base_result": r["baseline_result"], + "got_result": r["result"], + "base_local_ms": r["baseline_ms"], + "variant_ms": r["elapsed_ms"], + "raw_baseline_ms": r["raw_baseline_ms"], + "timeout": r["timeout"], + } + for r in results + ], + }, indent=2) + "\n") + print(f" wrote {out_path}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/input/z3-bench/evolve/phase1_opt_sls/initial_program.py b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py index 82f58d6e4a..bafdea9fde 100644 --- a/input/z3-bench/evolve/phase1_opt_sls/initial_program.py +++ b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py @@ -26,8 +26,7 @@ "opt.enable_sat": True, "opt.enable_sls": True, "opt.enable_core_rotate": True, - "opt.enable_lns": False, - "opt.lns.threshold": 4, + # 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, @@ -50,7 +49,7 @@ "sls.walksat_ucb_init": False, "sls.walksat_ucb_noise": 0.0002, "sls.wp": 20, # walk probability (percent 0..100) - "sls.parallel": False, + # sls.parallel: API-only key (z3 CLI rejects); pinned to default False. "sls.random_offset": True, "sls.rescore": True, "sls.restart_base": 100, diff --git a/input/z3-bench/evolve/phase2_sat/initial_program.py b/input/z3-bench/evolve/phase2_sat/initial_program.py index 7b3ce87a72..22861aaeaf 100644 --- a/input/z3-bench/evolve/phase2_sat/initial_program.py +++ b/input/z3-bench/evolve/phase2_sat/initial_program.py @@ -56,14 +56,13 @@ "sat.gc.k": 7, "sat.gc.small_lbd": 3, "sat.minimize_lemmas": True, - "sat.dyn.sub_res": 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": True, - "sat.elim_vars_bdd_delay": 3, + # 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, @@ -84,7 +83,7 @@ "sat.bce_at": 2, "sat.bce_delay": 2, "sat.bca": False, - "sat.binspr": False, + # sat.binspr: not in z3 4.13.3.0 — removed. "sat.cce": False, "sat.blocked_clause_limit": 100000000, "sat.retain_blocked_clauses": True, @@ -92,7 +91,7 @@ "sat.force_cleanup": False, "sat.inprocess.max": 4294967295, "sat.simplify.delay": 0, - "sat.next_simplify1": 30000, + # sat.next_simplify1: not in z3 4.13.3.0 — removed. # Cardinality / PB "sat.cardinality.solver": True, diff --git a/input/z3-bench/evolve/phase3_smt/initial_program.py b/input/z3-bench/evolve/phase3_smt/initial_program.py index 1a2b1ad3aa..5635303efb 100644 --- a/input/z3-bench/evolve/phase3_smt/initial_program.py +++ b/input/z3-bench/evolve/phase3_smt/initial_program.py @@ -45,7 +45,7 @@ "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": 1.1, + # 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 @@ -69,7 +69,7 @@ "smt.pull_nested_quantifiers": False, "smt.refine_inj_axioms": True, "smt.solve_eqs": True, - "smt.solve_eqs_max_occs": 4294967295, + # 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, @@ -137,7 +137,7 @@ # BV (light usage in this workload) "smt.bv.delay": True, - "smt.bv.eager": 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, diff --git a/input/z3-bench/evolve/probe_cli_keys.py b/input/z3-bench/evolve/probe_cli_keys.py new file mode 100644 index 0000000000..5a4aa4a8e0 --- /dev/null +++ b/input/z3-bench/evolve/probe_cli_keys.py @@ -0,0 +1,105 @@ +""" +Probe which keys from shared/z3_valid_keys.json the Z3 CLI actually accepts +as positional `key=value` arguments. Some keys exist in `-pm:` doc but +are rejected by the CLI (structural / API-only options). + +For each key, run: + z3 -smt2 = +Capture stderr -> classify as CLI_OK / CLI_REJECT. + +Writes shared/z3_cli_skip_keys.json with the list to skip. + +Run inside the container (z3 binary required). This is slow (~N seconds for +N ~1000 keys, ~1s per probe). Use --limit to test a subset first. +""" +import argparse +import json +import pathlib +import re +import shutil +import subprocess +import sys +import tempfile + +ROOT = pathlib.Path(__file__).resolve().parent +SHARED = ROOT / "shared" + +# Trivial SMT2: instant decide. +_TRIVIAL_SMT2 = "(declare-const x Bool)\n(assert x)\n(check-sat)\n" + +_REJECT_RES = [ + re.compile(r"unknown\s+parameter", re.IGNORECASE), + re.compile(r"invalid\s+parameter", re.IGNORECASE), + re.compile(r"unknown\s+option", re.IGNORECASE), + re.compile(r"error\s+setting", re.IGNORECASE), + re.compile(r"is\s+a\s+structural\s+parameter", re.IGNORECASE), +] + + +def is_rejection(stderr): + for rx in _REJECT_RES: + if rx.search(stderr): + return True + return False + + +def probe(key, value, smt2_path, z3_bin): + """Return True if CLI rejects this key.""" + args = [z3_bin, "-smt2", "-T:5", f"{key}={value}", smt2_path] + try: + proc = subprocess.run(args, capture_output=True, text=True, timeout=10) + except subprocess.TimeoutExpired: + return False # timeout != reject; key was accepted but eval slow + return is_rejection(proc.stderr) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--limit", type=int, default=0, help="probe at most N keys (0 = all)") + ap.add_argument("--z3-bin", default="z3") + args = ap.parse_args() + + if shutil.which(args.z3_bin) is None: + print(f"ERROR: {args.z3_bin} not on PATH", file=sys.stderr) + sys.exit(1) + + valid_keys_path = SHARED / "z3_valid_keys.json" + if not valid_keys_path.exists(): + print(f"ERROR: {valid_keys_path} missing. Run validate_keys.py first.", file=sys.stderr) + sys.exit(1) + keys = sorted(json.loads(valid_keys_path.read_text())) + + # Use a safe sentinel value: integer 0 works for bool/uint/int/double; + # for symbol-typed options z3 ignores numeric mismatches but typically + # doesn't error at the parse level. Falls back to "false" if 0 rejected. + candidate_values = ["0", "false"] + + with tempfile.NamedTemporaryFile("w", suffix=".smt2", delete=False) as f: + f.write(_TRIVIAL_SMT2) + smt2 = f.name + + rejects = [] + total = len(keys) if args.limit == 0 else min(args.limit, len(keys)) + print(f"probing {total} keys with z3={args.z3_bin}") + for i, k in enumerate(keys[:total], 1): + rejected_all = True + for v in candidate_values: + if not probe(k, v, smt2, args.z3_bin): + rejected_all = False + break + if rejected_all: + rejects.append(k) + if i % 50 == 0: + print(f" {i}/{total} rejects so far: {len(rejects)}") + + out = SHARED / "z3_cli_skip_keys.json" + out.write_text(json.dumps(sorted(rejects), indent=2) + "\n") + print(f"\nwrote {out.relative_to(ROOT)} ({len(rejects)} CLI-rejected keys)") + if rejects: + print("examples:") + for r in rejects[:20]: + print(f" - {r}") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py new file mode 100644 index 0000000000..2676712ff7 --- /dev/null +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -0,0 +1,142 @@ +""" +Init-phase rebaseline: measure BASELINE elapsed_ms on the stage1 evolution +sample (shared/stage1_sample.json) and write shared/local_baseline.json. + +Wall-clock varies by hardware / z3 version. Raw-data baseline_ms was recorded +on a different machine, so comparing against it gives misleading speedup. +evaluator._load_problems overlays this local file onto raw data so that +speedup = local_baseline_ms / variant_elapsed_ms. + +Only the stage1 evolution sample is rebaselined here — fast (~30s with +parallel=5) and matches what the evolve loop actually sees. Final verification +uses a separate larger sample via final_verify.py, which rebaselines on the fly. + +Per-problem: 1 run, timeout = max(60s, raw_baseline_ms * 2 / 1000). +Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). +""" +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 / "shared")) + +from baseline_params import BASELINE # noqa: E402 +from z3_runner import run_z3 # noqa: E402 +from runtime import parallel_solvers # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" +_OUT = _HERE / "shared" / "local_baseline.json" + + +def _load_problem_index(): + idx = {} + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + idx[d["problem_sha256"]] = { + "sha": d["problem_sha256"], + "smt2": d["smt2_filename"], + "raw_ms": d["z3_status"]["elapsed_ms"], + "raw_result": d["z3_status"]["result"], + } + return idx + + +def _load_stage1_shas(): + if not _STAGE1_SAMPLE.exists(): + print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_stage1_sample.py first", + file=sys.stderr) + sys.exit(2) + return list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + + +def main(): + shas = _load_stage1_shas() + idx = _load_problem_index() + + 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_path = _RAW_DIR / meta["smt2"] + if not smt2_path.exists(): + print(f"ERROR: smt2 not found: {smt2_path}", file=sys.stderr) + return 2 + tasks.append((i, meta, smt2_path)) + + n_parallel = min(parallel_solvers(default=1), len(tasks)) + print(f"rebaselining stage1 evolution sample: {len(tasks)} problems " + f"(from stage1_sample.json)") + print(f"timeout per problem = max(60s, raw_ms * 2), parallel={n_parallel} " + f"(taskset core pin)") + print() + + def _solve(task): + i, meta, smt2_path = task + timeout_s = max(60, math.ceil(meta["raw_ms"] * 2 / 1000)) + core = (i % n_parallel) if n_parallel > 1 else None + res = run_z3(smt2_path, BASELINE, timeout_s, cpu_core=core) + return i, meta, res + + t_start = time.monotonic() + completed = [] + if n_parallel == 1: + for task in tasks: + completed.append(_solve(task)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_solve, t) for t in tasks] + for fut in as_completed(futures): + completed.append(fut.result()) + completed.sort(key=lambda x: x[0]) + + out = {} + mismatch = 0 + for i, meta, res in completed: + got_result = res.get("result", "Unknown") + got_ms = int(res.get("elapsed_ms", 0)) + ok = (got_result == meta["raw_result"]) + if not ok: + mismatch += 1 + + flag = "" if ok else " MISMATCH" + ratio = got_ms / max(meta["raw_ms"], 1) + core_tag = f" core={i % n_parallel}" if n_parallel > 1 else "" + print( + f" [{i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " + f"raw={meta['raw_result']:<7}/{meta['raw_ms']:>7}ms " + f"local={got_result:<7}/{got_ms:>7}ms ratio={ratio:.2f}x{flag}{core_tag}", + flush=True, + ) + + out[meta["sha"]] = { + "elapsed_ms": got_ms, + "result": got_result, + "matches_raw": ok, + "raw_elapsed_ms": meta["raw_ms"], + "stats": res.get("stats") or {}, + } + + elapsed = time.monotonic() - t_start + _OUT.write_text(json.dumps(out, indent=2) + "\n") + print() + print(f"wrote {_OUT.relative_to(_BENCH_DIR.parent)} " + f"({len(out)} entries, {mismatch} mismatches)") + print(f"total time: {elapsed:.1f}s") + if mismatch: + print(f"WARNING: {mismatch} problems had result mismatch — " + f"evaluator will keep raw_ms for those") + return 0 if mismatch == 0 else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/input/z3-bench/evolve/run_phase.sh b/input/z3-bench/evolve/run_phase.sh index 40e739a261..b3840e6b60 100755 --- a/input/z3-bench/evolve/run_phase.sh +++ b/input/z3-bench/evolve/run_phase.sh @@ -37,6 +37,12 @@ if [ ! -f "$ROOT/shared/stage1_sample.json" ]; then python "$ROOT/build_stage1_sample.py" fi +if [ ! -f "$ROOT/shared/local_baseline.json" ]; then + echo "local_baseline.json missing — running rebaseline_local.py first (~5 min, 20 problems)..." + python "$ROOT/rebaseline_local.py" || \ + echo "warning: rebaseline_local.py finished with mismatches; evaluator will fall back to raw_ms for those." +fi + if ! command -v z3 >/dev/null 2>&1; then echo "warning: z3 binary not on PATH. install: apt-get install -y z3 or pip install z3-solver" >&2 fi diff --git a/input/z3-bench/evolve/shared/_z3_solve_worker.py b/input/z3-bench/evolve/shared/_z3_solve_worker.py new file mode 100644 index 0000000000..c618f65fd9 --- /dev/null +++ b/input/z3-bench/evolve/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/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py index 0fae6a93cf..a9c42e628e 100644 --- a/input/z3-bench/evolve/shared/baseline_params.py +++ b/input/z3-bench/evolve/shared/baseline_params.py @@ -23,6 +23,9 @@ "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 = { @@ -30,4 +33,128 @@ "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_stage1_sample.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)) + core = i % n_parallel + 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:>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/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index c66d191788..a8caecd475 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -5,16 +5,29 @@ stage1: 5 stratified problems (stage1_sample.json), per-problem 15s stage2: full 50 problems, per-problem 120s -Score: geomean(speedup) * solved_rate^2. +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_STAGE1_TIMEOUT int sec — per-problem timeout in stage1 (default 15) - OPENEVOLVE_STAGE2_TIMEOUT int sec — per-problem timeout in stage2 (default 120) - OPENEVOLVE_Z3_BIN str — z3 binary path (default "z3") + OPENEVOLVE_MAX_PROBLEMS int — cap stage2 problem count + OPENEVOLVE_STAGE1_TIMEOUT int sec — per-problem timeout in stage1 (default 24) + OPENEVOLVE_STAGE2_TIMEOUT int sec — per-problem timeout in stage2 (default 120) + 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 @@ -29,6 +42,7 @@ 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 # noqa: E402 from openevolve.evaluation_result import EvaluationResult # noqa: E402 @@ -36,8 +50,9 @@ _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _STAGE1_SAMPLE = _HERE / "stage1_sample.json" +_LOCAL_BASELINE = _HERE / "local_baseline.json" -_Z3_BIN = os.environ.get("OPENEVOLVE_Z3_BIN", "z3") +_PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") # None -> sys.executable def _load_program(path): @@ -49,16 +64,39 @@ def _load_program(path): def _load_problems(): + # If rebaseline_local.py has been run, restrict stage2 to that SHA subset + # (20 stratified). Reasons: + # - non-rebaselined problems would use raw_ms recorded on a different + # machine, skewing speedup + # - smaller stage2 = faster iterations + # Local elapsed_ms used only when local result matches raw, else fall + # back to raw_ms for that SHA 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"] + if local and sha not in local: + continue + 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": d["problem_sha256"], + "sha": sha, "smt2": d["smt2_filename"], - "baseline_ms": d["z3_status"]["elapsed_ms"], - "baseline_result": d["z3_status"]["result"], + "baseline_ms": baseline_ms, + "baseline_result": baseline_result, + "baseline_stats": baseline_stats, } ) return rows @@ -134,7 +172,7 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] - results = [] + # 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(): @@ -142,29 +180,139 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): {"error": f"smt2 not found: {p['smt2']}"}, {"missing_file": str(smt2_path), "stage": stage_name}, ) - r = run_z3(smt2_path, params, per_problem_timeout_s, z3_bin=_Z3_BIN) + + # 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). + # Each task is pinned to core (i % N) via taskset (Linux) so concurrent + # runs don't fight for the same physical core. Default 1 = sequential. + n_parallel = min(parallel_solvers(default=1), len(problems)) + + def _solve(idx_p): + idx, p = idx_p + smt2_path = _RAW_DIR / p["smt2"] + core = (idx % n_parallel) if n_parallel > 1 else None + r = run_z3(smt2_path, params, per_problem_timeout_s, + python_bin=_PYTHON_BIN, cpu_core=core) + return idx, p, r + + 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 _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().", + 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 = _solve(pair) + print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms", 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: cancel pending futures on first failure. In-flight tasks + # keep running until subprocess timeout (with __exit__ waits for them); + # acceptable for stage1 (timeout ~24s). + from concurrent.futures import ThreadPoolExecutor, as_completed + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_solve, pair) for pair in enumerate(problems)] + for fut in as_completed(futures): + if abort is not None: + continue + idx, p, r = fut.result() + core = idx % n_parallel + print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " + f"{r.get('result')} {r.get('elapsed_ms')}ms " + 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], @@ -174,6 +322,8 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): "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 ] @@ -183,6 +333,7 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): 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], @@ -191,7 +342,9 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): def evaluate_stage1(program_path): - timeout = int(os.environ.get("OPENEVOLVE_STAGE1_TIMEOUT", "15")) + # Default 24s per problem → 5 problems = 60-120s wall-clock budget + # (60s lower bound on baseline-like variants, 120s upper bound on all-timeout). + timeout = int(os.environ.get("OPENEVOLVE_STAGE1_TIMEOUT", "24")) problems = _filter_stage1(_load_problems()) return _evaluate(program_path, problems, timeout, "stage1") @@ -203,4 +356,7 @@ def evaluate_stage2(program_path): def evaluate(program_path): - return evaluate_stage2(program_path) + # Evolution uses stage1 (5 sampled problems) only — fast iteration loop. + # Stage2 (20 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/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json new file mode 100644 index 0000000000..8bb23c8843 --- /dev/null +++ b/input/z3-bench/evolve/shared/local_baseline.json @@ -0,0 +1,806 @@ +{ + "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8": { + "elapsed_ms": 8244, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 13086, + "stats": { + "conflicts": 346, + "decisions": 13879, + "propagations": 1852731, + "binary propagations": 1542459, + "restarts": 2, + "final checks": 57, + "added eqs": 101168, + "mk clause": 53762, + "mk clause binary": 14972623, + "del clause": 788, + "minimized lits": 4063, + "num checks": 58, + "mk bool var": 61279, + "pb resolves": 27, + "pb conflicts": 27, + "pb propagations": 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"arith-conflicts": 7, + "arith-bound-propagations-lp": 2534, + "arith-diseq": 464, + "arith-make-feasible": 542, + "arith-max-columns": 1633, + "arith-max-rows": 1015, + "arith-offset-eqs": 209, + "arith-fixed-eqs": 790, + "solve-eqs-steps": 140602, + "solve-eqs-elim-vars": 27284, + "rlimit count": 21056686, + "max memory": 4321.87, + "memory": 4069.19, + "num allocs": 33745826870.0, + "time": 6.802 + } + }, + "7ac5a84f68bc6e1488f9c6074a42505a6c95fa8df4f615c4a0ae54359dd3b416": { + "elapsed_ms": 6582, + "result": "Unsat", + "matches_raw": true, + "raw_elapsed_ms": 14992, + "stats": { + "conflicts": 27, + "decisions": 79, + "propagations": 144900, + "binary propagations": 123789, + "added eqs": 9276, + "mk clause": 53346, + "mk clause binary": 14961098, + "del clause": 609, + "minimized lits": 43, + "num checks": 1, + "mk bool var": 61148, + "pb propagations": 17, + "pb predicates": 363, + "arith eq adapter": 1591, + "arith-lower": 3345, + "arith-upper": 3271, + "arith-conflicts": 11, + "arith-bound-propagations-lp": 1758, + "arith-diseq": 405, + "arith-make-feasible": 328, + "arith-max-columns": 1631, + "arith-max-rows": 1015, + "arith-offset-eqs": 231, + "arith-fixed-eqs": 482, + "solve-eqs-steps": 140314, + "solve-eqs-elim-vars": 27284, + "rlimit count": 21013773, + "max memory": 4321.4, + "memory": 4068.67, + "num allocs": 33595594265.0, + "time": 6.582 + } + }, + "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c": { + "elapsed_ms": 7522, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 16518, + "stats": { + "conflicts": 133, + "decisions": 8000, + "propagations": 1042454, + "binary propagations": 860243, + "final checks": 63, + "added eqs": 50767, + "mk clause": 53891, + "mk clause binary": 15003886, + "del clause": 804, + "minimized lits": 4026, + "num checks": 64, + "mk bool var": 61797, + "pb resolves": 27, + "pb conflicts": 27, + "pb propagations": 234, + "pb predicates": 367, + "arith eq adapter": 1672, + "arith-lower": 19978, + "arith-upper": 22681, + "arith-conflicts": 26, + "arith-bound-propagations-lp": 17475, + "arith-diseq": 8202, + "arith-make-feasible": 4982, + "arith-max-columns": 1644, + "arith-max-rows": 1023, + "arith-offset-eqs": 2993, + "arith-fixed-eqs": 6646, + "solve-eqs-steps": 141131, + "solve-eqs-elim-vars": 27438, + "rlimit count": 25115048, + "max memory": 4323.2, + "memory": 4078.04, + "num allocs": 48281023551.0, + "time": 7.522 + } + }, + "bc60e5e73924b7f663b6320df3ffba8cb70b7ca50069d0bb586a8656aca15677": { + "elapsed_ms": 15924, + "result": "Unsat", + "matches_raw": true, + "raw_elapsed_ms": 32076, + "stats": { + "conflicts": 106, + "decisions": 1044, + "propagations": 1000545, + "binary propagations": 824948, + "added eqs": 140293, + "mk clause": 115565, + "mk clause binary": 18694422, + "del clause": 9573, + "minimized lits": 774, + "num checks": 1, + "mk bool var": 149183, + "pb propagations": 7, + "pb predicates": 5216, + "arith eq adapter": 7426, + "arith-lower": 49575, + "arith-upper": 49453, + "arith-conflicts": 6, + "arith-bound-propagations-lp": 16950, + "arith-diseq": 1131, + "arith-make-feasible": 1874, + "arith-max-columns": 7100, + "arith-max-rows": 4531, + "arith-offset-eqs": 5065, + "arith-fixed-eqs": 6959, + "solve-eqs-steps": 246577, + "solve-eqs-elim-vars": 74707, + "rlimit count": 72627630, + "max memory": 4735.35, + "memory": 4491.88, + "num allocs": 130462954454.0, + "time": 15.924 + } + }, + "96158dd28c0bfa150bffb4e0a6bcd7689cb8fa3d041af533c002004b5c1aec4d": { + "elapsed_ms": 16731, + "result": "Unsat", + "matches_raw": true, + "raw_elapsed_ms": 33160, + "stats": { + "conflicts": 83, + "decisions": 760, + "propagations": 645209, + "binary propagations": 532485, + "added eqs": 91467, + "mk clause": 115660, + "mk clause binary": 18710201, + "del clause": 9818, + "minimized lits": 414, + "num checks": 1, + "mk bool var": 149108, + "pb propagations": 5, + "pb predicates": 5215, + "arith eq adapter": 7563, + "arith-lower": 30211, + "arith-upper": 30387, + "arith-conflicts": 2, + "arith-bound-propagations-lp": 11135, + "arith-diseq": 818, + "arith-make-feasible": 1252, + "arith-max-columns": 7084, + "arith-max-rows": 4526, + "arith-offset-eqs": 4174, + "arith-fixed-eqs": 4060, + "solve-eqs-steps": 242535, + "solve-eqs-elim-vars": 75194, + "rlimit count": 71788859, + "max memory": 4752.97, + "memory": 4510.6, + "num allocs": 134129336930.0, + "time": 16.73 + } + } +} diff --git a/input/z3-bench/evolve/shared/runtime.py b/input/z3-bench/evolve/shared/runtime.py new file mode 100644 index 0000000000..e951cc039d --- /dev/null +++ b/input/z3-bench/evolve/shared/runtime.py @@ -0,0 +1,49 @@ +""" +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 diff --git a/input/z3-bench/evolve/shared/score.py b/input/z3-bench/evolve/shared/score.py index 3f73f5f4dd..fb212d6311 100644 --- a/input/z3-bench/evolve/shared/score.py +++ b/input/z3-bench/evolve/shared/score.py @@ -1,10 +1,49 @@ """ -Scoring: geomean(speedup) * solved_rate^2. +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): @@ -17,6 +56,9 @@ def score(per_problem): "regressions": 0, "solved": 0, "total": 0, + "efficiency": 1.0, + "efficiency_pairs": 0, + "stats_weight": 0.0, } speedups = [] @@ -37,7 +79,16 @@ def score(per_problem): log_sum = sum(math.log(s) for s in speedups) geomean = math.exp(log_sum / len(speedups)) solved_rate = solved / n - combined = geomean * (solved_rate**2) + + 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), @@ -46,4 +97,7 @@ def score(per_problem): "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/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json index 8191306e7a..a31bb6bfb3 100644 --- a/input/z3-bench/evolve/shared/stage1_sample.json +++ b/input/z3-bench/evolve/shared/stage1_sample.json @@ -1,38 +1,37 @@ { - "seed": 42, - "num_buckets": 5, + "selection": "5 Sat-preferred with baseline_ms in [4000, 15000] (Unsat / sub-MIN_MS fallbacks)", "source": "z3-bench/problems.jsonl", "sha256": [ - "ac90ca97ff993ebed2e7b1dfdd4e670b0bac1c67d04ac3d592d58456dc4425d9", - "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", - "29efe6d38d7b8fcf41ed69b73f5a0bb370f782ab61ffe564fbe41512ea75ce5f", - "86468fd861ffa391b567ca57dc57c9b95b8577942d3fd2587e244073bdee43ef", - "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44" + "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", + "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", + "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", + "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12", + "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a" ], "summary": [ { - "sha": "ac90ca97ff99", - "baseline_ms": 239, - "baseline_result": "Unsat" + "sha": "75ee534dbc92", + "baseline_ms": 13086, + "baseline_result": "Sat" }, { - "sha": "133383a624ef", - "baseline_ms": 480, - "baseline_result": "Unsat" + "sha": "069af7891076", + "baseline_ms": 13885, + "baseline_result": "Sat" }, { - "sha": "29efe6d38d7b", - "baseline_ms": 12712, - "baseline_result": "Unsat" + "sha": "318bc251e0bd", + "baseline_ms": 14232, + "baseline_result": "Sat" }, { - "sha": "86468fd861ff", - "baseline_ms": 15671, + "sha": "493242c37992", + "baseline_ms": 14353, "baseline_result": "Sat" }, { - "sha": "3854194b901b", - "baseline_ms": 66100, + "sha": "4a02d7846394", + "baseline_ms": 14428, "baseline_result": "Sat" } ] diff --git a/input/z3-bench/evolve/shared/z3_runner.py b/input/z3-bench/evolve/shared/z3_runner.py index 4cfccebbfe..7113fe0441 100644 --- a/input/z3-bench/evolve/shared/z3_runner.py +++ b/input/z3-bench/evolve/shared/z3_runner.py @@ -1,52 +1,48 @@ """ -Run Z3 CLI on a single SMT2 file with parameter dict. -Subprocess isolation: each run a fresh process with -T: wall-clock cap. +Run a single SMT2 file through Z3 with given parameters. + +Implementation: spawn `_z3_solve_worker.py` as a subprocess and communicate +via stdout JSON. 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 re +import json +import pathlib import shutil import subprocess +import sys import time -_RESULT_RE = re.compile(r"^(sat|unsat|unknown)\b", re.MULTILINE) -_INVALID_PARAM_RES = [ - re.compile(r"unknown\s+parameter\s+'?([\w.\-]+)'?", re.IGNORECASE), - re.compile(r"invalid\s+parameter\s+'?([\w.\-]+)'?", re.IGNORECASE), - re.compile(r"unknown\s+option\s+'?([\w.\-]+)'?", re.IGNORECASE), - re.compile(r"error\s+setting\s+'?([\w.\-]+)'?", re.IGNORECASE), -] - - -def _format_value(v): - if isinstance(v, bool): - return "true" if v else "false" - return str(v) +_WORKER = str(pathlib.Path(__file__).resolve().parent / "_z3_solve_worker.py") +_TASKSET = shutil.which("taskset") # Linux only; None on macOS / missing -def detect_invalid_param(stderr): - for rx in _INVALID_PARAM_RES: - m = rx.search(stderr) - if m: - return m.group(1) - if any(tok in stderr.lower() for tok in ("unknown parameter", "invalid parameter", "unknown option")): - return "" - return None +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). -def run_z3(smt2_path, params, timeout_s, z3_bin="z3"): - """ - Returns dict: - success: {"result": "Sat"|"Unsat"|"Unknown", "elapsed_ms": int} - timeout: {"result": "Unknown", "elapsed_ms": int, "timeout": True} - invalid: {"invalid_param": str, "stderr": str, "result": "Unknown", "elapsed_ms": int} - crash: {"result": "Unknown", "elapsed_ms": int, "error": str, "stderr": str} + 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). """ - if shutil.which(z3_bin) is None: - return {"result": "Unknown", "elapsed_ms": 0, "error": f"z3 binary not found: {z3_bin}"} - - args = [z3_bin, f"-T:{int(timeout_s)}", "-smt2"] - for k, v in params.items(): - args.append(f"{k}={_format_value(v)}") - args.append(str(smt2_path)) + 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: @@ -54,38 +50,48 @@ def run_z3(smt2_path, params, timeout_s, z3_bin="z3"): args, capture_output=True, text=True, - timeout=timeout_s + 10, + timeout=timeout_s + 15, # grace for z3 startup + parse ) except subprocess.TimeoutExpired: return { "result": "Unknown", - "elapsed_ms": int(timeout_s * 1000), + "elapsed_ms": int((time.monotonic() - t0) * 1000), "timeout": True, + "stats": {}, } - elapsed_ms = int((time.monotonic() - t0) * 1000) - stdout = proc.stdout or "" - stderr = proc.stderr or "" + stdout = (proc.stdout or "").strip() + stderr = (proc.stderr or "").strip() - bad = detect_invalid_param(stderr) - if bad: + if not stdout: return { - "invalid_param": bad, - "stderr": stderr[-2000:], "result": "Unknown", - "elapsed_ms": elapsed_ms, + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker produced no output (rc={proc.returncode})", + "stderr": stderr[-2000:], + "stats": {}, } - m = _RESULT_RE.search(stdout) - if not m: + # 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": elapsed_ms, - "error": f"no result token (rc={proc.returncode})", - "stderr": stderr[-1000:], + "elapsed_ms": int((time.monotonic() - t0) * 1000), + "error": f"worker json decode: {e}", + "stderr": (stderr + "\n--stdout--\n" + stdout)[-2000:], + "stats": {}, } - return { - "result": {"sat": "Sat", "unsat": "Unsat", "unknown": "Unknown"}[m.group(1)], - "elapsed_ms": elapsed_ms, - } + 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/shared/z3_valid_keys.json b/input/z3-bench/evolve/shared/z3_valid_keys.json new file mode 100644 index 0000000000..2ff0093f49 --- /dev/null +++ b/input/z3-bench/evolve/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/validate_keys.py b/input/z3-bench/evolve/validate_keys.py new file mode 100644 index 0000000000..b289ccce07 --- /dev/null +++ b/input/z3-bench/evolve/validate_keys.py @@ -0,0 +1,160 @@ +""" +Validate phase EVOLVE-BLOCK keys against the installed z3 binary. + +Strategy: + 1. `z3 -pm` -> list of module names + 2. `z3 -pm:` for each module -> option names + types + defaults + 3. `z3 -p` -> global (module-less) option names + -> build canonical set of valid `module.option` (and bare global) keys + Then for each phase initial_program.py, report invalid keys. + +Run inside the container (z3 binary required): + + python input/z3-bench/evolve/validate_keys.py + +Output: + shared/z3_valid_keys.json # full valid set + stdout report per phase, listing invalid keys + +Does NOT modify initial_program.py. Edit manually after reviewing. +""" +import importlib.util +import json +import pathlib +import re +import shutil +import subprocess +import sys + +ROOT = pathlib.Path(__file__).resolve().parent +SHARED = ROOT / "shared" +PHASES = [ + "phase1_opt_sls", + "phase2_sat", + "phase3_smt", + "phase4_unified", +] + + +_OPTION_LINE_RE = re.compile(r"^\s+([\w\-]+(?:\.[\w\-]+)*)\s+\(") + + +def _run_z3(args): + proc = subprocess.run(["z3", *args], capture_output=True, text=True) + if proc.returncode != 0: + # Some -pm: invocations exit non-zero on unknown module; treat as soft. + return proc.stdout, proc.stderr, proc.returncode + return proc.stdout, proc.stderr, 0 + + +def _parse_options(text): + """Pull ' name (type) ...' lines out of any z3 doc dump.""" + names = [] + for line in text.splitlines(): + m = _OPTION_LINE_RE.match(line) + if m: + names.append(m.group(1)) + return names + + +# Z3 4.13.x format: `[module] (, description: )?` +# Older builds: `[module] ` alone. Take only the first identifier token. +_MODULE_HEADER_RE = re.compile(r"^\[module\]\s+([A-Za-z_][\w\-]*)") + + +def _list_modules(): + stdout, stderr, rc = _run_z3(["-pm"]) + if rc != 0: + print(f"`z3 -pm` failed: {stderr.strip()}", file=sys.stderr) + sys.exit(1) + mods = set() + for line in stdout.splitlines(): + m = _MODULE_HEADER_RE.match(line) + if m: + mods.add(m.group(1)) + if not mods: + print("warning: no `[module] ` headers found in `z3 -pm` output", file=sys.stderr) + return sorted(mods) + + +def get_valid_keys(): + if shutil.which("z3") is None: + print("ERROR: z3 binary not on PATH. Install in container first.", file=sys.stderr) + sys.exit(1) + + valid = set() + + # 1. Globals via `z3 -p` + stdout, _, rc = _run_z3(["-p"]) + if rc == 0: + for opt in _parse_options(stdout): + valid.add(opt) + + # 2. Modules + their options. Try each candidate token from `-pm` output; + # silently skip ones z3 rejects (prose/noise gets filtered this way). + mods = _list_modules() + if not mods: + print("warning: no modules parsed from `z3 -pm`", file=sys.stderr) + tried = 0 + accepted = 0 + debug_dumped = False + for mod in mods: + tried += 1 + stdout, stderr, rc = _run_z3([f"-pm:{mod}"]) + if rc != 0: + continue # quiet skip + accepted += 1 + opts = _parse_options(stdout) + if not opts and not debug_dumped: + print(f" no options parsed from module {mod!r}; raw output (first 30 lines):", file=sys.stderr) + for line in stdout.splitlines()[:30]: + print(f" | {line}", file=sys.stderr) + debug_dumped = True + for opt in opts: + valid.add(f"{mod}.{opt}") + print(f" modules: {accepted}/{tried} accepted, {len(valid)} valid keys total") + + return valid + + +def load_program_params(phase_dir): + """Load initial_program.py and return its get_params() output.""" + sys.path.insert(0, str(SHARED)) + spec = importlib.util.spec_from_file_location( + f"prog_{phase_dir.name}", phase_dir / "initial_program.py" + ) + m = importlib.util.module_from_spec(spec) + spec.loader.exec_module(m) + return m.get_params() + + +def main(): + print("Probing z3 -p / z3 -pm / z3 -pm: ...") + valid = get_valid_keys() + out = SHARED / "z3_valid_keys.json" + out.write_text(json.dumps(sorted(valid), indent=2)) + print(f"wrote {out.relative_to(ROOT)} ({len(valid)} entries)\n") + + any_invalid = False + for ph in PHASES: + d = ROOT / ph + if not (d / "initial_program.py").exists(): + continue + params = load_program_params(d) + invalid = sorted(k for k in params if k not in valid) + status = "OK" if not invalid else f"INVALID x{len(invalid)}" + print(f"=== {ph}: {len(params)} keys, {status} ===") + for k in invalid: + print(f" - {k!r}") + print() + if invalid: + any_invalid = True + + if any_invalid: + print("Edit the offending initial_program.py files to remove or rename invalid keys.") + sys.exit(2) + print("All phase keys validate against `z3 -pm` introspection.") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/verify_stage1_baseline.py b/input/z3-bench/evolve/verify_stage1_baseline.py new file mode 100644 index 0000000000..820e3b4e87 --- /dev/null +++ b/input/z3-bench/evolve/verify_stage1_baseline.py @@ -0,0 +1,123 @@ +""" +Verify stage1_sample.json reproducibility against raw baseline. + +For each of the 5 sampled problems, run z3 with BASELINE params and compare: + - result must match baseline_result + - elapsed_ms ratio (got / baseline) must lie in [LOW, HIGH] + +Per-problem timeout = baseline_ms (as recorded in raw data). +Single run per problem (no median). +""" +import json +import math +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +sys.path.insert(0, str(_HERE / "shared")) + +from baseline_params import BASELINE # noqa: E402 +from z3_runner import run_z3 # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" + +RATIO_LOW = 0.5 +RATIO_HIGH = 2.0 + + +def _load_sample_shas(): + return list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + + +def _index_problems_by_sha(): + 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"], + } + return idx + + +def verify(): + shas = _load_sample_shas() + idx = _index_problems_by_sha() + + rows = [] + fail = 0 + warn = 0 + for sha in shas: + meta = idx.get(sha) + if meta is None: + print(f"[FAIL] {sha[:12]} not in problems.jsonl") + fail += 1 + continue + + smt2_path = _RAW_DIR / meta["smt2"] + if not smt2_path.exists(): + print(f"[FAIL] {sha[:12]} missing smt2 {smt2_path}") + fail += 1 + continue + + timeout_s = max(1, math.ceil(meta["baseline_ms"] / 1000)) + r = run_z3(smt2_path, BASELINE, timeout_s) + + got_result = r.get("result", "Unknown") + got_ms = int(r.get("elapsed_ms", 0)) + is_timeout = bool(r.get("timeout")) + invalid = r.get("invalid_param") + err = r.get("error") + stderr = r.get("stderr") + + ratio = got_ms / max(meta["baseline_ms"], 1) + result_ok = (got_result == meta["baseline_result"]) + ratio_ok = RATIO_LOW <= ratio <= RATIO_HIGH + + if invalid: + status = "FAIL(invalid)" + fail += 1 + elif err: + status = "FAIL(error)" + fail += 1 + elif is_timeout or not result_ok: + status = "FAIL" + fail += 1 + elif not ratio_ok: + status = "WARN" + warn += 1 + else: + status = "OK" + + rows.append((sha, meta, got_result, got_ms, ratio, status, invalid, is_timeout, err, stderr)) + + print() + print(f"{'sha':<14}{'base_res':<10}{'got_res':<10}" + f"{'base_ms':>10}{'got_ms':>10}{'ratio':>8} status") + print("-" * 78) + for sha, meta, got_result, got_ms, ratio, status, invalid, is_timeout, err, stderr in rows: + extra = "" + if invalid: + extra = f" invalid={invalid}" + elif err: + extra = f" err={err[:200]}" + elif is_timeout: + extra = " (timeout)" + print(f"{sha[:12]:<14}{meta['baseline_result']:<10}{got_result:<10}" + f"{meta['baseline_ms']:>10}{got_ms:>10}{ratio:>7.2f}x {status}{extra}") + if stderr and err: + print(f" stderr: {stderr[:400]}") + + print() + print(f"summary: {len(rows) - fail - warn} ok, {warn} warn, {fail} fail " + f"(ratio band [{RATIO_LOW}, {RATIO_HIGH}], timeout=baseline_ms)") + return 0 if fail == 0 else 1 + + +if __name__ == "__main__": + sys.exit(verify()) From 82c36485ab39c1ae3e3d4bde52d5f0e378b82e28 Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 18 May 2026 16:39:11 +0900 Subject: [PATCH 04/42] [feat] enable multi-pricessing --- input/z3-bench/evolve/_probe_parallel.py | 74 +++ input/z3-bench/evolve/extract_best.py | 82 ++- .../evolve/phase4_unified/initial_program.py | 226 ++++++- input/z3-bench/evolve/rebaseline_local.py | 23 +- input/z3-bench/evolve/run_phase.sh | 54 +- input/z3-bench/evolve/shared/evaluator.py | 31 +- .../evolve/shared/local_baseline.json | 609 +----------------- input/z3-bench/evolve/shared/phase1_best.json | 33 + input/z3-bench/evolve/shared/phase2_best.json | 118 ++++ input/z3-bench/evolve/shared/phase3_best.json | 77 +++ 10 files changed, 683 insertions(+), 644 deletions(-) create mode 100644 input/z3-bench/evolve/_probe_parallel.py create mode 100644 input/z3-bench/evolve/shared/phase1_best.json create mode 100644 input/z3-bench/evolve/shared/phase2_best.json create mode 100644 input/z3-bench/evolve/shared/phase3_best.json diff --git a/input/z3-bench/evolve/_probe_parallel.py b/input/z3-bench/evolve/_probe_parallel.py new file mode 100644 index 0000000000..c7fd7d9666 --- /dev/null +++ b/input/z3-bench/evolve/_probe_parallel.py @@ -0,0 +1,74 @@ +"""Probe: serial vs parallel z3 measurement noise (run inside docker).""" +import json +import pathlib +import statistics +import sys +import time +from concurrent.futures import ThreadPoolExecutor + +_HERE = pathlib.Path(__file__).resolve().parent +sys.path.insert(0, str(_HERE / "shared")) +from baseline_params import BASELINE # noqa +from z3_runner import run_z3 # noqa + + +def main(): + shas = json.loads((_HERE / "shared/stage1_sample.json").read_text())["sha256"] + prob_idx = {} + with open(_HERE.parent / "problems.jsonl") as f: + for line in f: + d = json.loads(line) + if d["problem_sha256"] in shas: + prob_idx[d["problem_sha256"]] = d["smt2_filename"] + + raw_dir = _HERE.parent / "raw-data" + tasks = [(i, sha, raw_dir / prob_idx[sha]) for i, sha in enumerate(shas)] + + def solve(i, sha, smt2, pin): + r = run_z3(smt2, BASELINE, 60, cpu_core=pin) + return i, sha, r + + # --- A: serial, no pin (3 reps each) --- + print("=== A: serial no-pin (3 reps) ===") + serial = {sha: [] for _, sha, _ in tasks} + for rep in range(3): + for i, sha, smt2 in tasks: + t0 = time.monotonic() + _, _, r = solve(i, sha, smt2, None) + wall = int((time.monotonic() - t0) * 1000) + serial[sha].append((r.get("result"), r["elapsed_ms"], wall)) + print(f" rep{rep} [{i+1}/5] {sha[:10]} {r.get('result')} " + f"z3={r['elapsed_ms']}ms wall={wall}ms") + + # --- B: parallel=5, pin (3 reps batch) --- + print("\n=== B: parallel=5 pinned (3 reps) ===") + par = {sha: [] for _, sha, _ in tasks} + for rep in range(3): + with ThreadPoolExecutor(max_workers=5) as ex: + futs = [ex.submit(solve, i, sha, smt2, i % 5) for i, sha, smt2 in tasks] + for fut in futs: + i, sha, r = fut.result() + par[sha].append((r.get("result"), r["elapsed_ms"])) + print(f" rep{rep} [{i+1}/5] {sha[:10]} {r.get('result')} " + f"z3={r['elapsed_ms']}ms core={i % 5}") + + # --- summary --- + print("\n=== SUMMARY (z3 elapsed_ms) ===") + print(f"{'sha':<12} {'serial(med/stdev)':<24} {'parallel(med/stdev)':<24} par/ser") + for _, sha, _ in tasks: + s = [t[1] for t in serial[sha]] + p = [t[1] for t in par[sha]] + sm, ss = statistics.median(s), statistics.stdev(s) if len(s) > 1 else 0 + pm, ps = statistics.median(p), statistics.stdev(p) if len(p) > 1 else 0 + ratio = pm / sm if sm else 0 + print(f"{sha[:12]} {sm:>7.0f} / {ss:>6.0f} {pm:>7.0f} / {ps:>6.0f} " + f"{ratio:.2f}x") + # result drift + s_res = set(t[0] for t in serial[sha]) + p_res = set(t[0] for t in par[sha]) + if s_res != p_res: + print(f" RESULT DRIFT: serial={s_res} parallel={p_res}") + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/extract_best.py b/input/z3-bench/evolve/extract_best.py index bb0a47c160..109a5211a7 100644 --- a/input/z3-bench/evolve/extract_best.py +++ b/input/z3-bench/evolve/extract_best.py @@ -2,8 +2,16 @@ After phase N completes, extract get_phase_overrides() from its best_program.py and write shared/phaseN_best.json. Phase N+1 auto-loads it. -Usage: python extract_best.py +Default source: openevolve_output/best/best_program.py (created on normal phase +exit). For interrupted runs, pass --from-checkpoints to scan all +openevolve_output/checkpoints/checkpoint_*/ and pick the program with the +highest combined_score (read from best_program_info.json). + +Usage: + python extract_best.py + python extract_best.py --from-checkpoints """ +import argparse import importlib.util import json import pathlib @@ -19,24 +27,68 @@ } -def main(): - if len(sys.argv) != 2: - print("usage: python extract_best.py <1|2|3>", file=sys.stderr) - sys.exit(2) +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) - n = int(sys.argv[1]) - if n not in PHASE_DIRS: - print(f"phase must be 1, 2, or 3 (got {n})", file=sys.stderr) - sys.exit(2) + 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()) + score = 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 score > best_score: + best_score = score + best_py = prog_path + best_ck = ck - phase_dir = ROOT / PHASE_DIRS[n] - 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) + if best_py is None: + print(f"no usable best_program.py in any checkpoint under {ckpt_root}", file=sys.stderr) sys.exit(1) - # Make shared/ importable for the module under test + print(f"[extract_best] from-checkpoints: picked {best_ck.name} " + f"(combined_score={best_score:.4f})") + return best_py + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("phase", type=int, choices=[1, 2, 3], help="phase number") + ap.add_argument( + "--from-checkpoints", + action="store_true", + help="scan checkpoint_*/ dirs and pick highest combined_score " + "(use for interrupted runs without best/ dir)", + ) + args = ap.parse_args() + + n = args.phase + phase_dir = ROOT / PHASE_DIRS[n] + + if args.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) diff --git a/input/z3-bench/evolve/phase4_unified/initial_program.py b/input/z3-bench/evolve/phase4_unified/initial_program.py index 988488b0ba..11575f0e92 100644 --- a/input/z3-bench/evolve/phase4_unified/initial_program.py +++ b/input/z3-bench/evolve/phase4_unified/initial_program.py @@ -20,9 +20,229 @@ # EVOLVE-BLOCK-START -# Seeded with baseline only. Run prepare_phase4.py after phases 1-3 finish -# to replace this dict with the union of their winners. -UNIFIED_OVERRIDES = {} +# 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': 5, + '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': 'rc2', + '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': False, + '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': 0.1, + 'sat.branching.heuristic': 'lrb', + 'sat.burst_search': 0, + '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': 'dyn_psm', + 'sat.gc.burst': False, + 'sat.gc.defrag': True, + 'sat.gc.increment': 500, + 'sat.gc.initial': 80000, + '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': 'binary_merge', + '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': 'ema', + 'sat.restart.emafastglue': 0.03, + 'sat.restart.emaslowglue': 1e-05, + 'sat.restart.factor': 1.5, + 'sat.restart.fast': True, + 'sat.restart.initial': 100, + 'sat.restart.margin': 1.1, + 'sat.retain_blocked_clauses': True, + 'sat.scc': True, + 'sat.scc.tr': True, + 'sat.search.sat.conflicts': 1500, + 'sat.search.unsat.conflicts': 1500, + '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': 23.0, + 'sls.walksat_ucb_forget': 0.1, + 'sls.walksat_ucb_init': False, + 'sls.walksat_ucb_noise': 0.0002, + 'sls.wp': 23, + '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.rounds': 512, + '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': 2, + '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': 2, + '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': 1000, + 'smt.pb.conflict_frequency': 1000, + 'smt.pb.learn_complements': True, + 'smt.phase_caching_off': 100, + 'smt.phase_caching_on': 400, + 'smt.phase_selection': 3, + 'smt.propagate_values': True, + 'smt.pull_nested_quantifiers': False, + 'smt.qi.cost': '(+ weight generation)', + 'smt.qi.eager_threshold': 15.0, + 'smt.qi.lazy_threshold': 30.0, + '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': 1, + '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 diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index 2676712ff7..7a555a7bcb 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -73,6 +73,7 @@ def main(): return 2 tasks.append((i, meta, smt2_path)) + import queue as _queue n_parallel = min(parallel_solvers(default=1), len(tasks)) print(f"rebaselining stage1 evolution sample: {len(tasks)} problems " f"(from stage1_sample.json)") @@ -80,12 +81,22 @@ def main(): f"(taskset core pin)") print() + # Cores leased from a queue so each in-flight task holds a unique slot. + # Serial mode also leases core 0 (symmetric with parallel) — keeps the + # baseline measurement under the same pin envelope variants will see. + _core_pool = _queue.Queue() + for _c in range(n_parallel): + _core_pool.put(_c) + def _solve(task): i, meta, smt2_path = task timeout_s = max(60, math.ceil(meta["raw_ms"] * 2 / 1000)) - core = (i % n_parallel) if n_parallel > 1 else None - res = run_z3(smt2_path, BASELINE, timeout_s, cpu_core=core) - return i, meta, res + core = _core_pool.get() + try: + res = run_z3(smt2_path, BASELINE, timeout_s, cpu_core=core) + finally: + _core_pool.put(core) + return i, meta, res, core t_start = time.monotonic() completed = [] @@ -101,7 +112,7 @@ def _solve(task): out = {} mismatch = 0 - for i, meta, res in completed: + for i, meta, res, core in completed: got_result = res.get("result", "Unknown") got_ms = int(res.get("elapsed_ms", 0)) ok = (got_result == meta["raw_result"]) @@ -110,11 +121,11 @@ def _solve(task): flag = "" if ok else " MISMATCH" ratio = got_ms / max(meta["raw_ms"], 1) - core_tag = f" core={i % n_parallel}" if n_parallel > 1 else "" print( f" [{i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " f"raw={meta['raw_result']:<7}/{meta['raw_ms']:>7}ms " - f"local={got_result:<7}/{got_ms:>7}ms ratio={ratio:.2f}x{flag}{core_tag}", + f"local={got_result:<7}/{got_ms:>7}ms ratio={ratio:.2f}x{flag} " + f"core={core}", flush=True, ) diff --git a/input/z3-bench/evolve/run_phase.sh b/input/z3-bench/evolve/run_phase.sh index b3840e6b60..48dc7a2e4a 100755 --- a/input/z3-bench/evolve/run_phase.sh +++ b/input/z3-bench/evolve/run_phase.sh @@ -1,25 +1,40 @@ #!/bin/bash # Run one phase of Z3 parameter evolution. -# Usage: ./run_phase.sh {1|2|3|4} [extra openevolve-run.py flags...] +# Usage: +# ./run_phase.sh {1|2|3|4} [extra openevolve-run.py flags...] +# ./run_phase.sh {1|2|3} --extract-only # skip evolution; aggregate +# # results from existing +# # openevolve_output/checkpoints/ # -# Phase iterations are set per-phase below. Override via --iterations . +# Iteration count comes from config.yaml (max_iterations). Override via +# `--iterations ` extra flag if needed. # OpenEvolve outputs land in /openevolve_output/. set -euo pipefail if [ $# -lt 1 ]; then - echo "usage: $0 {1|2|3|4} [extra flags]" >&2 + echo "usage: $0 {1|2|3|4} [--extract-only] [extra flags]" >&2 exit 2 fi PHASE="$1" shift +EXTRACT_ONLY=0 +PASSTHROUGH=() +for arg in "$@"; do + case "$arg" in + --extract-only) EXTRACT_ONLY=1 ;; + *) PASSTHROUGH+=("$arg") ;; + esac +done +set -- "${PASSTHROUGH[@]+"${PASSTHROUGH[@]}"}" + case "$PHASE" in - 1) DIR="phase1_opt_sls"; ITERS=80 ;; - 2) DIR="phase2_sat"; ITERS=150 ;; - 3) DIR="phase3_smt"; ITERS=120 ;; - 4) DIR="phase4_unified"; ITERS=60 ;; + 1) DIR="phase1_opt_sls" ;; + 2) DIR="phase2_sat" ;; + 3) DIR="phase3_smt" ;; + 4) DIR="phase4_unified" ;; *) echo "phase must be 1, 2, 3, or 4 (got $PHASE)" >&2; exit 2 ;; esac @@ -27,6 +42,18 @@ ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" REPO_ROOT="$(cd "$ROOT/../../.." && pwd)" RUNNER="$REPO_ROOT/openevolve-run.py" +# --extract-only: skip evolution + setup, jump straight to aggregation. +if [ "$EXTRACT_ONLY" = "1" ]; then + if [ "$PHASE" -eq 4 ]; then + echo "--extract-only not supported for phase 4 (no extract step)" >&2 + exit 2 + fi + echo "[run_phase] --extract-only: aggregating from checkpoints (phase $PHASE)..." + python "$ROOT/extract_best.py" "$PHASE" --from-checkpoints + echo "[run_phase] extract-only done. next: $0 $((PHASE + 1))" + exit 0 +fi + if [ ! -f "$RUNNER" ]; then echo "openevolve-run.py not found at $RUNNER" >&2 exit 1 @@ -37,8 +64,14 @@ if [ ! -f "$ROOT/shared/stage1_sample.json" ]; then python "$ROOT/build_stage1_sample.py" fi -if [ ! -f "$ROOT/shared/local_baseline.json" ]; then - echo "local_baseline.json missing — running rebaseline_local.py first (~5 min, 20 problems)..." +if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ -f "$ROOT/shared/local_baseline.json" ]; then + echo "SKIP_REBASELINE=1 set — reusing existing local_baseline.json" +elif [ "${SKIP_REBASELINE:-0}" = "1" ]; then + echo "SKIP_REBASELINE=1 set but local_baseline.json missing — running rebaseline_local.py..." + python "$ROOT/rebaseline_local.py" || \ + echo "warning: rebaseline_local.py finished with mismatches; evaluator will fall back to raw_ms for those." +else + echo "running rebaseline_local.py (~5 min, 20 problems)... set SKIP_REBASELINE=1 to skip" python "$ROOT/rebaseline_local.py" || \ echo "warning: rebaseline_local.py finished with mismatches; evaluator will fall back to raw_ms for those." fi @@ -60,13 +93,12 @@ if [ "$PHASE" -eq 4 ]; then fi cd "$ROOT/$DIR" -echo "[run_phase] phase=$PHASE dir=$DIR iters=$ITERS cwd=$(pwd)" +echo "[run_phase] phase=$PHASE dir=$DIR cwd=$(pwd) (iterations from config.yaml)" python "$RUNNER" \ initial_program.py \ "$ROOT/shared/evaluator.py" \ --config "$ROOT/config.yaml" \ - --iterations "$ITERS" \ "$@" echo "[run_phase] phase $PHASE finished." diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index a8caecd475..ab904fbc45 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -184,17 +184,28 @@ def _evaluate(program_path, problems, per_problem_timeout_s, 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). - # Each task is pinned to core (i % N) via taskset (Linux) so concurrent - # runs don't fight for the same physical core. Default 1 = sequential. + # Cores are leased from a queue.Queue so each in-flight task holds a + # unique core slot. This is correct even when len(problems) > n_parallel + # (idx % n_parallel would collide across workers). Serial mode also + # leases core 0 — symmetric with parallel so baseline / variant share + # the same pin envelope (no parallel=1-unpinned vs parallel=N-pinned + # bias). Default 1 = sequential, single slot. + import queue as _queue n_parallel = min(parallel_solvers(default=1), len(problems)) + _core_pool = _queue.Queue() + for _c in range(n_parallel): + _core_pool.put(_c) def _solve(idx_p): idx, p = idx_p smt2_path = _RAW_DIR / p["smt2"] - core = (idx % n_parallel) if n_parallel > 1 else None - r = run_z3(smt2_path, params, per_problem_timeout_s, - python_bin=_PYTHON_BIN, cpu_core=core) - return idx, p, r + core = _core_pool.get() + try: + r = run_z3(smt2_path, params, per_problem_timeout_s, + python_bin=_PYTHON_BIN, cpu_core=core) + finally: + _core_pool.put(core) + return idx, p, r, core def _is_regression(p, r): # Correctness regression: baseline gave a definitive answer (Sat/Unsat) @@ -246,9 +257,10 @@ def _regression_err(p, r): if n_parallel == 1: # Sequential: short-circuit immediately on invalid or regression. for pair in enumerate(problems): - idx, p, r = _solve(pair) + idx, p, r, core = _solve(pair) print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " - f"{r.get('result')} {r.get('elapsed_ms')}ms", flush=True) + f"{r.get('result')} {r.get('elapsed_ms')}ms " + f"(core={core})", flush=True) if "invalid_param" in r: return _invalid_err(r) if _is_regression(p, r): @@ -266,8 +278,7 @@ def _regression_err(p, r): for fut in as_completed(futures): if abort is not None: continue - idx, p, r = fut.result() - core = idx % n_parallel + idx, p, r, core = fut.result() print(f" [{stage_name}] {idx+1}/{len(problems)} {p['sha'][:10]} " f"{r.get('result')} {r.get('elapsed_ms')}ms " f"(core={core})", flush=True) diff --git a/input/z3-bench/evolve/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json index 8bb23c8843..c368df6609 100644 --- a/input/z3-bench/evolve/shared/local_baseline.json +++ b/input/z3-bench/evolve/shared/local_baseline.json @@ -1,6 +1,6 @@ { "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8": { - "elapsed_ms": 8244, + "elapsed_ms": 9658, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 13086, @@ -39,11 +39,11 @@ "max memory": 4322.01, "memory": 4078.56, "num allocs": 48404857791.0, - "time": 8.244 + "time": 9.658 } }, "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3": { - "elapsed_ms": 7990, + "elapsed_ms": 7827, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 13885, @@ -82,11 +82,11 @@ "max memory": 4321.63, "memory": 4075.61, "num allocs": 44267582854.0, - "time": 7.99 + "time": 7.826 } }, "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4": { - "elapsed_ms": 7213, + "elapsed_ms": 8160, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 14232, @@ -125,11 +125,11 @@ "max memory": 4323.22, "memory": 4077.97, "num allocs": 46757602227.0, - "time": 7.213 + "time": 8.159 } }, "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12": { - "elapsed_ms": 7317, + "elapsed_ms": 8207, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 14353, @@ -168,11 +168,11 @@ "max memory": 4323.19, "memory": 4078.87, "num allocs": 48160422899.0, - "time": 7.317 + "time": 8.206 } }, "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a": { - "elapsed_ms": 7295, + "elapsed_ms": 9681, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 14428, @@ -211,596 +211,7 @@ "max memory": 4321.57, "memory": 4075.38, "num allocs": 43841036999.0, - "time": 7.295 - } - }, - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3": { - "elapsed_ms": 44, - "result": "Unsat", - "matches_raw": true, - "raw_elapsed_ms": 221, - "stats": { - "solve-eqs-steps": 7209, - "solve-eqs-elim-vars": 3351, - "num allocs": 4155162, - "rlimit count": 897626, - "max memory": 24.4, - "memory": 21.86, - "time": 0.044 - } - }, - "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b": { - "elapsed_ms": 161, - "result": "Sat", - "matches_raw": true, - "raw_elapsed_ms": 258, - "stats": { - "conflicts": 48, - "decisions": 830, - "propagations": 50299, - "binary 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"sat.search.unsat.conflicts": 1500, + "sat.simplify.delay": 0, + "sat.subsumption": true, + "sat.subsumption.limit": 100000000, + "sat.threads": 1, + "sat.variable_decay": 110 +} diff --git a/input/z3-bench/evolve/shared/phase3_best.json b/input/z3-bench/evolve/shared/phase3_best.json new file mode 100644 index 0000000000..749479b651 --- /dev/null +++ b/input/z3-bench/evolve/shared/phase3_best.json @@ -0,0 +1,77 @@ +{ + "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.rounds": 512, + "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": 2, + "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": 2, + "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": 1000, + "smt.pb.conflict_frequency": 1000, + "smt.pb.learn_complements": true, + "smt.phase_caching_off": 100, + "smt.phase_caching_on": 400, + "smt.phase_selection": 3, + "smt.propagate_values": true, + "smt.pull_nested_quantifiers": false, + "smt.qi.cost": "(+ weight generation)", + "smt.qi.eager_threshold": 15.0, + "smt.qi.lazy_threshold": 30.0, + "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": 1, + "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 +} From 89a0f0f2a4949963477d42b75cfa7fd16eb226b8 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 11:00:53 +0900 Subject: [PATCH 05/42] [feat] size-based sample selection + adaptive per-problem timeout MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - build_samples.py replaces build_stage1_sample.py: scans raw-data, regenerates problems.jsonl, writes stage1_sample.json (5 SAT) and stage2_sample.json (50 SAT+UNSAT) using quintile-spread by (num_hard_constraints, num_variables). Runtime no longer used for selection — raw-data can accumulate beyond 50 without reshuffling. - evaluator.py: per-problem timeout = max(5s, baseline_ms * 1.3), replacing fixed OPENEVOLVE_STAGE{1,2}_TIMEOUT envs. Adaptive cap absorbs both small-problem startup overhead and huge-problem proportional headroom. _filter_stage2 added; _load_problems no longer filters by local_baseline (stage2 returns full set). - run_phase.sh / rebaseline_local.py / baseline_params.py: refs updated to build_samples.py. - README: reflect new sample layout. Co-Authored-By: Claude Opus 4.7 (1M context) --- input/z3-bench/evolve/README.md | 247 +---------- input/z3-bench/evolve/build_samples.py | 147 +++++++ input/z3-bench/evolve/build_stage1_sample.py | 86 ---- input/z3-bench/evolve/rebaseline_local.py | 2 +- input/z3-bench/evolve/run_phase.sh | 6 +- .../z3-bench/evolve/shared/baseline_params.py | 2 +- input/z3-bench/evolve/shared/evaluator.py | 81 ++-- .../z3-bench/evolve/shared/stage1_sample.json | 52 ++- .../z3-bench/evolve/shared/stage2_sample.json | 408 ++++++++++++++++++ input/z3-bench/problems.jsonl | 100 ++--- 10 files changed, 705 insertions(+), 426 deletions(-) create mode 100644 input/z3-bench/evolve/build_samples.py delete mode 100644 input/z3-bench/evolve/build_stage1_sample.py create mode 100644 input/z3-bench/evolve/shared/stage2_sample.json diff --git a/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md index ec96ccabf0..410df24889 100644 --- a/input/z3-bench/evolve/README.md +++ b/input/z3-bench/evolve/README.md @@ -1,194 +1,12 @@ -# Z3 Parameter Tuning via OpenEvolve +# Z3 Parameter Tuning via OpenEvolve — 실행 & 구조 OpenEvolve를 사용해 Z3 SMT 솔버의 파라미터를 진화적으로 탐색한다. 입력은 `input/z3-bench/` 데이터셋(50개 SMT2 인스턴스 + baseline 실행 로그). 목표는 baseline 대비 wall-clock 시간 단축, 단 정답(Sat/Unsat) 보존. ---- - -## 0. OpenEvolve 빠른 소개 (처음 보는 사람용) - -### 0.1 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. - -### 0.2 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. - -### 0.3 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. - -### 0.4 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 -``` - -### 0.5 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. - -### 0.6 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 | - -### 0.7 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) +> OpenEvolve 소개, 본 프로젝트의 목적/접근, 스코어링 공식, 설계 결정, 로드맵은 [OPENEVOLVE_INTRO.md](OPENEVOLVE_INTRO.md) 참고. --- -## 1. 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 - -## 2. 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 `extract_best.py N` 자동 호출 → `shared/phase{N}_best.json` 작성 → P{N+1}이 import. - -P4 시작 시 `prepare_phase4.py` 자동 실행 → `phase4_unified/initial_program.py`의 EVOLVE-BLOCK을 union dict literal로 머터리얼라이즈 (LLM이 diff 편집 가능하도록). - -## 3. 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` - -## 4. 디렉토리 구조 +## 1. 디렉토리 구조 ``` input/z3-bench/ @@ -197,18 +15,22 @@ input/z3-bench/ ├── problems.csv # 평탄화 버전 ├── raw-data/ # 원본 SMT2 + meta jsonl └── evolve/ - ├── README.md # 이 파일 + ├── README.md # 이 파일 (실행 & 구조) + ├── OPENEVOLVE_INTRO.md # 개념/목적/설계 ├── config.yaml # 공유 OpenEvolve config ├── run_phase.sh # 1/2/3/4 phase 실행 진입점 - ├── build_stage1_sample.py # stage1 sample 생성 스크립트 + ├── build_samples.py # stage1/stage2 sample 생성 ├── extract_best.py # phase N 종료 후 best 추출 ├── prepare_phase4.py # phase4 EVOLVE-BLOCK 머터리얼라이즈 + ├── rebaseline_local.py # 로컬에서 baseline 재측정 + ├── final_verify.py # P4 best 전수 검증 ├── shared/ │ ├── baseline_params.py # BASELINE 19키, LOCKED 4키 │ ├── score.py # geomean × solved_rate^2 │ ├── z3_runner.py # subprocess z3 CLI 호출 │ ├── evaluator.py # cascade stage1/stage2 │ ├── stage1_sample.json # 5문제 stratified sample (seed=42) + │ ├── stage2_sample.json # stage2 problem set │ └── phase{1,2,3}_best.json # 각 phase 종료 후 생성됨 ├── phase1_opt_sls/ │ └── initial_program.py # EVOLVE-BLOCK: OPT_SLS_OVERRIDES (~34키) @@ -220,7 +42,7 @@ input/z3-bench/ └── initial_program.py # EVOLVE-BLOCK: UNIFIED_OVERRIDES (union) ``` -## 5. 평가 흐름 (cascade) +## 2. 평가 흐름 (cascade) ``` LLM 변이된 initial_program.py @@ -250,7 +72,7 @@ ac90ca97ff99 239 ms Unsat (fast) 5분위 버킷에서 하나씩 → Sat/Unsat + 빠름/느림 골고루. -## 6. Initial program 표준 형태 +## 3. Initial program 표준 형태 각 phase의 `initial_program.py`는 동일 패턴: @@ -287,7 +109,7 @@ def get_phase_overrides(): evaluator는 phase를 모름 — `get_params()` 결과만 받음. `extract_best.py`는 `get_phase_overrides()`만 호출 → phase별 dict 분리 유지. -## 7. 실행 절차 (Docker) +## 4. 실행 절차 (Docker) ### Host에서 @@ -351,7 +173,7 @@ apt-get install -y z3 # 또는: pip install z3-solver (CLI 동반) export OPENAI_API_KEY="..." # 셸 안에서도 export 필요 # (이미 생성됨, 재생성 원할 때만) -python input/z3-bench/evolve/build_stage1_sample.py +python input/z3-bench/evolve/build_samples.py # 순차 실행 — 각 phase 종료 시 extract_best.py 자동 호출 ./input/z3-bench/evolve/run_phase.sh 1 @@ -383,7 +205,7 @@ nohup ./input/z3-bench/evolve/run_phase.sh 1 \ &> /app/logs/phase1.log & ``` -## 8. 환경 변수 +## 5. 환경 변수 | 변수 | 기본 | 용도 | |---|---|---| @@ -393,47 +215,8 @@ nohup ./input/z3-bench/evolve/run_phase.sh 1 \ | `OPENEVOLVE_STAGE2_TIMEOUT` | 120 | stage2 문제당 초 | | `OPENEVOLVE_Z3_BIN` | `z3` | z3 바이너리 경로 | -## 9. 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 | - -## 10. 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) - -## 11. 도커 안에서 추가 검증 필요 +## 6. 도커 안에서 추가 검증 필요 - `z3 -pmd | less` 출력으로 4.13.3.0의 실제 키 검증 (일부 키명/타입이 마이너 버전마다 다를 수 있음) - baseline 변이로 stage1 1회 평가 직접 호출 → z3 binary 동작/타임아웃 검증 - LLM API 호출 sanity check (`config.yaml`의 api_base + 키 매칭) - -## 12. 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↑ - -## 13. 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/build_samples.py b/input/z3-bench/evolve/build_samples.py new file mode 100644 index 0000000000..ef0f66e88f --- /dev/null +++ b/input/z3-bench/evolve/build_samples.py @@ -0,0 +1,147 @@ +""" +Build problems.jsonl + stage1_sample.json + stage2_sample.json from raw-data. + +Source of truth = raw-data/*.meta.jsonl. raw-data accumulates over time; +this script (re)scans the directory each run, rewrites problems.jsonl as the +full aggregate, and re-selects stage1 / stage2 samples. + +Size metric: (num_hard_constraints, num_variables) tuple ascending — primary +then tiebreak. Runtime (elapsed_ms) is NOT used for selection. + +Stage1 (5): SAT-only, quintile-spread by size (5 buckets, 1 per bucket). +Stage2 (50): SAT + UNSAT, quintile-spread by size (5 buckets, 10 per bucket + rank-linspace within bucket). + +Quintile-spread = stable, deterministic ordering by rank then equal-density +sampling per bucket. Guarantees representation across the size distribution +rather than clustering on fastest / smallest problems. +""" +import json +import pathlib + +_HERE = pathlib.Path(__file__).resolve().parent +_BENCH = _HERE.parent +_RAW = _BENCH / "raw-data" +_PROBLEMS = _BENCH / "problems.jsonl" +_STAGE1 = _HERE / "shared" / "stage1_sample.json" +_STAGE2 = _HERE / "shared" / "stage2_sample.json" + +STAGE1_N = 5 +STAGE2_N = 50 +N_BUCKETS = 5 + + +def _scan_raw(): + rows = [] + for path in sorted(_RAW.glob("*.meta.jsonl")): + with open(path) as f: + for line in f: + line = line.strip() + if not line: + continue + rows.append(json.loads(line)) + break + return rows + + +def _size_key(d): + feats = d.get("features") or {} + return (feats.get("num_hard_constraints", 0), feats.get("num_variables", 0)) + + +def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): + total = len(sorted_rows) + if total == 0 or n_pick <= 0: + return [] + if total <= n_pick: + return list(sorted_rows) + + per_bucket = n_pick // n_buckets + remainder = n_pick % n_buckets + picked = [] + for b in range(n_buckets): + lo = (b * total) // n_buckets + hi = ((b + 1) * total) // n_buckets + bucket = sorted_rows[lo:hi] + if not bucket: + continue + k = per_bucket + (1 if b < remainder else 0) + if k <= 0: + continue + if k == 1: + picked.append(bucket[len(bucket) // 2]) + else: + for j in range(k): + idx = round(j * (len(bucket) - 1) / (k - 1)) + picked.append(bucket[idx]) + return picked + + +def _summary(d): + f = d.get("features") or {} + z = d.get("z3_status") or {} + return { + "sha": d["problem_sha256"][:12], + "num_hard_constraints": f.get("num_hard_constraints", 0), + "num_variables": f.get("num_variables", 0), + "baseline_result": z.get("result"), + "baseline_ms": z.get("elapsed_ms"), + } + + +def _write_sample(path, picks, label, criteria): + path.write_text( + json.dumps( + { + "selection": f"{len(picks)} {criteria}, quintile-spread by " + "(num_hard_constraints, num_variables)", + "source": str(_PROBLEMS.relative_to(_BENCH.parent)), + "sha256": [d["problem_sha256"] for d in picks], + "summary": [_summary(d) for d in picks], + }, + indent=2, + ) + + "\n" + ) + print(f"wrote {path.relative_to(_BENCH.parent)} ({len(picks)} {label})") + + +def main(): + rows = _scan_raw() + if not rows: + raise SystemExit(f"no *.meta.jsonl files found under {_RAW}") + print(f"scanned {len(rows)} raw meta files") + + with open(_PROBLEMS, "w") as f: + for d in rows: + f.write(json.dumps(d) + "\n") + print(f"wrote {_PROBLEMS.relative_to(_BENCH.parent)} ({len(rows)} entries)") + + sat = sorted( + (d for d in rows if (d.get("z3_status") or {}).get("result") == "Sat"), + key=_size_key, + ) + all_sorted = sorted(rows, key=_size_key) + + s1 = _quintile_spread(sat, STAGE1_N, N_BUCKETS) + s2 = _quintile_spread(all_sorted, STAGE2_N, N_BUCKETS) + + _write_sample(_STAGE1, s1, "stage1", "SAT-only") + _write_sample(_STAGE2, s2, "stage2", "SAT+UNSAT") + + for label, picks in (("stage1", s1), ("stage2", s2)): + print(f"\n{label}:") + for d in picks: + f_ = d.get("features") or {} + z = d.get("z3_status") or {} + print( + f" {d['problem_sha256'][:12]} " + f"hc={f_.get('num_hard_constraints', 0):>7} " + f"vars={f_.get('num_variables', 0):>7} " + f"{z.get('result', '?'):<7} " + f"{z.get('elapsed_ms', 0):>6}ms" + ) + + +if __name__ == "__main__": + main() diff --git a/input/z3-bench/evolve/build_stage1_sample.py b/input/z3-bench/evolve/build_stage1_sample.py deleted file mode 100644 index ba6e421b80..0000000000 --- a/input/z3-bench/evolve/build_stage1_sample.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -Generate stage1_sample.json: 5 fastest Sat problems (fallback: fastest Unsat -to fill remainder). - -Rationale: stage1 has tight per-problem timeout (15s default). Including slow -baseline problems (>15s) guarantees timeout regardless of variant quality, -making stage1 score uninformative. Sat is preferred since LLM is more likely -to preserve Sat than Unsat under aggressive param changes (Unsat proofs depend -on completeness of preprocessing pipeline). - -No randomness — deterministic pick of fastest N by result class. -""" -import json -import pathlib - -ROOT = pathlib.Path(__file__).resolve().parent -PROBLEMS = ROOT.parent / "problems.jsonl" -OUT = ROOT / "shared" / "stage1_sample.json" - -NUM_PROBLEMS = 5 -# Stage1 wall-clock budget: 60-120s per variant. -# - good variant (baseline-like): 5 × ~12s = 60s lower bound -# - bad variant (all timeout): 5 × 24s = 120s upper bound -# Pick problems whose baseline elapsed_ms falls in [MIN_MS, MAX_MS]. -# Per-problem timeout defaults to 24s in evaluator.py. -MIN_MS = 4000 -MAX_MS = 15000 - - -def main(): - rows = [] - with open(PROBLEMS) as f: - for line in f: - d = json.loads(line) - rows.append( - ( - d["problem_sha256"], - d["z3_status"]["elapsed_ms"], - d["z3_status"]["result"], - ) - ) - rows.sort(key=lambda r: r[1]) # ascending by elapsed_ms - - in_range = [r for r in rows if MIN_MS <= r[1] <= MAX_MS] - sat_band = [r for r in in_range if r[2] == "Sat"] - unsat_band = [r for r in in_range if r[2] == "Unsat"] - - picked = sat_band[:NUM_PROBLEMS] - if len(picked) < NUM_PROBLEMS: - picked.extend(unsat_band[: NUM_PROBLEMS - len(picked)]) - if len(picked) < NUM_PROBLEMS: - # fallback: widen below MIN_MS toward 0, prefer Sat - below = [r for r in rows if r[1] < MIN_MS] - sat_below = sorted((r for r in below if r[2] == "Sat"), key=lambda r: -r[1]) - picked.extend(sat_below[: NUM_PROBLEMS - len(picked)]) - if len(picked) < NUM_PROBLEMS: - remaining = [r for r in rows if r not in picked] - picked.extend(remaining[: NUM_PROBLEMS - len(picked)]) - - OUT.parent.mkdir(parents=True, exist_ok=True) - OUT.write_text( - json.dumps( - { - "selection": f"{NUM_PROBLEMS} Sat-preferred with baseline_ms in [{MIN_MS}, {MAX_MS}] (Unsat / sub-MIN_MS fallbacks)", - "source": str(PROBLEMS.relative_to(ROOT.parent.parent)), - "sha256": [r[0] for r in picked], - "summary": [ - { - "sha": r[0][:12], - "baseline_ms": r[1], - "baseline_result": r[2], - } - for r in picked - ], - }, - indent=2, - ) - + "\n" - ) - print(f"wrote {OUT.relative_to(ROOT.parent.parent)} ({len(picked)} problems)") - for s in picked: - print(f" {s[0][:12]} {s[1]:>6} ms {s[2]}") - - -if __name__ == "__main__": - main() diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index 7a555a7bcb..b8c69c56d1 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -51,7 +51,7 @@ def _load_problem_index(): def _load_stage1_shas(): if not _STAGE1_SAMPLE.exists(): - print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_stage1_sample.py first", + print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", file=sys.stderr) sys.exit(2) return list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) diff --git a/input/z3-bench/evolve/run_phase.sh b/input/z3-bench/evolve/run_phase.sh index 48dc7a2e4a..681e2abb36 100755 --- a/input/z3-bench/evolve/run_phase.sh +++ b/input/z3-bench/evolve/run_phase.sh @@ -59,9 +59,9 @@ if [ ! -f "$RUNNER" ]; then exit 1 fi -if [ ! -f "$ROOT/shared/stage1_sample.json" ]; then - echo "stage1_sample.json missing — running build_stage1_sample.py first..." - python "$ROOT/build_stage1_sample.py" +if [ ! -f "$ROOT/shared/stage1_sample.json" ] || [ ! -f "$ROOT/shared/stage2_sample.json" ]; then + echo "sample json missing — running build_samples.py first..." + python "$ROOT/build_samples.py" fi if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ -f "$ROOT/shared/local_baseline.json" ]; then diff --git a/input/z3-bench/evolve/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py index a9c42e628e..1b45d966c8 100644 --- a/input/z3-bench/evolve/shared/baseline_params.py +++ b/input/z3-bench/evolve/shared/baseline_params.py @@ -70,7 +70,7 @@ def _self_test(): stage1_sample = here / "stage1_sample.json" if not stage1_sample.exists(): - print(f"ERROR: {stage1_sample} missing. run build_stage1_sample.py first.", + print(f"ERROR: {stage1_sample} missing. run build_samples.py first.", file=sys.stderr) return 2 diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index ab904fbc45..f3aa1208ce 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -1,9 +1,15 @@ """ OpenEvolve evaluator for Z3 parameter tuning. -Cascade: - stage1: 5 stratified problems (stage1_sample.json), per-problem 15s - stage2: full 50 problems, per-problem 120s +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} @@ -17,8 +23,6 @@ Environment overrides: OPENEVOLVE_MAX_PROBLEMS int — cap stage2 problem count - OPENEVOLVE_STAGE1_TIMEOUT int sec — per-problem timeout in stage1 (default 24) - OPENEVOLVE_STAGE2_TIMEOUT int sec — per-problem timeout in stage2 (default 120) OPENEVOLVE_PARALLEL_SOLVERS int — SINGLE knob for total z3 concurrency (default 1). Spawns N z3 worker subprocesses concurrently per stage; each pinned to a @@ -31,11 +35,15 @@ """ import importlib.util import json +import math import os import pathlib import sys import traceback +TIMEOUT_FACTOR = 1.3 +MIN_TIMEOUT_S = 5 + _HERE = pathlib.Path(__file__).resolve().parent sys.path.insert(0, str(_HERE)) @@ -50,6 +58,7 @@ _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _STAGE1_SAMPLE = _HERE / "stage1_sample.json" +_STAGE2_SAMPLE = _HERE / "stage2_sample.json" _LOCAL_BASELINE = _HERE / "local_baseline.json" _PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") # None -> sys.executable @@ -64,14 +73,12 @@ def _load_program(path): def _load_problems(): - # If rebaseline_local.py has been run, restrict stage2 to that SHA subset - # (20 stratified). Reasons: - # - non-rebaselined problems would use raw_ms recorded on a different - # machine, skewing speedup - # - smaller stage2 = faster iterations - # Local elapsed_ms used only when local result matches raw, else fall - # back to raw_ms for that SHA to avoid speedup distortion from a bad - # local run (timeout, mismatch). + # 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()) @@ -81,8 +88,6 @@ def _load_problems(): for line in f: d = json.loads(line) sha = d["problem_sha256"] - if local and sha not in local: - continue baseline_ms = d["z3_status"]["elapsed_ms"] baseline_result = d["z3_status"]["result"] baseline_stats = {} @@ -109,6 +114,17 @@ def _filter_stage1(problems): return [p for p in problems if p["sha"] in keep] +def _filter_stage2(problems): + # stage2_sample.json pins a deterministic 50-problem subset built by + # build_samples.py (quintile-spread by num_hard_constraints, num_variables). + # raw-data may exceed 50; without this filter, stage2 cost grows + # unbounded as raw-data accumulates. + 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 _err_result(metrics_extra, artifacts): metrics = { "combined_score": 0.0, @@ -122,7 +138,7 @@ def _err_result(metrics_extra, artifacts): return EvaluationResult(metrics=metrics, artifacts=artifacts) -def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): +def _evaluate(program_path, problems, stage_name): try: program = _load_program(program_path) except Exception as e: @@ -199,13 +215,14 @@ def _evaluate(program_path, problems, per_problem_timeout_s, stage_name): 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, per_problem_timeout_s, + 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 + return idx, p, r, core, timeout_s def _is_regression(p, r): # Correctness regression: baseline gave a definitive answer (Sat/Unsat) @@ -257,9 +274,9 @@ def _regression_err(p, r): if n_parallel == 1: # Sequential: short-circuit immediately on invalid or regression. for pair in enumerate(problems): - idx, p, r, core = _solve(pair) + 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 " + 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) @@ -270,17 +287,18 @@ def _regression_err(p, r): by_idx[idx] = (p, r) else: # Parallel: cancel pending futures on first failure. In-flight tasks - # keep running until subprocess timeout (with __exit__ waits for them); - # acceptable for stage1 (timeout ~24s). + # keep running until subprocess timeout (with __exit__ waits for them). + # Per-problem timeout is now 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 with ThreadPoolExecutor(max_workers=n_parallel) as ex: futures = [ex.submit(_solve, pair) for pair in enumerate(problems)] for fut in as_completed(futures): if abort is not None: continue - idx, p, r, core = fut.result() + 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 " + 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) @@ -353,21 +371,20 @@ def _regression_err(p, r): def evaluate_stage1(program_path): - # Default 24s per problem → 5 problems = 60-120s wall-clock budget - # (60s lower bound on baseline-like variants, 120s upper bound on all-timeout). - timeout = int(os.environ.get("OPENEVOLVE_STAGE1_TIMEOUT", "24")) + # 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, timeout, "stage1") + return _evaluate(program_path, problems, "stage1") def evaluate_stage2(program_path): - timeout = int(os.environ.get("OPENEVOLVE_STAGE2_TIMEOUT", "120")) - problems = _load_problems() - return _evaluate(program_path, problems, timeout, "stage2") + problems = _filter_stage2(_load_problems()) + return _evaluate(program_path, problems, "stage2") def evaluate(program_path): # Evolution uses stage1 (5 sampled problems) only — fast iteration loop. - # Stage2 (20 problems) is reserved for final verification via + # 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/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json index a31bb6bfb3..82cd674969 100644 --- a/input/z3-bench/evolve/shared/stage1_sample.json +++ b/input/z3-bench/evolve/shared/stage1_sample.json @@ -1,38 +1,48 @@ { - "selection": "5 Sat-preferred with baseline_ms in [4000, 15000] (Unsat / sub-MIN_MS fallbacks)", + "selection": "5 SAT-only, quintile-spread by (num_hard_constraints, num_variables)", "source": "z3-bench/problems.jsonl", "sha256": [ - "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", - "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", - "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12", - "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a" + "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", + "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", + "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", + "6aab51935a092a1d7f05bb431d3d05e0de50c19f5f983026a6d14e7c4b9cf240", + "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" ], "summary": [ { - "sha": "75ee534dbc92", - "baseline_ms": 13086, - "baseline_result": "Sat" + "sha": "505a2e36055c", + "num_hard_constraints": 24937, + "num_variables": 5752, + "baseline_result": "Sat", + "baseline_ms": 307 }, { - "sha": "069af7891076", - "baseline_ms": 13885, - "baseline_result": "Sat" + "sha": "4a02d7846394", + "num_hard_constraints": 105784, + "num_variables": 32912, + "baseline_result": "Sat", + "baseline_ms": 14428 }, { - "sha": "318bc251e0bd", - "baseline_ms": 14232, - "baseline_result": "Sat" + "sha": "8a304b9c461a", + "num_hard_constraints": 112796, + "num_variables": 17043, + "baseline_result": "Sat", + "baseline_ms": 3053 }, { - "sha": "493242c37992", - "baseline_ms": 14353, - "baseline_result": "Sat" + "sha": "6aab51935a09", + "num_hard_constraints": 113172, + "num_variables": 17151, + "baseline_result": "Sat", + "baseline_ms": 3681 }, { - "sha": "4a02d7846394", - "baseline_ms": 14428, - "baseline_result": "Sat" + "sha": "a7df812c620d", + "num_hard_constraints": 696365, + "num_variables": 83551, + "baseline_result": "Sat", + "baseline_ms": 93445 } ] } diff --git a/input/z3-bench/evolve/shared/stage2_sample.json b/input/z3-bench/evolve/shared/stage2_sample.json new file mode 100644 index 0000000000..d5dd3fe11c --- /dev/null +++ b/input/z3-bench/evolve/shared/stage2_sample.json @@ -0,0 +1,408 @@ +{ + "selection": "50 SAT+UNSAT, quintile-spread by (num_hard_constraints, num_variables)", + "source": "z3-bench/problems.jsonl", + "sha256": [ + 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propagations": 45, "pb predicates": 260, "arith eq adapter": 357, "arith-lower": 2136, "arith-upper": 2122, "arith-fixed-eqs": 309, "arith-conflicts": 5, "arith-bound-propagations-lp": 483, "arith-diseq": 592, "arith-make-feasible": 416, "arith-max-columns": 309, "arith-max-rows": 191, "arith-offset-eqs": 291, "solve-eqs-steps": 13111, "solve-eqs-elim-vars": 4574, "num allocs": 863687625, "rlimit count": 1730326, "max memory": 88.65, "memory": 86.87, "time": 0.258}, "problem_sha256": "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", "applied_params_hash": "543b29ed8f75ba2d0c54a5409cce4ee23f2d54d221a09c0cca667e1b31ffcec6", "smt2_filename": "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b.smt2"} From 8eb20fd1e3f854566cbc3268f0d73597cded3f46 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 11:07:07 +0900 Subject: [PATCH 06/42] [fix] update utils --- docker-run.sh | 20 +- input/z3-bench/evolve/OPENEVOLVE_INTRO.md | 228 ++++++++++++++++++++++ input/z3-bench/evolve/README.md | 129 +++++++----- input/z3-bench/evolve/build_samples.py | 18 +- scripts/docker-init-claude.sh | 73 +++++++ 5 files changed, 411 insertions(+), 57 deletions(-) create mode 100644 input/z3-bench/evolve/OPENEVOLVE_INTRO.md create mode 100755 scripts/docker-init-claude.sh diff --git a/docker-run.sh b/docker-run.sh index 6b428aafe8..e3f2a6db99 100755 --- a/docker-run.sh +++ b/docker-run.sh @@ -347,6 +347,24 @@ else 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 $INIT_SCRIPT_REL || true; fi; exec bash" + ) +fi +DOCKER_RUN_OPTS+=("-e" "AUTO_INSTALL_CLAUDE=${AUTO_INSTALL_CLAUDE:-1}") + echo "" echo -e "${BLUE}Run Configuration:${NC}" echo -e " Environment: $ENV_TYPE" @@ -375,7 +393,7 @@ fi echo "" # Run Docker container -if docker run "${DOCKER_RUN_OPTS[@]}" "$LOCAL_IMAGE"; then +if docker run "${DOCKER_RUN_OPTS[@]}" "$LOCAL_IMAGE" "${CONTAINER_CMD[@]}"; then echo "" if [ "$DETACHED_MODE" = true ]; then echo -e "${GREEN}================================================${NC}" diff --git a/input/z3-bench/evolve/OPENEVOLVE_INTRO.md b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md new file mode 100644 index 0000000000..6473a64d53 --- /dev/null +++ b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md @@ -0,0 +1,228 @@ +# 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 `extract_best.py N` 자동 호출 → `shared/phase{N}_best.json` 작성 → P{N+1}이 import. + +P4 시작 시 `prepare_phase4.py` 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 index 410df24889..5490c3f6cf 100644 --- a/input/z3-bench/evolve/README.md +++ b/input/z3-bench/evolve/README.md @@ -111,78 +111,86 @@ evaluator는 phase를 모름 — `get_params()` 결과만 받음. `extract_best. ## 4. 실행 절차 (Docker) -### Host에서 +### 4.1 백엔드 선택 + +| 항목 | OpenAI 호환 (기본) | Claude Code | +|---|---|---| +| 인증 | env var `OPENAI_API_KEY` (Gemini/OpenAI/local 등) | env var `CLAUDE_CODE_OAUTH_TOKEN` (host에서 `claude setup-token`) | +| config.yaml model | `provider` 없음 또는 `openai` | `provider: claude_code` | +| Config 예시 | `configs/default_config.yaml` | `configs/claude_code_example.yaml` | +| Rate limit | 키 발급사 정책 | Pro/Max 5h window — 빠르게 막힘 | +| 재현성 | `temperature`/`seed` 적용 | SDK 미지원, 약함 | +| Docker 자동 셋업 | skip 권장 (`AUTO_INSTALL_CLAUDE=0`) | docker-init-claude.sh가 자동 실행 | + +### 4.2 Quickstart — Claude Code 백엔드 ```bash -export OPENAI_API_KEY="..." # config.yaml의 api_base에 맞는 키 - # (현재 gemini-2.5-flash → Google AI Studio key) -./docker-run.sh dev -s z3evo # interactive shell -``` +# === Host에서 (1회만) === +claude setup-token # 출력 토큰 복사 (Keychain 우회용 long-lived OAuth) +export CLAUDE_CODE_OAUTH_TOKEN="sk-..." # ~/.zshrc 등 영구화 권장 -`OPENAI_API_KEY`라는 이름은 OpenEvolve가 OpenAI 호환 SDK를 쓰기 때문. 실제 라우팅은 `config.yaml`의 `api_base`가 결정. +./docker-run.sh dev -s z3evo # 진입 — 첫 실행: claude CLI + SDK 자동 설치 (수 분) + # 두 번째부터: 즉시 셸 (영속 mount로 skip) +``` -### Claude Code 백엔드 사용 시 +```bash +# === Container 안 (1회만) === +pip install -e ".[dev]" # OpenEvolve 본체 +apt-get install -y z3 # 또는: pip install z3-solver -`config.yaml`에서 `provider: claude_code`로 모델을 정의하면 (`configs/claude_code_example.yaml` 참고) API 키 대신 Claude Code 구독 인증을 쓸 수 있다. Docker 안에서 쓰려면: +python -c "from openevolve.llm.claude_code import ClaudeCodeLLM; print('ok')" # sanity check -1. **Host에서 (1회만)**: long-lived OAuth 토큰 생성 - ```bash - claude setup-token # 출력된 토큰 복사 - export CLAUDE_CODE_OAUTH_TOKEN="sk-..." - ``` - macOS는 OAuth credential을 Keychain에 저장하므로 `~/.claude/` 마운트만으로는 인증 안 됨. 토큰 방식이 필수. +# 데이터 sample (이미 생성됨; 재생성 원할 때만) +python input/z3-bench/evolve/build_samples.py -2. **docker-run.sh 실행**: 위 env var가 export 되어 있으면 자동 전달 + `~/.claude/` 마운트 (settings/sessions 공유). - ```bash - ./docker-run.sh dev -s z3evo - ``` +# Phase 순차 실행 — 각 phase 종료 시 extract_best.py 자동 호출 +./input/z3-bench/evolve/run_phase.sh 1 +./input/z3-bench/evolve/run_phase.sh 2 +./input/z3-bench/evolve/run_phase.sh 3 +./input/z3-bench/evolve/run_phase.sh 4 +``` -3. **Container 안 (1회만)**: `claude` CLI 설치. axion 이미지에는 Node.js/npm 없음 → Anthropic 공식 standalone installer 사용 (Node 번들, 시스템 의존성 없음). - ```bash - curl -fsSL https://claude.ai/install.sh | bash - # 설치 위치: ~/.local/bin/claude - export PATH="$HOME/.local/bin:$PATH" # ~/.bashrc에 영구 추가 권장 - claude --version # sanity check - pip install -e ".[claude-code]" # claude-agent-sdk - ``` - SDK 탐색 순서: `~/.npm-global/bin/claude` → `/usr/local/bin/claude` → `~/.local/bin/claude` → `~/.claude/local/claude` → PATH. 위 경로 그대로 작동. +### 4.3 Quickstart — OpenAI 호환 백엔드 - **설치 영속화**: 컨테이너는 `--rm`이라 종료 시 사라지지만 docker-run.sh가 `~/.axion-docker-persist/claude-local/`을 `/root/.local`로 마운트 → 한 번 설치하면 다음 컨테이너에서도 그대로 사용 가능. +```bash +# === Host에서 === +export OPENAI_API_KEY="..." # config.yaml의 api_base에 맞는 키 +AUTO_INSTALL_CLAUDE=0 ./docker-run.sh dev -s z3evo # claude 셋업 skip - **host 차이**: - - Linux host: docker-run.sh가 host `claude` 바이너리도 자동 마운트 (`/usr/local/bin/claude` ro) → installer 생략 가능. - - Mac host: cross-OS 불가 → 위 installer 필수. +# === Container 안 === +pip install -e ".[dev]" +apt-get install -y z3 +export OPENAI_API_KEY="..." # 셸 안에서도 필요 (-e로 전달됨) +./input/z3-bench/evolve/run_phase.sh 1 +``` - **HOME 분리**: `~/.claude/`가 host에서 마운트되므로 host config 공유됨. 충돌 우려 시 컨테이너 안에서 `CLAUDE_CONFIG_DIR` 등 별도 path 지정. +`OPENAI_API_KEY`라는 이름은 OpenEvolve가 OpenAI 호환 SDK를 쓰기 때문. 실제 라우팅은 `config.yaml`의 `api_base`가 결정 (Gemini, OpenAI, vLLM 등). -4. **체크**: 인증 작동 여부 - ```bash - python -c "from openevolve.llm.claude_code import ClaudeCodeLLM; print('ok')" - ``` +### 4.4 docker-run.sh의 Claude Code 셋업 동작 -**주의**: Claude Pro/Max 구독은 5시간 윈도우 rate limit 있음. 큰 evolution run은 빠르게 막힘. 작은 iteration으로 검증 먼저. +docker-run.sh가 [scripts/docker-init-claude.sh](../../../scripts/docker-init-claude.sh)를 컨테이너 startup 명령으로 실행. **멱등** (이미 설치돼 있으면 skip). -### Container 안 +| 단계 | 동작 | Skip 조건 | +|---|---|---| +| 1 | `~/.local/bin`을 PATH 추가 + `~/.bashrc` 영구화 | grep로 중복 차단 | +| 2 | `claude` CLI 설치 (`curl -fsSL https://claude.ai/install.sh \| bash`) | `command -v claude` 성공 | +| 3 | `pip install -e ".[claude-code]"` (= claude-agent-sdk) | `import claude_agent_sdk` 성공 | +| 4 | Auth env var 체크 → 없으면 경고 | — | -```bash -cd $SCRIPT_DIR # rootless: 호스트 경로 그대로 / root: /app +**영속화**: `--rm` 컨테이너지만 `~/.axion-docker-persist/claude-local/` → `/root/.local` 마운트로 설치 결과 호스트에 남음. 다음 컨테이너 즉시 사용. -# 1회 셋업 -pip install -e ".[dev]" -apt-get install -y z3 # 또는: pip install z3-solver (CLI 동반) -export OPENAI_API_KEY="..." # 셸 안에서도 export 필요 +**자동 셋업 끄기**: `AUTO_INSTALL_CLAUDE=0 ./docker-run.sh ...` -# (이미 생성됨, 재생성 원할 때만) -python input/z3-bench/evolve/build_samples.py +**Host OS별 차이**: +- Linux host: host의 `claude` 바이너리도 read-only 자동 마운트 (`/usr/local/bin/claude`) → init script가 installer skip. +- Mac host: cross-OS 불가 → init script가 standalone installer 실행 (Node 번들 포함). -# 순차 실행 — 각 phase 종료 시 extract_best.py 자동 호출 -./input/z3-bench/evolve/run_phase.sh 1 -./input/z3-bench/evolve/run_phase.sh 2 -./input/z3-bench/evolve/run_phase.sh 3 -./input/z3-bench/evolve/run_phase.sh 4 -``` +**Mount 요약** (root mode 기준): +- `$HOME/.claude/` → `/root/.claude` (settings/sessions/projects 공유) +- `~/.axion-docker-persist/claude-local/` → `/root/.local` (claude 바이너리 영속) +- env forward: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `ANTHROPIC_AUTH_TOKEN`, `ANTHROPIC_BASE_URL`, `CLAUDE_CODE_OAUTH_TOKEN`, `CLAUDE_CODE_USE_BEDROCK`, `CLAUDE_CODE_USE_VERTEX` -### 체크포인트 재개 +### 4.5 체크포인트 재개 OpenEvolve가 `phase{N}_*/openevolve_output/checkpoints/checkpoint_K/`에 자동 저장. 재개: @@ -196,7 +204,7 @@ python /app/openevolve-run.py \ --iterations 100 ``` -### Detached 장시간 실행 +### 4.6 Detached 장시간 실행 ```bash ./docker-run.sh dev -s z3evo -d @@ -205,6 +213,19 @@ nohup ./input/z3-bench/evolve/run_phase.sh 1 \ &> /app/logs/phase1.log & ``` +Detached mode에서도 init script가 background로 한 번 실행됨. `docker logs $CONTAINER_NAME`로 init 출력 확인. + +### 4.7 트러블슈팅 + +| 증상 | 원인 / 해결 | +|---|---| +| 진입 시 `[init-claude] no CLAUDE_CODE_OAUTH_TOKEN ...` 경고 | host에서 `claude setup-token` → token export → 재실행 | +| `claude --version` 안 됨 | `source ~/.bashrc` 또는 `export PATH="$HOME/.local/bin:$PATH"` | +| init이 매번 재설치 | `~/.axion-docker-persist/claude-local/` 마운트 누락 (rootless면 `$HOME` bind에 포함되어 있어야 함). `ls ~/.local/bin/claude` 확인 | +| `pip install` 실패 | cwd 확인 (`pyproject.toml` 있는지) — rootless면 `cd $SCRIPT_DIR` 먼저 | +| Rate limit | Pro/Max 5h window 소진. `OPENEVOLVE_MAX_PROBLEMS` 축소 또는 OpenAI 호환 백엔드 병행 | +| Mac에서 host claude 마운트 시도 | Mac 바이너리는 Linux 컨테이너에서 안 돎 → docker-run.sh가 자동 skip하고 installer로 fallback | + ## 5. 환경 변수 | 변수 | 기본 | 용도 | diff --git a/input/z3-bench/evolve/build_samples.py b/input/z3-bench/evolve/build_samples.py index ef0f66e88f..cddc43bab2 100644 --- a/input/z3-bench/evolve/build_samples.py +++ b/input/z3-bench/evolve/build_samples.py @@ -8,6 +8,11 @@ Size metric: (num_hard_constraints, num_variables) tuple ascending — primary then tiebreak. Runtime (elapsed_ms) is NOT used for selection. +Sample pool cap: baseline_ms <= MAX_BASELINE_MS (5 min). Excludes monster +problems where even baseline times out under reasonable per-iteration +budgets. problems.jsonl still contains the full set; only sample selection +applies the cap. + Stage1 (5): SAT-only, quintile-spread by size (5 buckets, 1 per bucket). Stage2 (50): SAT + UNSAT, quintile-spread by size (5 buckets, 10 per bucket rank-linspace within bucket). @@ -29,6 +34,7 @@ STAGE1_N = 5 STAGE2_N = 50 N_BUCKETS = 5 +MAX_BASELINE_MS = 300_000 # 5 min cap — exclude monster problems from sample pool def _scan_raw(): @@ -117,11 +123,19 @@ def main(): f.write(json.dumps(d) + "\n") print(f"wrote {_PROBLEMS.relative_to(_BENCH.parent)} ({len(rows)} entries)") + candidates = [ + d for d in rows + if (d.get("z3_status") or {}).get("elapsed_ms", 0) <= MAX_BASELINE_MS + ] + skipped = len(rows) - len(candidates) + print(f"sample pool: {len(candidates)} (skipped {skipped} with " + f"baseline_ms > {MAX_BASELINE_MS}ms)") + sat = sorted( - (d for d in rows if (d.get("z3_status") or {}).get("result") == "Sat"), + (d for d in candidates if (d.get("z3_status") or {}).get("result") == "Sat"), key=_size_key, ) - all_sorted = sorted(rows, key=_size_key) + all_sorted = sorted(candidates, key=_size_key) s1 = _quintile_spread(sat, STAGE1_N, N_BUCKETS) s2 = _quintile_spread(all_sorted, STAGE2_N, N_BUCKETS) diff --git a/scripts/docker-init-claude.sh b/scripts/docker-init-claude.sh new file mode 100755 index 0000000000..9fa871517e --- /dev/null +++ b/scripts/docker-init-claude.sh @@ -0,0 +1,73 @@ +#!/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 + +# 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 From ceb1150508517007048a14a4d9ee4810e5d4e8de Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 11:33:44 +0900 Subject: [PATCH 07/42] update samples --- input/z3-bench/evolve/build_samples.py | 75 ++-- input/z3-bench/evolve/config.yaml | 9 +- input/z3-bench/evolve/rebaseline_local.py | 37 +- input/z3-bench/evolve/shared/evaluator.py | 35 +- .../evolve/shared/local_baseline.json | 378 ++++++++-------- .../z3-bench/evolve/shared/stage1_sample.json | 48 +-- .../z3-bench/evolve/shared/stage2_sample.json | 370 +--------------- .../z3-bench/evolve/shared/stage3_sample.json | 408 ++++++++++++++++++ 8 files changed, 733 insertions(+), 627 deletions(-) create mode 100644 input/z3-bench/evolve/shared/stage3_sample.json diff --git a/input/z3-bench/evolve/build_samples.py b/input/z3-bench/evolve/build_samples.py index cddc43bab2..a41cfc527c 100644 --- a/input/z3-bench/evolve/build_samples.py +++ b/input/z3-bench/evolve/build_samples.py @@ -1,25 +1,27 @@ """ -Build problems.jsonl + stage1_sample.json + stage2_sample.json from raw-data. +Build problems.jsonl + stage1/2/3 sample files from raw-data. Source of truth = raw-data/*.meta.jsonl. raw-data accumulates over time; -this script (re)scans the directory each run, rewrites problems.jsonl as the -full aggregate, and re-selects stage1 / stage2 samples. - -Size metric: (num_hard_constraints, num_variables) tuple ascending — primary -then tiebreak. Runtime (elapsed_ms) is NOT used for selection. - -Sample pool cap: baseline_ms <= MAX_BASELINE_MS (5 min). Excludes monster -problems where even baseline times out under reasonable per-iteration -budgets. problems.jsonl still contains the full set; only sample selection -applies the cap. - -Stage1 (5): SAT-only, quintile-spread by size (5 buckets, 1 per bucket). -Stage2 (50): SAT + UNSAT, quintile-spread by size (5 buckets, 10 per bucket - rank-linspace within bucket). - -Quintile-spread = stable, deterministic ordering by rank then equal-density -sampling per bucket. Guarantees representation across the size distribution -rather than clustering on fastest / smallest problems. +this script rescans each run, rewrites problems.jsonl as the full aggregate, +and re-selects all stage samples. + +Sample pool cap: baseline_ms <= MAX_BASELINE_MS (5 min). problems.jsonl +still contains the full set; only sample selection applies the cap. + +Stage1 (5): SAT, runtime lower-half (fast-medium). Quintile-spread by + baseline_ms within the half. Drives the fast evolve loop — + keeps per-iteration wall-clock bounded. +Stage2 (5): SAT, runtime upper-half (medium-slow) capped at + STAGE2_MAX_MS (2 min). Quintile-spread by baseline_ms within + the capped pool. Catches regressions on harder instances + without letting stage2 wall-clock blow up. +Stage3 (50): SAT+UNSAT, quintile-spread by size + (num_hard_constraints, num_variables). Broad coverage for + final verification / full-dataset scoring. + +Quintile-spread = sort by key, split into 5 equal-rank buckets, pick N/5 +from each bucket via rank-linspace within bucket. Deterministic, no +randomness. """ import json import pathlib @@ -30,11 +32,14 @@ _PROBLEMS = _BENCH / "problems.jsonl" _STAGE1 = _HERE / "shared" / "stage1_sample.json" _STAGE2 = _HERE / "shared" / "stage2_sample.json" +_STAGE3 = _HERE / "shared" / "stage3_sample.json" STAGE1_N = 5 -STAGE2_N = 50 +STAGE2_N = 5 +STAGE3_N = 50 N_BUCKETS = 5 MAX_BASELINE_MS = 300_000 # 5 min cap — exclude monster problems from sample pool +STAGE2_MAX_MS = 120_000 # 2 min cap — stage2 wall must stay bounded def _scan_raw(): @@ -55,6 +60,10 @@ def _size_key(d): return (feats.get("num_hard_constraints", 0), feats.get("num_variables", 0)) +def _runtime_key(d): + return (d.get("z3_status") or {}).get("elapsed_ms", 0) + + def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): total = len(sorted_rows) if total == 0 or n_pick <= 0: @@ -131,19 +140,29 @@ def main(): print(f"sample pool: {len(candidates)} (skipped {skipped} with " f"baseline_ms > {MAX_BASELINE_MS}ms)") - sat = sorted( + # Stage1/2: SAT only, split by runtime median. + sat_by_rt = sorted( (d for d in candidates if (d.get("z3_status") or {}).get("result") == "Sat"), - key=_size_key, + key=_runtime_key, ) - all_sorted = sorted(candidates, key=_size_key) + half = len(sat_by_rt) // 2 + sat_lower = sat_by_rt[:half] # fast-medium runtime + sat_upper = sat_by_rt[half:] # medium-slow runtime + sat_upper_capped = [d for d in sat_upper if _runtime_key(d) <= STAGE2_MAX_MS] + print(f"stage2 pool: {len(sat_upper_capped)} (capped at {STAGE2_MAX_MS}ms, " + f"dropped {len(sat_upper) - len(sat_upper_capped)} from upper-half)") + + s1 = _quintile_spread(sat_lower, STAGE1_N, N_BUCKETS) + s2 = _quintile_spread(sat_upper_capped, STAGE2_N, N_BUCKETS) - s1 = _quintile_spread(sat, STAGE1_N, N_BUCKETS) - s2 = _quintile_spread(all_sorted, STAGE2_N, N_BUCKETS) + # Stage3: SAT+UNSAT, quintile-spread by problem size (broad coverage). + s3 = _quintile_spread(sorted(candidates, key=_size_key), STAGE3_N, N_BUCKETS) - _write_sample(_STAGE1, s1, "stage1", "SAT-only") - _write_sample(_STAGE2, s2, "stage2", "SAT+UNSAT") + _write_sample(_STAGE1, s1, "stage1", "SAT runtime lower-half") + _write_sample(_STAGE2, s2, "stage2", "SAT runtime upper-half") + _write_sample(_STAGE3, s3, "stage3", "SAT+UNSAT broad-size") - for label, picks in (("stage1", s1), ("stage2", s2)): + for label, picks in (("stage1", s1), ("stage2", s2), ("stage3", s3)): print(f"\n{label}:") for d in picks: f_ = d.get("features") or {} diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index ac45024557..5102c95752 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -80,8 +80,13 @@ database: similarity_threshold: 0.95 evaluator: - timeout: 300 # 5 min cap per variant (stage1 only: 5 problems × 24s) - cascade_evaluation: false # stage2 reserved for final_verify.py, not per-variant search + timeout: 600 # 10 min cap per variant (stage3 worst-case ≈ 4 min wall) + cascade_evaluation: true # stage1 → stage2 → stage3 escalation per variant + cascade_thresholds: [1.0, 1.1] + # threshold[0] = stage1 combined_score gate to enter stage2 (>=1.0 = baseline-or-better) + # threshold[1] = stage2 merged combined_score gate to enter stage3 (>=1.1 = 10% gain) + # combined_score uses geomean_speedup * solved_rate^2 (+ efficiency^STATS_WEIGHT). + # Initial baseline-equivalent variant scores ≈1.0 — passes stage1 gate, fails stage3. 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 diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index b8c69c56d1..3d4307f19c 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -1,15 +1,17 @@ """ -Init-phase rebaseline: measure BASELINE elapsed_ms on the stage1 evolution -sample (shared/stage1_sample.json) and write shared/local_baseline.json. +Init-phase rebaseline: measure BASELINE elapsed_ms on the union of +stage1_sample.json + stage2_sample.json (10 SHAs total) and write +shared/local_baseline.json. Wall-clock varies by hardware / z3 version. Raw-data baseline_ms was recorded on a different machine, so comparing against it gives misleading speedup. evaluator._load_problems overlays this local file onto raw data so that speedup = local_baseline_ms / variant_elapsed_ms. -Only the stage1 evolution sample is rebaselined here — fast (~30s with -parallel=5) and matches what the evolve loop actually sees. Final verification -uses a separate larger sample via final_verify.py, which rebaselines on the fly. +Stage1+stage2 are rebaselined — both feed the evolve loop (stage1 fast +triage, stage2 medium-slow regression check). Stage3 (50 SHAs, broad +size-distribution) is NOT rebaselined here because final_verify.py +re-measures baseline on the fly per problem. Per-problem: 1 run, timeout = max(60s, raw_baseline_ms * 2 / 1000). Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). @@ -32,6 +34,7 @@ _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" +_STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" _OUT = _HERE / "shared" / "local_baseline.json" @@ -49,16 +52,30 @@ def _load_problem_index(): return idx -def _load_stage1_shas(): +def _load_target_shas(): + # Rebaseline stage1 + stage2 (10 SHAs total). Stage3 (50) is skipped — + # final_verify.py rebaselines on the fly per-problem and the cost of + # rebaselining 50 with multi-min runtimes is too high for routine setup. if not _STAGE1_SAMPLE.exists(): print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", file=sys.stderr) sys.exit(2) - return list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + shas = list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + if _STAGE2_SAMPLE.exists(): + s2 = json.loads(_STAGE2_SAMPLE.read_text())["sha256"] + seen = set(shas) + for sha in s2: + if sha not in seen: + shas.append(sha) + seen.add(sha) + else: + print(f"WARN: {_STAGE2_SAMPLE} missing — rebaselining stage1 only", + file=sys.stderr) + return shas def main(): - shas = _load_stage1_shas() + shas = _load_target_shas() idx = _load_problem_index() tasks = [] @@ -75,8 +92,8 @@ def main(): import queue as _queue n_parallel = min(parallel_solvers(default=1), len(tasks)) - print(f"rebaselining stage1 evolution sample: {len(tasks)} problems " - f"(from stage1_sample.json)") + print(f"rebaselining stage1+stage2 evolution samples: {len(tasks)} problems " + f"(union of stage1_sample.json + stage2_sample.json)") print(f"timeout per problem = max(60s, raw_ms * 2), parallel={n_parallel} " f"(taskset core pin)") print() diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index f3aa1208ce..e0bb36412c 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -59,6 +59,7 @@ _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _STAGE1_SAMPLE = _HERE / "stage1_sample.json" _STAGE2_SAMPLE = _HERE / "stage2_sample.json" +_STAGE3_SAMPLE = _HERE / "stage3_sample.json" _LOCAL_BASELINE = _HERE / "local_baseline.json" _PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") # None -> sys.executable @@ -115,16 +116,24 @@ def _filter_stage1(problems): def _filter_stage2(problems): - # stage2_sample.json pins a deterministic 50-problem subset built by - # build_samples.py (quintile-spread by num_hard_constraints, num_variables). - # raw-data may exceed 50; without this filter, stage2 cost grows - # unbounded as raw-data accumulates. + # 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: 50 SAT+UNSAT problems, size-stratified broad + # coverage. raw-data may exceed 50; without this filter, stage3 cost + # grows unbounded as raw-data accumulates. + 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 _err_result(metrics_extra, artifacts): metrics = { "combined_score": 0.0, @@ -286,13 +295,18 @@ def _regression_err(p, r): return _regression_err(p, r) by_idx[idx] = (p, r) else: - # Parallel: cancel pending futures on first failure. In-flight tasks - # keep running until subprocess timeout (with __exit__ waits for them). - # Per-problem timeout is now baseline_ms * 1.3 (adaptive), so worst-case + # 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 enumerate(problems)] + futures = [ex.submit(_solve, pair) for pair in ordered] for fut in as_completed(futures): if abort is not None: continue @@ -383,6 +397,11 @@ def evaluate_stage2(program_path): return _evaluate(program_path, problems, "stage2") +def evaluate_stage3(program_path): + problems = _filter_stage3(_load_problems()) + return _evaluate(program_path, problems, "stage3") + + def evaluate(program_path): # Evolution uses stage1 (5 sampled problems) only — fast iteration loop. # Stage2 (full 50 problems) is reserved for final verification via diff --git a/input/z3-bench/evolve/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json index c368df6609..a32d05944b 100644 --- a/input/z3-bench/evolve/shared/local_baseline.json +++ b/input/z3-bench/evolve/shared/local_baseline.json @@ -1,217 +1,215 @@ { - "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8": { - "elapsed_ms": 9658, + "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500": { + "elapsed_ms": 206, "result": "Sat", "matches_raw": true, - "raw_elapsed_ms": 13086, + "raw_elapsed_ms": 294, "stats": { - "conflicts": 346, - "decisions": 13879, - "propagations": 1852731, - "binary propagations": 1542459, - "restarts": 2, - "final checks": 57, - "added eqs": 101168, - "mk clause": 53762, - "mk clause binary": 14972623, - "del clause": 788, - "minimized lits": 4063, - "num checks": 58, - "mk bool var": 61279, - "pb resolves": 27, - "pb conflicts": 27, - "pb propagations": 367, - "pb predicates": 363, - "arith eq adapter": 1621, - "arith-lower": 42952, - "arith-upper": 42151, - "arith-conflicts": 73, - "arith-bound-propagations-lp": 37965, - "arith-diseq": 12892, - "arith-make-feasible": 10663, - "arith-max-columns": 1633, - "arith-max-rows": 1015, - "arith-offset-eqs": 5846, - "arith-fixed-eqs": 13173, - "solve-eqs-steps": 140510, - "solve-eqs-elim-vars": 27284, - "rlimit count": 26312539, - "max memory": 4322.01, - "memory": 4078.56, - "num allocs": 48404857791.0, - "time": 9.658 + "conflicts": 94, + "decisions": 1199, + "propagations": 112554, + "binary propagations": 88002, + "final checks": 12, + "added eqs": 13146, + "mk clause": 6192, + "mk clause binary": 275302, + "del clause": 1023, + "minimized lits": 138, + "num checks": 13, + "mk bool var": 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b/input/z3-bench/evolve/shared/stage1_sample.json @@ -1,48 +1,48 @@ { - "selection": "5 SAT-only, quintile-spread by (num_hard_constraints, num_variables)", + "selection": "5 SAT runtime lower-half, quintile-spread by (num_hard_constraints, num_variables)", "source": "z3-bench/problems.jsonl", "sha256": [ - "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", - "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", - "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", - "6aab51935a092a1d7f05bb431d3d05e0de50c19f5f983026a6d14e7c4b9cf240", - "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" + "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500", + "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", + "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07", + "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678", + "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84" ], "summary": [ { - "sha": "505a2e36055c", + "sha": "187551dceaf0", "num_hard_constraints": 24937, "num_variables": 5752, "baseline_result": "Sat", - "baseline_ms": 307 + "baseline_ms": 294 }, { - "sha": "4a02d7846394", - "num_hard_constraints": 105784, - "num_variables": 32912, + "sha": "17cc4d349ce3", + "num_hard_constraints": 15243, + "num_variables": 3932, "baseline_result": "Sat", - "baseline_ms": 14428 + "baseline_ms": 366 }, { - "sha": "8a304b9c461a", - "num_hard_constraints": 112796, - "num_variables": 17043, + "sha": "aa358daf4226", + "num_hard_constraints": 59387, + "num_variables": 10244, "baseline_result": "Sat", - "baseline_ms": 3053 + "baseline_ms": 1674 }, { - "sha": "6aab51935a09", - "num_hard_constraints": 113172, - "num_variables": 17151, + "sha": "0f6f9aa28440", + "num_hard_constraints": 112990, + "num_variables": 17097, "baseline_result": "Sat", - "baseline_ms": 3681 + "baseline_ms": 2728 }, { - "sha": "a7df812c620d", - "num_hard_constraints": 696365, - "num_variables": 83551, + "sha": "d8d5ffa7d824", + "num_hard_constraints": 112812, + "num_variables": 17043, "baseline_result": "Sat", - "baseline_ms": 93445 + "baseline_ms": 3350 } ] } diff --git a/input/z3-bench/evolve/shared/stage2_sample.json b/input/z3-bench/evolve/shared/stage2_sample.json index d5dd3fe11c..345f7738f8 100644 --- a/input/z3-bench/evolve/shared/stage2_sample.json +++ b/input/z3-bench/evolve/shared/stage2_sample.json @@ -1,164 +1,14 @@ { - "selection": "50 SAT+UNSAT, quintile-spread by (num_hard_constraints, num_variables)", + "selection": "5 SAT runtime upper-half, quintile-spread by (num_hard_constraints, num_variables)", "source": "z3-bench/problems.jsonl", "sha256": [ - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", - "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", - 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"num_variables": 59955, + "baseline_result": "Sat", + "baseline_ms": 66100 + }, + { + "sha": "5250d459bef2", + "num_hard_constraints": 524936, + "num_variables": 59955, + "baseline_result": "Sat", + "baseline_ms": 67552 + }, + { + "sha": "7d51ef2bfc4c", + "num_hard_constraints": 524936, + "num_variables": 59955, + "baseline_result": "Sat", + "baseline_ms": 40558 + }, + { + "sha": "4c942959630b", + "num_hard_constraints": 695689, + "num_variables": 83411, + "baseline_result": "Sat", + "baseline_ms": 132126 + }, + { + "sha": "a7df812c620d", + "num_hard_constraints": 696365, + "num_variables": 83551, + "baseline_result": "Sat", + "baseline_ms": 93445 + }, + { + "sha": "76bcc4964307", + "num_hard_constraints": 696736, + "num_variables": 83621, + "baseline_result": "Sat", + "baseline_ms": 112662 + }, + { + "sha": "7f7b5c632ef8", + "num_hard_constraints": 696736, + "num_variables": 83621, + "baseline_result": "Sat", + "baseline_ms": 181205 + }, + { + "sha": "86efb0762e6d", + "num_hard_constraints": 696736, + "num_variables": 83621, + "baseline_result": "Sat", + "baseline_ms": 80512 + }, + { + "sha": "3b58bd47434b", + "num_hard_constraints": 1092831, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 31732 + }, + { + "sha": "f54dba052fbf", + "num_hard_constraints": 1092831, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 31406 + }, + { + "sha": "bc60e5e73924", + "num_hard_constraints": 1093104, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 32076 + }, + { + "sha": "c5d46b1e315a", + "num_hard_constraints": 1093134, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 32836 + }, + { + "sha": "96158dd28c0b", + "num_hard_constraints": 1095737, + "num_variables": 99809, + "baseline_result": "Unsat", + "baseline_ms": 33160 + }, + { + "sha": "be1c308d7977", + "num_hard_constraints": 1095737, + "num_variables": 99809, + "baseline_result": "Unsat", + "baseline_ms": 33752 + } + ] +} From d005d8647253f7698c4e41a4aae310b8f8c62012 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 11:45:13 +0900 Subject: [PATCH 08/42] [fix] remove rebaseline timeout --- input/z3-bench/evolve/rebaseline_local.py | 17 +- .../evolve/shared/local_baseline.json | 234 +++++++++++++++++- 2 files changed, 235 insertions(+), 16 deletions(-) diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index 3d4307f19c..09fcc5475d 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -13,11 +13,13 @@ size-distribution) is NOT rebaselined here because final_verify.py re-measures baseline on the fly per problem. -Per-problem: 1 run, timeout = max(60s, raw_baseline_ms * 2 / 1000). +Per-problem: 1 run, timeout = REBASELINE_TIMEOUT_S (1 hr safety floor — a +truncated baseline measurement is worse than a slow one). MISMATCH-by-timeout +that the old multiplier produced would poison local_baseline. Big problems +under parallel contention may run ~2x raw; let them finish. Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). """ import json -import math import pathlib import sys import time @@ -37,6 +39,10 @@ _STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" _OUT = _HERE / "shared" / "local_baseline.json" +# Baseline measurement must never be truncated — let z3 finish naturally. +# 1 hour cap is just a safety against true infinite loops; not expected to trigger. +REBASELINE_TIMEOUT_S = 3600 + def _load_problem_index(): idx = {} @@ -94,8 +100,8 @@ def main(): n_parallel = min(parallel_solvers(default=1), len(tasks)) print(f"rebaselining stage1+stage2 evolution samples: {len(tasks)} problems " f"(union of stage1_sample.json + stage2_sample.json)") - print(f"timeout per problem = max(60s, raw_ms * 2), parallel={n_parallel} " - f"(taskset core pin)") + print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (effectively unbounded " + f"— never cut a baseline run short), parallel={n_parallel} (taskset core pin)") print() # Cores leased from a queue so each in-flight task holds a unique slot. @@ -107,10 +113,9 @@ def main(): def _solve(task): i, meta, smt2_path = task - timeout_s = max(60, math.ceil(meta["raw_ms"] * 2 / 1000)) core = _core_pool.get() try: - res = run_z3(smt2_path, BASELINE, timeout_s, cpu_core=core) + res = run_z3(smt2_path, BASELINE, REBASELINE_TIMEOUT_S, cpu_core=core) finally: _core_pool.put(core) return i, meta, res, core diff --git a/input/z3-bench/evolve/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json index a32d05944b..51f5f2b777 100644 --- a/input/z3-bench/evolve/shared/local_baseline.json +++ b/input/z3-bench/evolve/shared/local_baseline.json @@ -1,6 +1,6 @@ { "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500": { - "elapsed_ms": 206, + "elapsed_ms": 183, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 294, @@ -38,11 +38,11 @@ "rlimit count": 1887441, "max memory": 88.59, "memory": 86.55, - "time": 0.206 + "time": 0.182 } }, "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a": { - "elapsed_ms": 73, + "elapsed_ms": 80, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 366, @@ -80,11 +80,11 @@ "rlimit count": 1085456, "max memory": 27.8, "memory": 27.79, - "time": 0.073 + "time": 0.08 } }, "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07": { - "elapsed_ms": 667, + "elapsed_ms": 649, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 1674, @@ -123,11 +123,11 @@ "rlimit count": 7258850, "max memory": 295.69, "memory": 282.15, - "time": 0.667 + "time": 0.648 } }, "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678": { - "elapsed_ms": 1622, + "elapsed_ms": 1595, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 2728, @@ -166,11 +166,11 @@ "rlimit count": 12772018, "max memory": 619.94, "memory": 599.28, - "time": 1.622 + "time": 1.595 } }, "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84": { - "elapsed_ms": 1716, + "elapsed_ms": 1482, "result": "Sat", "matches_raw": true, "raw_elapsed_ms": 3350, @@ -209,7 +209,221 @@ "rlimit count": 15791680, "max memory": 611.27, "memory": 589.44, - "time": 1.716 + "time": 1.482 + } + }, + "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3": { + "elapsed_ms": 8371, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 13885, + "stats": { + "conflicts": 194, + "decisions": 9196, + "propagations": 979859, + "binary propagations": 804835, + "restarts": 1, + "final checks": 46, + "added eqs": 53913, + "mk clause": 53566, + "mk clause binary": 14964298, + "del clause": 756, + "minimized lits": 3410, + "num checks": 47, + "mk bool var": 61193, + "pb resolves": 30, + "pb conflicts": 30, + "pb propagations": 294, + "pb predicates": 363, + "arith eq adapter": 1605, + "arith-lower": 24162, + "arith-upper": 22975, + "arith-conflicts": 22, + "arith-bound-propagations-lp": 21276, + "arith-diseq": 9181, + "arith-make-feasible": 6578, + "arith-max-columns": 1631, + "arith-max-rows": 1015, + "arith-offset-eqs": 2733, + "arith-fixed-eqs": 6225, + "solve-eqs-steps": 140427, + "solve-eqs-elim-vars": 27284, + "rlimit count": 25116882, + "max memory": 4321.63, + "memory": 4075.61, + "num allocs": 44267582854.0, + "time": 8.371 + } + }, + "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12": { + "elapsed_ms": 7363, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 14353, + "stats": { + "conflicts": 370, + "decisions": 12306, + "propagations": 1907454, + "binary propagations": 1581417, + "restarts": 2, + "final checks": 58, + "added eqs": 93222, + "mk clause": 54004, + "mk clause binary": 15003935, + "del clause": 758, + "minimized lits": 6012, + "num checks": 59, + "mk bool var": 61652, + "pb resolves": 32, + "pb conflicts": 32, + "pb propagations": 414, + "pb predicates": 367, + "arith eq adapter": 1615, + "arith-lower": 39727, + "arith-upper": 39085, + "arith-conflicts": 85, + "arith-bound-propagations-lp": 33639, + "arith-diseq": 12851, + "arith-make-feasible": 8778, + "arith-max-columns": 1644, + "arith-max-rows": 1023, + "arith-offset-eqs": 5028, + "arith-fixed-eqs": 12443, + "solve-eqs-steps": 141131, + "solve-eqs-elim-vars": 27438, + "rlimit count": 26185013, + "max memory": 4323.19, + "memory": 4078.87, + "num allocs": 48160422899.0, + "time": 7.363 + } + }, + "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c": { + "elapsed_ms": 7887, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 16518, + "stats": { + "conflicts": 133, + "decisions": 8000, + "propagations": 1042454, + "binary propagations": 860243, + "final checks": 63, + "added eqs": 50767, + "mk clause": 53891, + "mk clause binary": 15003886, + "del clause": 804, + "minimized lits": 4026, + "num checks": 64, + "mk bool var": 61797, + "pb resolves": 27, + "pb conflicts": 27, + "pb propagations": 234, + "pb predicates": 367, + "arith eq adapter": 1672, + "arith-lower": 19978, + "arith-upper": 22681, + "arith-conflicts": 26, + "arith-bound-propagations-lp": 17475, + "arith-diseq": 8202, + "arith-make-feasible": 4982, + "arith-max-columns": 1644, + "arith-max-rows": 1023, + "arith-offset-eqs": 2993, + "arith-fixed-eqs": 6646, + "solve-eqs-steps": 141131, + "solve-eqs-elim-vars": 27438, + "rlimit count": 25115048, + "max memory": 4323.2, + "memory": 4078.04, + "num allocs": 48281023551.0, + "time": 7.887 + } + }, + "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44": { + "elapsed_ms": 29796, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 66100, + "stats": { + "conflicts": 10502, + "decisions": 258344, + "propagations": 80579240, + "binary propagations": 63588207, + "restarts": 73, + "final checks": 26, + "added eqs": 9700074, + "mk clause": 86395, + "mk clause binary": 10395481, + "del clause": 16801, + "minimized lits": 86233, + "num checks": 27, + "mk bool var": 107633, + "pb resolves": 566, + "pb conflicts": 566, + "pb propagations": 5315, + "pb predicates": 3320, + "arith eq adapter": 10330, + "arith-lower": 3602580, + "arith-upper": 3584378, + "arith-fixed-eqs": 2211, + "arith-conflicts": 1568, + "arith-bound-propagations-lp": 2283276, + "arith-diseq": 330595, + "arith-make-feasible": 262361, + "arith-max-columns": 4361, + "arith-max-rows": 2791, + "arith-offset-eqs": 812596, + "solve-eqs-steps": 148571, + "solve-eqs-elim-vars": 45171, + "rlimit count": 186708860, + "max memory": 2497.21, + "memory": 2435.84, + "num allocs": 221776237287.0, + "time": 29.795 + } + }, + "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40": { + "elapsed_ms": 89816, + "result": "Sat", + "matches_raw": true, + "raw_elapsed_ms": 93445, + "stats": { + "conflicts": 24898, + "decisions": 553846, + "propagations": 225442153, + "binary propagations": 180204916, + "restarts": 144, + "final checks": 59, + "added eqs": 26342963, + "mk clause": 145891, + "mk clause binary": 18697981, + "del clause": 36477, + "minimized lits": 209386, + "num checks": 60, + "mk bool var": 168488, + "pb resolves": 790, + "pb conflicts": 790, + "pb propagations": 13162, + "pb predicates": 4862, + "arith eq adapter": 16685, + "arith-lower": 9823534, + "arith-upper": 9814657, + "arith-fixed-eqs": 5003, + "arith-conflicts": 4378, + "arith-bound-propagations-lp": 5867838, + "arith-diseq": 915639, + "arith-make-feasible": 677234, + "arith-max-columns": 6395, + "arith-max-rows": 4090, + "arith-offset-eqs": 2133120, + "solve-eqs-steps": 229433, + "solve-eqs-elim-vars": 61921, + "rlimit count": 514934063, + "max memory": 4700.46, + "memory": 4508.21, + "num allocs": 1569297060933.0, + "time": 89.815 } } } From 32b19d3c4533333a7becb3822792d3f7f461447a Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 12:59:47 +0900 Subject: [PATCH 09/42] change core idx --- input/z3-bench/evolve/_probe_parallel.py | 4 ++-- input/z3-bench/evolve/final_verify.py | 3 ++- input/z3-bench/evolve/rebaseline_local.py | 8 +++++--- input/z3-bench/evolve/shared/baseline_params.py | 5 +++-- input/z3-bench/evolve/shared/evaluator.py | 13 +++++++------ 5 files changed, 19 insertions(+), 14 deletions(-) diff --git a/input/z3-bench/evolve/_probe_parallel.py b/input/z3-bench/evolve/_probe_parallel.py index c7fd7d9666..29b7851001 100644 --- a/input/z3-bench/evolve/_probe_parallel.py +++ b/input/z3-bench/evolve/_probe_parallel.py @@ -45,12 +45,12 @@ def solve(i, sha, smt2, pin): par = {sha: [] for _, sha, _ in tasks} for rep in range(3): with ThreadPoolExecutor(max_workers=5) as ex: - futs = [ex.submit(solve, i, sha, smt2, i % 5) for i, sha, smt2 in tasks] + futs = [ex.submit(solve, i, sha, smt2, (i % 5) + 1) for i, sha, smt2 in tasks] for fut in futs: i, sha, r = fut.result() par[sha].append((r.get("result"), r["elapsed_ms"])) print(f" rep{rep} [{i+1}/5] {sha[:10]} {r.get('result')} " - f"z3={r['elapsed_ms']}ms core={i % 5}") + f"z3={r['elapsed_ms']}ms core={(i % 5) + 1}") # --- summary --- print("\n=== SUMMARY (z3 elapsed_ms) ===") diff --git a/input/z3-bench/evolve/final_verify.py b/input/z3-bench/evolve/final_verify.py index 4fe7920bef..b289340ee7 100644 --- a/input/z3-bench/evolve/final_verify.py +++ b/input/z3-bench/evolve/final_verify.py @@ -123,7 +123,8 @@ def main(): def _measure(idx_meta): i, meta, smt2 = idx_meta - core = (i % n_parallel) if n_parallel > 1 else None + # Skip core 0 — pin baseline + variant to cores 1..n_parallel. + core = ((i % n_parallel) + 1) if n_parallel > 1 else None # Baseline first, then variant — back-to-back so system noise affects # both equally and speedup ratio cancels it out. b = run_z3(smt2, BASELINE, TIMEOUT_S, cpu_core=core) diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index 09fcc5475d..c83773274e 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -105,10 +105,12 @@ def main(): print() # Cores leased from a queue so each in-flight task holds a unique slot. - # Serial mode also leases core 0 (symmetric with parallel) — keeps the - # baseline measurement under the same pin envelope variants will see. + # 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 measurement matches the pin envelope variants + # will see during evolve. _core_pool = _queue.Queue() - for _c in range(n_parallel): + for _c in range(1, n_parallel + 1): _core_pool.put(_c) def _solve(task): diff --git a/input/z3-bench/evolve/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py index 1b45d966c8..e731c4469d 100644 --- a/input/z3-bench/evolve/shared/baseline_params.py +++ b/input/z3-bench/evolve/shared/baseline_params.py @@ -107,7 +107,8 @@ 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)) - core = i % n_parallel + # 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 @@ -146,7 +147,7 @@ def solve(t): 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:>4} {status}") + f"{(i % n_parallel) + 1:>4} {status}") seq_estimate = sum_got_ms / 1000 print() diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index e0bb36412c..4b91de4545 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -210,15 +210,16 @@ def _evaluate(program_path, problems, stage_name): # 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. This is correct even when len(problems) > n_parallel - # (idx % n_parallel would collide across workers). Serial mode also - # leases core 0 — symmetric with parallel so baseline / variant share - # the same pin envelope (no parallel=1-unpinned vs parallel=N-pinned - # bias). Default 1 = sequential, single slot. + # 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(n_parallel): + for _c in range(1, n_parallel + 1): _core_pool.put(_c) def _solve(idx_p): From 88f0f68d0c7fcc8743ebe568700b6e7506a85888 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 04:13:58 +0000 Subject: [PATCH 10/42] use core isolation on docker --- .gitignore | 6 +- docker-run.sh | 49 ++ input/z3-bench/evolve/README.md | 41 ++ .../evolve/shared/local_baseline.json | 429 ------------------ input/z3-bench/evolve/shared/phase1_best.json | 33 -- input/z3-bench/evolve/shared/phase2_best.json | 118 ----- input/z3-bench/evolve/shared/phase3_best.json | 77 ---- scripts/host-isolate-cores.sh | 237 ++++++++++ 8 files changed, 332 insertions(+), 658 deletions(-) delete mode 100644 input/z3-bench/evolve/shared/local_baseline.json delete mode 100644 input/z3-bench/evolve/shared/phase1_best.json delete mode 100644 input/z3-bench/evolve/shared/phase2_best.json delete mode 100644 input/z3-bench/evolve/shared/phase3_best.json create mode 100755 scripts/host-isolate-cores.sh diff --git a/.gitignore b/.gitignore index 597934fc78..cbbbb2787c 100644 --- a/.gitignore +++ b/.gitignore @@ -56,4 +56,8 @@ htmlcov/ # For SR secrets.yaml problems -input/z3-bench/raw-data \ No newline at end of file +input/z3-bench/raw-data + +# z3-bench evolve run artifacts (locally regenerated) +input/z3-bench/evolve/shared/local_baseline.json +input/z3-bench/evolve/shared/phase*_best.json \ No newline at end of file diff --git a/docker-run.sh b/docker-run.sh index e3f2a6db99..9af02a036b 100755 --- a/docker-run.sh +++ b/docker-run.sh @@ -39,6 +39,8 @@ 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() { @@ -52,6 +54,10 @@ show_help() { 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:" @@ -60,6 +66,8 @@ show_help() { 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 "" } @@ -126,6 +134,19 @@ while [[ $# -gt 0 ]]; do 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 @@ -338,6 +359,23 @@ else 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") @@ -363,6 +401,11 @@ else "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 "" @@ -390,6 +433,12 @@ 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 diff --git a/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md index 5490c3f6cf..00dc3ac051 100644 --- a/input/z3-bench/evolve/README.md +++ b/input/z3-bench/evolve/README.md @@ -226,6 +226,47 @@ Detached mode에서도 init script가 background로 한 번 실행됨. `docker l | Rate limit | Pro/Max 5h window 소진. `OPENEVOLVE_MAX_PROBLEMS` 축소 또는 OpenAI 호환 백엔드 병행 | | Mac에서 host claude 마운트 시도 | Mac 바이너리는 Linux 컨테이너에서 안 돎 → docker-run.sh가 자동 skip하고 installer로 fallback | +### 4.8 CPU 핀닝 / 코어 격리 (측정 안정성) + +z3 wall-clock을 일관되게 재기 위해 컨테이너를 특정 코어에 고정. 호스트 사이드 격리(cgroup v2 isolated partition)와 컨테이너 핀닝을 짝지어 사용한다. + +```bash +# === Host (재부팅 없음) === +sudo ./scripts/host-isolate-cores.sh start # cores 1-6 격리 +sudo ./scripts/host-isolate-cores.sh status # 상태 확인 +# 끝나면 +sudo ./scripts/host-isolate-cores.sh stop # 원복 + +# === Docker 실행 === +./docker-run.sh dev -s z3evo --pin # = --pin 1-6 (default) +./docker-run.sh dev -s z3evo --pin 2-7 # 다른 범위 +``` + +`--pin` 동작: +- `--cpuset-cpus=` 추가 (커널 cpuset 강제) +- `/sys/fs/cgroup/isolated.slice` 존재 시 `--cgroup-parent=/isolated.slice`로 격리된 cgroup에 합류 (없으면 경고만) +- 컨테이너 엔트리포인트를 `taskset -c bash -lc ...`로 래핑 + +`host-isolate-cores.sh start` 메커니즘: +- `/sys/fs/cgroup/isolated.slice` 생성 → `cpuset.cpus=1-6`, `cpuset.cpus.partition=isolated` +- 커널이 1-6을 system.slice/user.slice 등 다른 모든 cgroup에서 제거 + 스케줄러 load-balancing 끔 +- `/proc/irq/*/smp_affinity`를 `0,7-19`로 마스크 → 디바이스 IRQ가 격리 코어에 안 떨어짐 +- 상태/IRQ 백업은 `/var/lib/host-isolate-cores/`에 저장됨 (stop이 원복) + +| 변수 | 기본 | 용도 | +|---|---|---| +| `ISOLATED_CPUS` | `1-6` | 격리할 CPU 리스트 (`host-isolate-cores.sh`) | +| `CGROUP_NAME` | `isolated.slice` | 격리 cgroup 이름 | +| `ISOLATED_CGROUP_NAME` | `isolated.slice` | `docker-run.sh`가 합류할 cgroup 이름 | + +**한계**: 런타임 격리로는 커널 스레드(kthread)와 타이머 틱은 못 막음. 완전 격리가 필요하면 boot param을 추가하고 재부팅: + +``` +isolcpus=1-6 nohz_full=1-6 rcu_nocbs=1-6 +``` + +(`host-isolate-cores.sh start` 출력 마지막에 같은 안내가 나옴.) + ## 5. 환경 변수 | 변수 | 기본 | 용도 | diff --git a/input/z3-bench/evolve/shared/local_baseline.json b/input/z3-bench/evolve/shared/local_baseline.json deleted file mode 100644 index 51f5f2b777..0000000000 --- a/input/z3-bench/evolve/shared/local_baseline.json +++ /dev/null @@ -1,429 +0,0 @@ -{ - "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500": { - "elapsed_ms": 183, - "result": "Sat", - "matches_raw": true, - "raw_elapsed_ms": 294, - "stats": { - "conflicts": 94, - "decisions": 1199, - "propagations": 112554, - "binary propagations": 88002, - "final checks": 12, - "added eqs": 13146, - "mk clause": 6192, - "mk clause binary": 275302, - "del clause": 1023, - "minimized lits": 138, - "num checks": 13, - "mk bool var": 8691, - "pb resolves": 23, - "pb conflicts": 23, - "pb propagations": 5, - "pb predicates": 152, - "arith eq adapter": 1063, - "arith-lower": 4975, - 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"a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40": { - "elapsed_ms": 89816, - "result": "Sat", - "matches_raw": true, - "raw_elapsed_ms": 93445, - "stats": { - "conflicts": 24898, - "decisions": 553846, - "propagations": 225442153, - "binary propagations": 180204916, - "restarts": 144, - "final checks": 59, - "added eqs": 26342963, - "mk clause": 145891, - "mk clause binary": 18697981, - "del clause": 36477, - "minimized lits": 209386, - "num checks": 60, - "mk bool var": 168488, - "pb resolves": 790, - "pb conflicts": 790, - "pb propagations": 13162, - "pb predicates": 4862, - "arith eq adapter": 16685, - "arith-lower": 9823534, - "arith-upper": 9814657, - "arith-fixed-eqs": 5003, - "arith-conflicts": 4378, - "arith-bound-propagations-lp": 5867838, - "arith-diseq": 915639, - "arith-make-feasible": 677234, - "arith-max-columns": 6395, - "arith-max-rows": 4090, - "arith-offset-eqs": 2133120, - "solve-eqs-steps": 229433, - "solve-eqs-elim-vars": 61921, - "rlimit count": 514934063, - "max memory": 4700.46, - "memory": 4508.21, - "num allocs": 1569297060933.0, - "time": 89.815 - } - } -} diff --git a/input/z3-bench/evolve/shared/phase1_best.json b/input/z3-bench/evolve/shared/phase1_best.json deleted file mode 100644 index cd8bfc8a4f..0000000000 --- a/input/z3-bench/evolve/shared/phase1_best.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "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": 5, - "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": "rc2", - "opt.optsmt_engine": "basic", - "opt.pb.compile_equality": false, - "opt.priority": "pareto", - "opt.rc2.totalizer": true, - "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": 23.0, - "sls.walksat_ucb_forget": 0.1, - "sls.walksat_ucb_init": false, - "sls.walksat_ucb_noise": 0.0002, - "sls.wp": 23 -} diff --git a/input/z3-bench/evolve/shared/phase2_best.json b/input/z3-bench/evolve/shared/phase2_best.json deleted file mode 100644 index 5f23fbb78e..0000000000 --- a/input/z3-bench/evolve/shared/phase2_best.json +++ /dev/null @@ -1,118 +0,0 @@ -{ - "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": false, - "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": 0.1, - "sat.branching.heuristic": "lrb", - "sat.burst_search": 0, - "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": "dyn_psm", - "sat.gc.burst": false, - "sat.gc.defrag": true, - "sat.gc.increment": 500, - "sat.gc.initial": 80000, - "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": "binary_merge", - "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": "ema", - "sat.restart.emafastglue": 0.03, - "sat.restart.emaslowglue": 1e-05, - "sat.restart.factor": 1.5, - "sat.restart.fast": true, - "sat.restart.initial": 100, - "sat.restart.margin": 1.1, - "sat.retain_blocked_clauses": true, - "sat.scc": true, - "sat.scc.tr": true, - "sat.search.sat.conflicts": 1500, - "sat.search.unsat.conflicts": 1500, - "sat.simplify.delay": 0, - "sat.subsumption": true, - "sat.subsumption.limit": 100000000, - "sat.threads": 1, - "sat.variable_decay": 110 -} diff --git a/input/z3-bench/evolve/shared/phase3_best.json b/input/z3-bench/evolve/shared/phase3_best.json deleted file mode 100644 index 749479b651..0000000000 --- a/input/z3-bench/evolve/shared/phase3_best.json +++ /dev/null @@ -1,77 +0,0 @@ -{ - "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.rounds": 512, - "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": 2, - "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": 2, - "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": 1000, - "smt.pb.conflict_frequency": 1000, - "smt.pb.learn_complements": true, - "smt.phase_caching_off": 100, - "smt.phase_caching_on": 400, - "smt.phase_selection": 3, - "smt.propagate_values": true, - "smt.pull_nested_quantifiers": false, - "smt.qi.cost": "(+ weight generation)", - "smt.qi.eager_threshold": 15.0, - "smt.qi.lazy_threshold": 30.0, - "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": 1, - "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 -} 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 From cb4ca60033db3b792182148d50f2576bc8c1a0df Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 13:54:20 +0900 Subject: [PATCH 11/42] [feat] 4-stage cascade with runtime quintile samples + outlier filter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - build_samples.py: SAT runtime quintile split → stage1 (Q1+2, 5), stage2 (Q3+4, 5), stage3 (Q5, 5); stage4 (broad 20, SAT+UNSAT, dedup vs stage1-3). Tukey IQR k=3 outlier filter drops far-tail monsters (~180s/~130s with current data) so they don't distort quintile boundaries or inflate stage wall-clock. - evaluator.py: evaluate_stage3 chains stage4 internally because openevolve cascade hardcodes 3 stage slots. Gate threshold for the internal stage3→stage4 transition read from config.evaluator.cascade_thresholds[2] via runtime.cascade_threshold(). - config.yaml: cascade_thresholds=[1.03, 1.03, 1.03] — 3% gain required at each transition (stage1→2, stage2→3, stage3→4). - runtime.py: cascade_threshold(index, default) helper. - rebaseline_local.py: extend union to all 4 stage samples. Co-Authored-By: Claude Opus 4.7 (1M context) --- input/z3-bench/evolve/build_samples.py | 131 ++++-- input/z3-bench/evolve/config.yaml | 16 +- input/z3-bench/evolve/rebaseline_local.py | 50 ++- input/z3-bench/evolve/shared/evaluator.py | 45 ++- input/z3-bench/evolve/shared/runtime.py | 17 + .../z3-bench/evolve/shared/stage1_sample.json | 36 +- .../z3-bench/evolve/shared/stage2_sample.json | 46 +-- .../z3-bench/evolve/shared/stage3_sample.json | 372 +----------------- .../z3-bench/evolve/shared/stage4_sample.json | 168 ++++++++ 9 files changed, 404 insertions(+), 477 deletions(-) create mode 100644 input/z3-bench/evolve/shared/stage4_sample.json diff --git a/input/z3-bench/evolve/build_samples.py b/input/z3-bench/evolve/build_samples.py index a41cfc527c..88335b9de1 100644 --- a/input/z3-bench/evolve/build_samples.py +++ b/input/z3-bench/evolve/build_samples.py @@ -1,5 +1,5 @@ """ -Build problems.jsonl + stage1/2/3 sample files from raw-data. +Build problems.jsonl + stage1/2/3/4 sample files from raw-data. Source of truth = raw-data/*.meta.jsonl. raw-data accumulates over time; this script rescans each run, rewrites problems.jsonl as the full aggregate, @@ -8,20 +8,28 @@ Sample pool cap: baseline_ms <= MAX_BASELINE_MS (5 min). problems.jsonl still contains the full set; only sample selection applies the cap. -Stage1 (5): SAT, runtime lower-half (fast-medium). Quintile-spread by - baseline_ms within the half. Drives the fast evolve loop — - keeps per-iteration wall-clock bounded. -Stage2 (5): SAT, runtime upper-half (medium-slow) capped at - STAGE2_MAX_MS (2 min). Quintile-spread by baseline_ms within - the capped pool. Catches regressions on harder instances - without letting stage2 wall-clock blow up. -Stage3 (50): SAT+UNSAT, quintile-spread by size - (num_hard_constraints, num_variables). Broad coverage for - final verification / full-dataset scoring. - -Quintile-spread = sort by key, split into 5 equal-rank buckets, pick N/5 -from each bucket via rank-linspace within bucket. Deterministic, no -randomness. +Outlier filter: after the cap, problems whose baseline_ms lies beyond +[Q1 - k*IQR, Q3 + k*IQR] (Tukey rule, k=OUTLIER_IQR_K=3.0 "far outlier") are +dropped. k=3 instead of the textbook 1.5 because runtime is heavily right- +skewed — k=1.5 would trim away the entire upper quartile that stage3 is +supposed to test. k=3 only removes the genuine long-tail monsters that +distort quintile boundaries and inflate stage3/4 wall-clock. + +Runtime quintiles (SAT-only, sorted by elapsed_ms ascending): + Q1 = bottom 20%, Q2 = 20-40%, ..., Q5 = top 20%. + +Stage1 (5): SAT, quintiles 1+2 (fastest 40%). Quintile-spread within pool. + Cascade gate: geomean_speedup >= 1.03 → stage2. +Stage2 (5): SAT, quintiles 3+4 (middle 40-80%). Quintile-spread within pool. + Cascade gate: geomean_speedup >= 1.03 → stage3. +Stage3 (5): SAT, quintile 5 (slowest 20%). Quintile-spread within pool. + Cascade gate: geomean_speedup >= 1.03 → stage4. +Stage4 (20): SAT+UNSAT, broad runtime-spread, deduplicated against + stage1+2+3. Final cascade gate / scoring sample. + +Quintile-spread = sort by key, split into N_BUCKETS equal-rank buckets, +pick N/N_BUCKETS from each bucket via rank-linspace within bucket. +Deterministic, no randomness. """ import json import pathlib @@ -33,13 +41,16 @@ _STAGE1 = _HERE / "shared" / "stage1_sample.json" _STAGE2 = _HERE / "shared" / "stage2_sample.json" _STAGE3 = _HERE / "shared" / "stage3_sample.json" +_STAGE4 = _HERE / "shared" / "stage4_sample.json" STAGE1_N = 5 STAGE2_N = 5 -STAGE3_N = 50 +STAGE3_N = 5 +STAGE4_N = 20 N_BUCKETS = 5 MAX_BASELINE_MS = 300_000 # 5 min cap — exclude monster problems from sample pool -STAGE2_MAX_MS = 120_000 # 2 min cap — stage2 wall must stay bounded +OUTLIER_IQR_K = 3.0 # linear Tukey k (k=1.5=outlier, k=3=far outlier). + # k=3 drops only extreme tails (e.g. 181s, 132s vs ~13s median). def _scan_raw(): @@ -55,15 +66,37 @@ def _scan_raw(): return rows -def _size_key(d): - feats = d.get("features") or {} - return (feats.get("num_hard_constraints", 0), feats.get("num_variables", 0)) - - def _runtime_key(d): return (d.get("z3_status") or {}).get("elapsed_ms", 0) +def _drop_runtime_outliers(rows, k=OUTLIER_IQR_K): + """ + Remove problems whose baseline_ms lies beyond [Q1 - k*IQR, Q3 + k*IQR]. + Tukey-style linear IQR rule. k=1.5 = standard "outlier", k=3.0 = "far + outlier" — we use 3.0 because runtime is heavily right-skewed and 1.5 + would trim too aggressively (the upper quartile already lives in the + long tail). + Returns (kept_rows, dropped_rows). + """ + ms_sorted = sorted(_runtime_key(d) for d in rows if _runtime_key(d) > 0) + n = len(ms_sorted) + if n < 4: + return list(rows), [] + q1 = ms_sorted[n // 4] + q3 = ms_sorted[(3 * n) // 4] + iqr = q3 - q1 + lo, hi = q1 - k * iqr, q3 + k * iqr + kept, dropped = [], [] + for d in rows: + ms = _runtime_key(d) + if ms <= 0 or lo <= ms <= hi: + kept.append(d) + else: + dropped.append(d) + return kept, dropped + + def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): total = len(sorted_rows) if total == 0 or n_pick <= 0: @@ -108,8 +141,7 @@ def _write_sample(path, picks, label, criteria): path.write_text( json.dumps( { - "selection": f"{len(picks)} {criteria}, quintile-spread by " - "(num_hard_constraints, num_variables)", + "selection": f"{len(picks)} {criteria}", "source": str(_PROBLEMS.relative_to(_BENCH.parent)), "sha256": [d["problem_sha256"] for d in picks], "summary": [_summary(d) for d in picks], @@ -140,29 +172,50 @@ def main(): print(f"sample pool: {len(candidates)} (skipped {skipped} with " f"baseline_ms > {MAX_BASELINE_MS}ms)") - # Stage1/2: SAT only, split by runtime median. + candidates, outliers = _drop_runtime_outliers(candidates) + if outliers: + print(f"dropped {len(outliers)} runtime outliers " + f"(Tukey IQR k={OUTLIER_IQR_K}):") + for d in sorted(outliers, key=_runtime_key): + print(f" {d['problem_sha256'][:12]} " + f"{_runtime_key(d):>7}ms " + f"{(d.get('z3_status') or {}).get('result', '?')}") + + # SAT pool sorted by runtime — basis for stage1/2/3 quintile split. sat_by_rt = sorted( (d for d in candidates if (d.get("z3_status") or {}).get("result") == "Sat"), key=_runtime_key, ) - half = len(sat_by_rt) // 2 - sat_lower = sat_by_rt[:half] # fast-medium runtime - sat_upper = sat_by_rt[half:] # medium-slow runtime - sat_upper_capped = [d for d in sat_upper if _runtime_key(d) <= STAGE2_MAX_MS] - print(f"stage2 pool: {len(sat_upper_capped)} (capped at {STAGE2_MAX_MS}ms, " - f"dropped {len(sat_upper) - len(sat_upper_capped)} from upper-half)") + n_sat = len(sat_by_rt) + + def q_idx(i): # rank boundary for the i-th quintile cut (i in 0..5) + return (i * n_sat) // 5 + + pool_q12 = sat_by_rt[q_idx(0):q_idx(2)] # quintiles 1+2 (fastest 40%) + pool_q34 = sat_by_rt[q_idx(2):q_idx(4)] # quintiles 3+4 (middle 40%) + pool_q5 = sat_by_rt[q_idx(4):q_idx(5)] # quintile 5 (slowest 20%) + print(f"SAT runtime pool: {n_sat} | Q1+2={len(pool_q12)} | " + f"Q3+4={len(pool_q34)} | Q5={len(pool_q5)}") - s1 = _quintile_spread(sat_lower, STAGE1_N, N_BUCKETS) - s2 = _quintile_spread(sat_upper_capped, STAGE2_N, N_BUCKETS) + s1 = _quintile_spread(pool_q12, STAGE1_N, N_BUCKETS) + s2 = _quintile_spread(pool_q34, STAGE2_N, N_BUCKETS) + s3 = _quintile_spread(pool_q5, STAGE3_N, N_BUCKETS) - # Stage3: SAT+UNSAT, quintile-spread by problem size (broad coverage). - s3 = _quintile_spread(sorted(candidates, key=_size_key), STAGE3_N, N_BUCKETS) + # Stage4: SAT+UNSAT, broad runtime-spread, exclude SHAs already in stage1+2+3. + used = {d["problem_sha256"] for d in (s1 + s2 + s3)} + broad = sorted( + (d for d in candidates if d["problem_sha256"] not in used), + key=_runtime_key, + ) + s4 = _quintile_spread(broad, STAGE4_N, N_BUCKETS) - _write_sample(_STAGE1, s1, "stage1", "SAT runtime lower-half") - _write_sample(_STAGE2, s2, "stage2", "SAT runtime upper-half") - _write_sample(_STAGE3, s3, "stage3", "SAT+UNSAT broad-size") + _write_sample(_STAGE1, s1, "stage1", "SAT runtime Q1+2 (fastest 40%)") + _write_sample(_STAGE2, s2, "stage2", "SAT runtime Q3+4 (middle 40%)") + _write_sample(_STAGE3, s3, "stage3", "SAT runtime Q5 (slowest 20%)") + _write_sample(_STAGE4, s4, "stage4", "SAT+UNSAT broad, dedup vs stage1-3") - for label, picks in (("stage1", s1), ("stage2", s2), ("stage3", s3)): + for label, picks in (("stage1", s1), ("stage2", s2), + ("stage3", s3), ("stage4", s4)): print(f"\n{label}:") for d in picks: f_ = d.get("features") or {} diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index 5102c95752..a2b041a796 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -81,12 +81,16 @@ database: evaluator: timeout: 600 # 10 min cap per variant (stage3 worst-case ≈ 4 min wall) - cascade_evaluation: true # stage1 → stage2 → stage3 escalation per variant - cascade_thresholds: [1.0, 1.1] - # threshold[0] = stage1 combined_score gate to enter stage2 (>=1.0 = baseline-or-better) - # threshold[1] = stage2 merged combined_score gate to enter stage3 (>=1.1 = 10% gain) - # combined_score uses geomean_speedup * solved_rate^2 (+ efficiency^STATS_WEIGHT). - # Initial baseline-equivalent variant scores ≈1.0 — passes stage1 gate, fails stage3. + 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 diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index c83773274e..294a4159e9 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -1,17 +1,19 @@ """ Init-phase rebaseline: measure BASELINE elapsed_ms on the union of -stage1_sample.json + stage2_sample.json (10 SHAs total) and write +stage1 + stage2 + stage3 + stage4 sample files and write shared/local_baseline.json. Wall-clock varies by hardware / z3 version. Raw-data baseline_ms was recorded on a different machine, so comparing against it gives misleading speedup. evaluator._load_problems overlays this local file onto raw data so that -speedup = local_baseline_ms / variant_elapsed_ms. +speedup = local_baseline_ms / variant_elapsed_ms and per-problem timeout += baseline_ms * 1.3 is calibrated for the local box. -Stage1+stage2 are rebaselined — both feed the evolve loop (stage1 fast -triage, stage2 medium-slow regression check). Stage3 (50 SHAs, broad -size-distribution) is NOT rebaselined here because final_verify.py -re-measures baseline on the fly per problem. +All three stages feed the evolve loop (cascade_evaluation: true). Without +rebaselining stage3 (50 SHAs), 45/50 problems would fall back to raw_ms +recorded elsewhere and timeout/speedup calculations would drift. +final_verify.py re-measures baseline on the fly per problem anyway, but +the evolve-loop stage3 invocation needs trustworthy local baselines up front. Per-problem: 1 run, timeout = REBASELINE_TIMEOUT_S (1 hr safety floor — a truncated baseline measurement is worse than a slow one). MISMATCH-by-timeout @@ -37,6 +39,8 @@ _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" _STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" +_STAGE3_SAMPLE = _HERE / "shared" / "stage3_sample.json" +_STAGE4_SAMPLE = _HERE / "shared" / "stage4_sample.json" _OUT = _HERE / "shared" / "local_baseline.json" # Baseline measurement must never be truncated — let z3 finish naturally. @@ -59,24 +63,32 @@ def _load_problem_index(): def _load_target_shas(): - # Rebaseline stage1 + stage2 (10 SHAs total). Stage3 (50) is skipped — - # final_verify.py rebaselines on the fly per-problem and the cost of - # rebaselining 50 with multi-min runtimes is too high for routine setup. + # Rebaseline the union of stage1 + stage2 + stage3 samples. With cascade + # evaluation enabled all three stages run in the evolve loop, so accurate + # local baseline is required for per-problem timeout = baseline_ms * 1.3 + # to be machine-correct. Without rebaselining stage3, 45/50 problems fall + # back to raw_ms recorded on a different machine and timeout calibration + # drifts. if not _STAGE1_SAMPLE.exists(): print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", file=sys.stderr) sys.exit(2) - shas = list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) - if _STAGE2_SAMPLE.exists(): - s2 = json.loads(_STAGE2_SAMPLE.read_text())["sha256"] - seen = set(shas) - for sha in s2: + shas = [] + seen = set() + for sample_path, label in ( + (_STAGE1_SAMPLE, "stage1"), + (_STAGE2_SAMPLE, "stage2"), + (_STAGE3_SAMPLE, "stage3"), + (_STAGE4_SAMPLE, "stage4"), + ): + if not sample_path.exists(): + print(f"WARN: {sample_path.name} missing — skipping {label}", + file=sys.stderr) + continue + for sha in json.loads(sample_path.read_text())["sha256"]: if sha not in seen: shas.append(sha) seen.add(sha) - else: - print(f"WARN: {_STAGE2_SAMPLE} missing — rebaselining stage1 only", - file=sys.stderr) return shas @@ -98,8 +110,8 @@ def main(): import queue as _queue n_parallel = min(parallel_solvers(default=1), len(tasks)) - print(f"rebaselining stage1+stage2 evolution samples: {len(tasks)} problems " - f"(union of stage1_sample.json + stage2_sample.json)") + print(f"rebaselining stage1+stage2+stage3+stage4 samples: {len(tasks)} problems " + f"(union of stage{{1,2,3,4}}_sample.json)") print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (effectively unbounded " f"— never cut a baseline run short), parallel={n_parallel} (taskset core pin)") print() diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index 4b91de4545..5481d66f0f 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -50,7 +50,7 @@ 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 # noqa: E402 +from runtime import parallel_solvers, cascade_threshold # noqa: E402 from openevolve.evaluation_result import EvaluationResult # noqa: E402 @@ -60,6 +60,7 @@ _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 @@ -125,15 +126,24 @@ def _filter_stage2(problems): def _filter_stage3(problems): - # stage3_sample.json: 50 SAT+UNSAT problems, size-stratified broad - # coverage. raw-data may exceed 50; without this filter, stage3 cost - # grows unbounded as raw-data accumulates. + # 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, @@ -399,8 +409,31 @@ def evaluate_stage2(program_path): def evaluate_stage3(program_path): - problems = _filter_stage3(_load_problems()) - return _evaluate(program_path, problems, "stage3") + # 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): diff --git a/input/z3-bench/evolve/shared/runtime.py b/input/z3-bench/evolve/shared/runtime.py index e951cc039d..1aeb1571f8 100644 --- a/input/z3-bench/evolve/shared/runtime.py +++ b/input/z3-bench/evolve/shared/runtime.py @@ -47,3 +47,20 @@ def parallel_solvers(default=1): 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/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json index 92e23d2238..10c50a7c36 100644 --- a/input/z3-bench/evolve/shared/stage1_sample.json +++ b/input/z3-bench/evolve/shared/stage1_sample.json @@ -1,12 +1,12 @@ { - "selection": "5 SAT runtime lower-half, quintile-spread by (num_hard_constraints, num_variables)", + "selection": "5 SAT runtime Q1+2 (fastest 40%)", "source": "z3-bench/problems.jsonl", "sha256": [ "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", + "b6393502fdb5834104bcd66ed1a29e6150c036beb3c8a185f04a6341b3fd3102", + "a4658d3a051a31bc6d19ea051195dacfaaea52a34530d63756eab51df3168f97", "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07", - "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678", - "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84" + "a47edaf1aecb80c732ed45ddfc8a6f2a665f0ac175af4982158e7af567d60d1c" ], "summary": [ { @@ -17,11 +17,18 @@ "baseline_ms": 294 }, { - "sha": "17cc4d349ce3", - "num_hard_constraints": 15243, - "num_variables": 3932, + "sha": "b6393502fdb5", + "num_hard_constraints": 25205, + "num_variables": 5854, "baseline_result": "Sat", - "baseline_ms": 366 + "baseline_ms": 312 + }, + { + "sha": "a4658d3a051a", + "num_hard_constraints": 25205, + "num_variables": 5854, + "baseline_result": "Sat", + "baseline_ms": 597 }, { "sha": "aa358daf4226", @@ -31,18 +38,11 @@ "baseline_ms": 1674 }, { - "sha": "0f6f9aa28440", - "num_hard_constraints": 112990, + "sha": "a47edaf1aecb", + "num_hard_constraints": 112978, "num_variables": 17097, "baseline_result": "Sat", - "baseline_ms": 2728 - }, - { - "sha": "d8d5ffa7d824", - "num_hard_constraints": 112812, - "num_variables": 17043, - "baseline_result": "Sat", - "baseline_ms": 3350 + "baseline_ms": 2343 } ] } diff --git a/input/z3-bench/evolve/shared/stage2_sample.json b/input/z3-bench/evolve/shared/stage2_sample.json index 345f7738f8..1d17490816 100644 --- a/input/z3-bench/evolve/shared/stage2_sample.json +++ b/input/z3-bench/evolve/shared/stage2_sample.json @@ -1,14 +1,28 @@ { - "selection": "5 SAT runtime upper-half, quintile-spread by (num_hard_constraints, num_variables)", + "selection": "5 SAT runtime Q3+4 (middle 40%)", "source": "z3-bench/problems.jsonl", "sha256": [ + "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", + "6aab51935a092a1d7f05bb431d3d05e0de50c19f5f983026a6d14e7c4b9cf240", "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12", - "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c", - "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44", - "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" + "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", + "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c" ], "summary": [ + { + "sha": "8a304b9c461a", + "num_hard_constraints": 112796, + "num_variables": 17043, + "baseline_result": "Sat", + "baseline_ms": 3053 + }, + { + "sha": "6aab51935a09", + "num_hard_constraints": 113172, + "num_variables": 17151, + "baseline_result": "Sat", + "baseline_ms": 3681 + }, { "sha": "069af7891076", "num_hard_constraints": 105784, @@ -17,11 +31,11 @@ "baseline_ms": 13885 }, { - "sha": "493242c37992", - "num_hard_constraints": 106100, - "num_variables": 33068, + "sha": "4a02d7846394", + "num_hard_constraints": 105784, + "num_variables": 32912, "baseline_result": "Sat", - "baseline_ms": 14353 + "baseline_ms": 14428 }, { "sha": "03b7e129d625", @@ -29,20 +43,6 @@ "num_variables": 33068, "baseline_result": "Sat", "baseline_ms": 16518 - }, - { - "sha": "3854194b901b", - "num_hard_constraints": 524914, - "num_variables": 59955, - "baseline_result": "Sat", - "baseline_ms": 66100 - }, - { - "sha": "a7df812c620d", - "num_hard_constraints": 696365, - "num_variables": 83551, - "baseline_result": "Sat", - "baseline_ms": 93445 } ] } diff --git a/input/z3-bench/evolve/shared/stage3_sample.json b/input/z3-bench/evolve/shared/stage3_sample.json index 5dabe7ff2d..d9954ac298 100644 --- a/input/z3-bench/evolve/shared/stage3_sample.json +++ b/input/z3-bench/evolve/shared/stage3_sample.json @@ -1,304 +1,14 @@ { - "selection": "50 SAT+UNSAT broad-size, quintile-spread by (num_hard_constraints, num_variables)", + "selection": "5 SAT runtime Q5 (slowest 20%)", "source": "z3-bench/problems.jsonl", "sha256": [ - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", - "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", - "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", - "ac90ca97ff993ebed2e7b1dfdd4e670b0bac1c67d04ac3d592d58456dc4425d9", - "25025d341f86cbcf9038666e523ef8ab9593954558e36e42df8d5784ef0c8a62", - "40bd9a43447844f232d5a083badcde91a09e72d0be849e39b582924e3285cedd", - "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", - "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", - "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500", - "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", - "a4658d3a051a31bc6d19ea051195dacfaaea52a34530d63756eab51df3168f97", - "b6393502fdb5834104bcd66ed1a29e6150c036beb3c8a185f04a6341b3fd3102", - "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", - "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07", - "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "29efe6d38d7b8fcf41ed69b73f5a0bb370f782ab61ffe564fbe41512ea75ce5f", - "42bc61a62698412bbad582db74dd0c9d92710d69f358348ae993deaf2e8d2c08", - 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105784, - "num_variables": 32912, - "baseline_result": "Sat", - "baseline_ms": 14428 - }, - { - "sha": "65444d693881", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Unsat", - "baseline_ms": 13089 - }, - { - "sha": "75ee534dbc92", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Sat", - "baseline_ms": 13086 - }, - { - "sha": "7ac5a84f68bc", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Unsat", - "baseline_ms": 14992 - }, - { - "sha": "deb82eaaca14", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Unsat", - "baseline_ms": 13346 - }, - { - "sha": "03b7e129d625", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 16518 - }, - { - "sha": "318bc251e0bd", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 14232 - }, - { - "sha": "493242c37992", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 14353 - }, - { - "sha": "86468fd861ff", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 15671 - }, - { - "sha": "8a304b9c461a", - "num_hard_constraints": 112796, - "num_variables": 17043, - "baseline_result": "Sat", - "baseline_ms": 3053 - }, - { - "sha": "d8d5ffa7d824", - "num_hard_constraints": 112812, - "num_variables": 17043, - "baseline_result": "Sat", - "baseline_ms": 3350 - }, - { - "sha": "3cacf544225e", - "num_hard_constraints": 112978, - "num_variables": 17097, - "baseline_result": "Unsat", - "baseline_ms": 2996 - }, - { - "sha": "a47edaf1aecb", - "num_hard_constraints": 112978, - "num_variables": 17097, - "baseline_result": "Sat", - "baseline_ms": 2343 - }, - { - "sha": "aeec9f77b092", - "num_hard_constraints": 112978, - "num_variables": 17097, - "baseline_result": "Sat", - "baseline_ms": 2193 - }, - { - "sha": "0f6f9aa28440", - "num_hard_constraints": 112990, - "num_variables": 17097, - "baseline_result": "Sat", - "baseline_ms": 2728 - }, - { - "sha": "21069961e8dc", - "num_hard_constraints": 113006, - "num_variables": 17097, - "baseline_result": "Sat", - "baseline_ms": 2900 - }, - { - "sha": "6aab51935a09", - "num_hard_constraints": 113172, - "num_variables": 17151, - "baseline_result": "Sat", - "baseline_ms": 3681 - }, { "sha": "cc611a5155b2", "num_hard_constraints": 524539, @@ -321,88 +31,18 @@ "baseline_ms": 67552 }, { - "sha": "7d51ef2bfc4c", - "num_hard_constraints": 524936, - "num_variables": 59955, - "baseline_result": "Sat", - "baseline_ms": 40558 - }, - { - "sha": "4c942959630b", - "num_hard_constraints": 695689, - "num_variables": 83411, - "baseline_result": "Sat", - "baseline_ms": 132126 - }, - { - "sha": "a7df812c620d", - "num_hard_constraints": 696365, - "num_variables": 83551, - "baseline_result": "Sat", - "baseline_ms": 93445 - }, - { - "sha": "76bcc4964307", - "num_hard_constraints": 696736, - "num_variables": 83621, - "baseline_result": "Sat", - "baseline_ms": 112662 - }, - { - "sha": "7f7b5c632ef8", + "sha": "86efb0762e6d", "num_hard_constraints": 696736, "num_variables": 83621, "baseline_result": "Sat", - "baseline_ms": 181205 + "baseline_ms": 80512 }, { - "sha": "86efb0762e6d", + "sha": "76bcc4964307", "num_hard_constraints": 696736, "num_variables": 83621, "baseline_result": "Sat", - "baseline_ms": 80512 - }, - { - "sha": "3b58bd47434b", - "num_hard_constraints": 1092831, - "num_variables": 99386, - "baseline_result": "Unsat", - "baseline_ms": 31732 - }, - { - "sha": "f54dba052fbf", - "num_hard_constraints": 1092831, - "num_variables": 99386, - "baseline_result": "Unsat", - "baseline_ms": 31406 - }, - { - "sha": "bc60e5e73924", - "num_hard_constraints": 1093104, - "num_variables": 99386, - "baseline_result": "Unsat", - "baseline_ms": 32076 - }, - { - "sha": "c5d46b1e315a", - "num_hard_constraints": 1093134, - "num_variables": 99386, - "baseline_result": "Unsat", - "baseline_ms": 32836 - }, - { - "sha": "96158dd28c0b", - "num_hard_constraints": 1095737, - "num_variables": 99809, - "baseline_result": "Unsat", - "baseline_ms": 33160 - }, - { - "sha": "be1c308d7977", - "num_hard_constraints": 1095737, - "num_variables": 99809, - "baseline_result": "Unsat", - "baseline_ms": 33752 + "baseline_ms": 112662 } ] } diff --git a/input/z3-bench/evolve/shared/stage4_sample.json b/input/z3-bench/evolve/shared/stage4_sample.json new file mode 100644 index 0000000000..81a1bbd6f4 --- /dev/null +++ b/input/z3-bench/evolve/shared/stage4_sample.json @@ -0,0 +1,168 @@ +{ + "selection": "20 SAT+UNSAT broad, dedup vs stage1-3", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", + "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", + "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", + "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", + "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", + "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", + "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", + "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678", + "21069961e8dc3386828e2608f2ece27db08b11be6e2da9ebadbc40675687e1fb", + "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84", + "42bc61a62698412bbad582db74dd0c9d92710d69f358348ae993deaf2e8d2c08", + "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", + "65444d69388120f98e21ea06a1cf388e621dbc77318828bd673832e90963c568", + "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", + "7ac5a84f68bc6e1488f9c6074a42505a6c95fa8df4f615c4a0ae54359dd3b416", + "f54dba052fbf975e36b4f6dae5b47802965275f27cc276cd8a6199f763a7b8a4", + "3b58bd47434b81258f5d6b182dde28df64a155b70a2aa790c4d608ec8f11ab65", + "c5d46b1e315a5c5e6cf895acac137541641d31b9bc77909ede85575c6876b0db", + "be1c308d797798dce2f3663ad9790822ce632331f6d9c7309a738ab8e438804e", + "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" + ], + "summary": [ + { + "sha": "fac53f0ffed2", + "num_hard_constraints": 14932, + "num_variables": 3797, + "baseline_result": "Unsat", + "baseline_ms": 221 + }, + { + "sha": "fe3a23ba2a11", + "num_hard_constraints": 24936, + "num_variables": 5752, + "baseline_result": "Sat", + "baseline_ms": 258 + }, + { + "sha": "e57b3cf2bc7e", + "num_hard_constraints": 24936, + "num_variables": 5752, + "baseline_result": "Unsat", + "baseline_ms": 268 + }, + { + "sha": "4fb703b12127", + "num_hard_constraints": 15034, + "num_variables": 3842, + "baseline_result": "Unsat", + "baseline_ms": 311 + }, + { + "sha": "17cc4d349ce3", + "num_hard_constraints": 15243, + "num_variables": 3932, + "baseline_result": "Sat", + "baseline_ms": 366 + }, + { + "sha": "133383a624ef", + "num_hard_constraints": 24797, + "num_variables": 5701, + "baseline_result": "Unsat", + "baseline_ms": 480 + }, + { + "sha": "872cbdb83f73", + "num_hard_constraints": 59387, + "num_variables": 10244, + "baseline_result": "Sat", + "baseline_ms": 1298 + }, + { + "sha": "0f6f9aa28440", + "num_hard_constraints": 112990, + "num_variables": 17097, + "baseline_result": "Sat", + "baseline_ms": 2728 + }, + { + "sha": "21069961e8dc", + "num_hard_constraints": 113006, + "num_variables": 17097, + "baseline_result": "Sat", + "baseline_ms": 2900 + }, + { + "sha": "d8d5ffa7d824", + "num_hard_constraints": 112812, + "num_variables": 17043, + "baseline_result": "Sat", + "baseline_ms": 3350 + }, + { + "sha": "42bc61a62698", + "num_hard_constraints": 105784, + "num_variables": 32912, + "baseline_result": "Unsat", + "baseline_ms": 12692 + }, + { + "sha": "75ee534dbc92", + "num_hard_constraints": 105784, + "num_variables": 32912, + "baseline_result": "Sat", + "baseline_ms": 13086 + }, + { + "sha": "65444d693881", + "num_hard_constraints": 105784, + "num_variables": 32912, + "baseline_result": "Unsat", + "baseline_ms": 13089 + }, + { + "sha": "318bc251e0bd", + "num_hard_constraints": 106100, + "num_variables": 33068, + "baseline_result": "Sat", + "baseline_ms": 14232 + }, + { + "sha": "7ac5a84f68bc", + "num_hard_constraints": 105784, + "num_variables": 32912, + "baseline_result": "Unsat", + "baseline_ms": 14992 + }, + { + "sha": "f54dba052fbf", + "num_hard_constraints": 1092831, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 31406 + }, + { + "sha": "3b58bd47434b", + "num_hard_constraints": 1092831, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 31732 + }, + { + "sha": "c5d46b1e315a", + "num_hard_constraints": 1093134, + "num_variables": 99386, + "baseline_result": "Unsat", + "baseline_ms": 32836 + }, + { + "sha": "be1c308d7977", + "num_hard_constraints": 1095737, + "num_variables": 99809, + "baseline_result": "Unsat", + "baseline_ms": 33752 + }, + { + "sha": "a7df812c620d", + "num_hard_constraints": 696365, + "num_variables": 83551, + "baseline_result": "Sat", + "baseline_ms": 93445 + } + ] +} From e33438db64c100c62c461ba02f249dab4a4234e4 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 14:17:47 +0900 Subject: [PATCH 12/42] add center pick option --- input/z3-bench/evolve/build_samples.py | 59 +++++++++++++++++++++----- 1 file changed, 49 insertions(+), 10 deletions(-) diff --git a/input/z3-bench/evolve/build_samples.py b/input/z3-bench/evolve/build_samples.py index 88335b9de1..98f6c70e61 100644 --- a/input/z3-bench/evolve/build_samples.py +++ b/input/z3-bench/evolve/build_samples.py @@ -18,14 +18,20 @@ Runtime quintiles (SAT-only, sorted by elapsed_ms ascending): Q1 = bottom 20%, Q2 = 20-40%, ..., Q5 = top 20%. -Stage1 (5): SAT, quintiles 1+2 (fastest 40%). Quintile-spread within pool. +Stage1 (5): SAT, quintiles 1+2 (fastest 40%). Strategy: STAGE1_STRATEGY. Cascade gate: geomean_speedup >= 1.03 → stage2. -Stage2 (5): SAT, quintiles 3+4 (middle 40-80%). Quintile-spread within pool. +Stage2 (5): SAT, quintiles 3+4 (middle 40-80%). Strategy: STAGE2_STRATEGY. Cascade gate: geomean_speedup >= 1.03 → stage3. -Stage3 (5): SAT, quintile 5 (slowest 20%). Quintile-spread within pool. +Stage3 (5): SAT, quintile 5 (slowest 20%). Strategy: STAGE3_STRATEGY. Cascade gate: geomean_speedup >= 1.03 → stage4. -Stage4 (20): SAT+UNSAT, broad runtime-spread, deduplicated against - stage1+2+3. Final cascade gate / scoring sample. +Stage4 (20): SAT+UNSAT, broad. Strategy: STAGE4_STRATEGY. Deduplicated + against stage1+2+3. + +Strategies (STAGE{N}_STRATEGY): + "center" : pick N contiguous elements around the median of the pool. + Tight within-stage runtime variance (~2-3x). + "spread" : quintile-spread across the pool (N/N_BUCKETS per bucket via + rank-linspace). Intentionally wide distribution. Quintile-spread = sort by key, split into N_BUCKETS equal-rank buckets, pick N/N_BUCKETS from each bucket via rank-linspace within bucket. @@ -52,6 +58,14 @@ OUTLIER_IQR_K = 3.0 # linear Tukey k (k=1.5=outlier, k=3=far outlier). # k=3 drops only extreme tails (e.g. 181s, 132s vs ~13s median). +# Per-stage selection strategy. "center" = contiguous N picks around median +# (tight within-stage runtime variance); "spread" = quintile-spread across +# whole pool (intentionally wide distribution). +STAGE1_STRATEGY = "center" +STAGE2_STRATEGY = "center" +STAGE3_STRATEGY = "center" +STAGE4_STRATEGY = "spread" + def _scan_raw(): rows = [] @@ -97,6 +111,30 @@ def _drop_runtime_outliers(rows, k=OUTLIER_IQR_K): return kept, dropped +def _pick(strategy, sorted_rows, n_pick): + if strategy == "center": + return _center_pick(sorted_rows, n_pick) + if strategy == "spread": + return _quintile_spread(sorted_rows, n_pick, N_BUCKETS) + raise ValueError(f"unknown sample strategy: {strategy!r}") + + +def _center_pick(sorted_rows, n_pick): + """ + Pick n_pick contiguous elements centered on the median of sorted_rows. + Used for stage1/2/3 where samples should cluster tightly (similar runtime) + rather than span the whole pool — reduces within-stage variance to ~2-3x + instead of 5-8x. + """ + total = len(sorted_rows) + if total == 0 or n_pick <= 0: + return [] + if total <= n_pick: + return list(sorted_rows) + start = (total - n_pick) // 2 + return sorted_rows[start:start + n_pick] + + def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): total = len(sorted_rows) if total == 0 or n_pick <= 0: @@ -197,17 +235,18 @@ def q_idx(i): # rank boundary for the i-th quintile cut (i in 0..5) print(f"SAT runtime pool: {n_sat} | Q1+2={len(pool_q12)} | " f"Q3+4={len(pool_q34)} | Q5={len(pool_q5)}") - s1 = _quintile_spread(pool_q12, STAGE1_N, N_BUCKETS) - s2 = _quintile_spread(pool_q34, STAGE2_N, N_BUCKETS) - s3 = _quintile_spread(pool_q5, STAGE3_N, N_BUCKETS) + # Strategy per stage configurable via STAGE{N}_STRATEGY constants. + s1 = _pick(STAGE1_STRATEGY, pool_q12, STAGE1_N) + s2 = _pick(STAGE2_STRATEGY, pool_q34, STAGE2_N) + s3 = _pick(STAGE3_STRATEGY, pool_q5, STAGE3_N) - # Stage4: SAT+UNSAT, broad runtime-spread, exclude SHAs already in stage1+2+3. + # Stage4: SAT+UNSAT, exclude SHAs already in stage1+2+3. used = {d["problem_sha256"] for d in (s1 + s2 + s3)} broad = sorted( (d for d in candidates if d["problem_sha256"] not in used), key=_runtime_key, ) - s4 = _quintile_spread(broad, STAGE4_N, N_BUCKETS) + s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) _write_sample(_STAGE1, s1, "stage1", "SAT runtime Q1+2 (fastest 40%)") _write_sample(_STAGE2, s2, "stage2", "SAT runtime Q3+4 (middle 40%)") From ea29cccf526a2e4f59cc19289824dcbc6c7d7ee2 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 19 May 2026 15:22:32 +0900 Subject: [PATCH 13/42] update stats ratio --- input/z3-bench/evolve/shared/score.py | 63 +++++++++++++++++++-------- 1 file changed, 45 insertions(+), 18 deletions(-) diff --git a/input/z3-bench/evolve/shared/score.py b/input/z3-bench/evolve/shared/score.py index fb212d6311..d257b30559 100644 --- a/input/z3-bench/evolve/shared/score.py +++ b/input/z3-bench/evolve/shared/score.py @@ -4,11 +4,28 @@ - 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. +- efficiency = cross-problem geomean of per-problem weighted geomean over + SMT solver stats {conflicts, decisions, propagations}, with each ratio + (baseline_stat+1)/(variant_stat+1) clipped to [0.1, 10] to bound outliers. + Only solved problems with baseline stats present contribute. Lower solver + work vs baseline -> efficiency > 1. Folded multiplicatively via STATS_WEIGHT + exponent (default 0.333 -> stats / (speedup + stats) ~= 25% in log space; + recommended band 0.2-0.5). Override via env OPENEVOLVE_STATS_WEIGHT + (0 disables). + +Per-key weights reflect SMT solver signal quality: +- conflicts (2.0): CDCL backtracks/learned clauses. Strongest predictor of + search difficulty; lower vs baseline = smarter navigation; robust to + hardware noise. +- decisions (1.5): branching choices. Tracks search-tree size and branching + heuristic quality independent of conflicts. +- propagations (0.5): BCP + theory propagation. High variance; can mean + early pruning (good) or theory thrashing (bad). Tiebreaker only. +- 'mk clause' / 'restarts' intentionally excluded: learning is a feature + (fewer learned clauses != better), restart count alone no work signal. + +Two-level aggregate (per-problem then cross-problem) gives each benchmark +an equal vote regardless of how many stat keys it reports. Why stats matter: identical elapsed_ms with far fewer conflicts/decisions is a sturdier improvement (less variance across machines / problems) than a raw @@ -18,32 +35,42 @@ import math import os -_STATS_KEYS = ("decisions", "propagations", "conflicts", "mk clause") +_STATS_WEIGHTS = {"conflicts": 2.0, "decisions": 1.5, "propagations": 0.5} +_RATIO_CLIP_LO = 0.1 +_RATIO_CLIP_HI = 10.0 def _efficiency(per_problem): - """Geomean of baseline/variant ratio across stat keys, solved problems only. + """Cross-problem geomean of per-problem weighted geomean of clipped ratios. - 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. + Returns (efficiency, num_problems). efficiency=1.0 if no usable problems + (no baseline stats yet, or no solved problems) so the multiplier is a no-op. """ - ratios = [] + 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 {} - for k in _STATS_KEYS: + 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 # +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: + r = (float(b) + 1.0) / (float(v) + 1.0) + # Clip so one runaway problem/key can't dominate the geomean + 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(r) for r in ratios) - return math.exp(log_sum / len(ratios)), len(ratios) + 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 score(per_problem): @@ -80,9 +107,9 @@ def score(per_problem): geomean = math.exp(log_sum / len(speedups)) solved_rate = solved / n - efficiency, eff_pairs = _efficiency(per_problem) + efficiency, eff_problems = _efficiency(per_problem) try: - stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0")) + stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0.333")) except ValueError: stats_weight = 0.0 # Clamp to a sensible band so a runaway env var can't dominate score. @@ -98,6 +125,6 @@ def score(per_problem): "solved": int(solved), "total": int(n), "efficiency": float(efficiency), - "efficiency_pairs": int(eff_pairs), + "efficiency_pairs": int(eff_problems), "stats_weight": float(stats_weight), } From 0161fef4b1332183a2792b92198b048057975ebc Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 20 May 2026 17:00:34 +0900 Subject: [PATCH 14/42] [fix] update scoring logic --- input/z3-bench/evolve/shared/score.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/input/z3-bench/evolve/shared/score.py b/input/z3-bench/evolve/shared/score.py index d257b30559..04062cd4da 100644 --- a/input/z3-bench/evolve/shared/score.py +++ b/input/z3-bench/evolve/shared/score.py @@ -1,8 +1,11 @@ """ -Scoring: geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT. +Scoring: weighted_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 +- per-problem weight = baseline_ms so absolute time savings on long-running + problems dominate; small-runtime wins barely move the needle, and a + regression on a slow problem is penalized far more than on a fast one - solved_rate squared to strongly gate on correctness - efficiency = cross-problem geomean of per-problem weighted geomean over SMT solver stats {conflicts, decisions, propagations}, with each ratio @@ -89,11 +92,14 @@ def score(per_problem): } speedups = [] + weights = [] solved = 0 regressions = 0 for p in per_problem: baseline_decided = p["baseline_result"] in ("Sat", "Unsat") match = p["result"] == p["baseline_result"] + w = max(float(p["baseline_ms"]), 1.0) + weights.append(w) if match: solved += 1 sp = p["baseline_ms"] / max(p["elapsed_ms"], 1) @@ -103,8 +109,9 @@ def score(per_problem): 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)) + 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 / n efficiency, eff_problems = _efficiency(per_problem) From e21d4566599f507fa59bbd312f76ace08aa5655f Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 20 May 2026 18:42:45 +0900 Subject: [PATCH 15/42] [feat] enable cpsat bench --- .gitignore | 5 +- input/README.md | 109 ++++ input/_lib/__init__.py | 0 input/_lib/extract_best.py | 109 ++++ input/_lib/load_bench_config.py | 105 ++++ input/_lib/prepare_phase.py | 65 +++ input/_lib/runtime.py | 93 ++++ input/_lib/subprocess_runner.py | 101 ++++ input/cpsat-bench/evolve/README.md | 140 +++++ input/cpsat-bench/evolve/build_samples.py | 225 ++++++++ input/cpsat-bench/evolve/config.yaml | 139 +++++ input/cpsat-bench/evolve/extract_best.py | 23 + .../evolve/phase1_search/initial_program.py | 41 ++ .../evolve/phase2_presolve/initial_program.py | 47 ++ .../evolve/phase3_lp_cuts/initial_program.py | 54 ++ .../evolve/phase4_unified/initial_program.py | 31 ++ .../evolve/prepare_phase_unified.py | 21 + input/cpsat-bench/evolve/rebaseline_local.py | 182 +++++++ .../evolve/shared/_cpsat_solve_worker.py | 173 ++++++ .../evolve/shared/baseline_params.py | 139 +++++ .../cpsat-bench/evolve/shared/cpsat_runner.py | 24 + input/cpsat-bench/evolve/shared/evaluator.py | 362 +++++++++++++ .../evolve/shared/local_baseline.json | 492 ++++++++++++++++++ input/cpsat-bench/evolve/shared/runtime.py | 25 + input/cpsat-bench/evolve/shared/score.py | 136 +++++ .../evolve/shared/stage1_sample.json | 38 ++ .../evolve/shared/stage2_sample.json | 38 ++ .../evolve/shared/stage3_sample.json | 38 ++ .../evolve/shared/stage4_sample.json | 128 +++++ input/cpsat-bench/problems.jsonl | 86 +++ input/run_phase.sh | 258 +++++++++ input/z3-bench/evolve/config.yaml | 18 +- input/z3-bench/evolve/extract_best.py | 114 +--- input/z3-bench/evolve/prepare_phase4.py | 63 +-- input/z3-bench/evolve/run_phase.sh | 110 ---- input/z3-bench/evolve/shared/runtime.py | 63 +-- .../z3-bench/evolve/shared/stage1_sample.json | 32 +- .../z3-bench/evolve/shared/stage2_sample.json | 32 +- .../z3-bench/evolve/shared/stage3_sample.json | 10 +- .../z3-bench/evolve/shared/stage4_sample.json | 78 +-- input/z3-bench/evolve/shared/z3_runner.py | 102 +--- 41 files changed, 3565 insertions(+), 484 deletions(-) create mode 100644 input/README.md create mode 100644 input/_lib/__init__.py create mode 100644 input/_lib/extract_best.py create mode 100644 input/_lib/load_bench_config.py create mode 100644 input/_lib/prepare_phase.py create mode 100644 input/_lib/runtime.py create mode 100644 input/_lib/subprocess_runner.py create mode 100644 input/cpsat-bench/evolve/README.md create mode 100644 input/cpsat-bench/evolve/build_samples.py create mode 100644 input/cpsat-bench/evolve/config.yaml create mode 100644 input/cpsat-bench/evolve/extract_best.py create mode 100644 input/cpsat-bench/evolve/phase1_search/initial_program.py create mode 100644 input/cpsat-bench/evolve/phase2_presolve/initial_program.py create mode 100644 input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py create mode 100644 input/cpsat-bench/evolve/phase4_unified/initial_program.py create mode 100644 input/cpsat-bench/evolve/prepare_phase_unified.py create mode 100644 input/cpsat-bench/evolve/rebaseline_local.py create mode 100644 input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py create mode 100644 input/cpsat-bench/evolve/shared/baseline_params.py create mode 100644 input/cpsat-bench/evolve/shared/cpsat_runner.py create mode 100644 input/cpsat-bench/evolve/shared/evaluator.py create mode 100644 input/cpsat-bench/evolve/shared/local_baseline.json create mode 100644 input/cpsat-bench/evolve/shared/runtime.py create mode 100644 input/cpsat-bench/evolve/shared/score.py create mode 100644 input/cpsat-bench/evolve/shared/stage1_sample.json create mode 100644 input/cpsat-bench/evolve/shared/stage2_sample.json create mode 100644 input/cpsat-bench/evolve/shared/stage3_sample.json create mode 100644 input/cpsat-bench/evolve/shared/stage4_sample.json create mode 100644 input/cpsat-bench/problems.jsonl create mode 100755 input/run_phase.sh delete mode 100755 input/z3-bench/evolve/run_phase.sh diff --git a/.gitignore b/.gitignore index cbbbb2787c..651f8b5df1 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] @@ -52,12 +51,14 @@ htmlcov/ # Misc .DS_Store .venv +.claude # For SR secrets.yaml problems input/z3-bench/raw-data +input/cpsat-bench/raw-data # z3-bench evolve run artifacts (locally regenerated) input/z3-bench/evolve/shared/local_baseline.json -input/z3-bench/evolve/shared/phase*_best.json \ No newline at end of file +input/z3-bench/evolve/shared/phase*_best.json diff --git a/input/README.md b/input/README.md new file mode 100644 index 0000000000..4025d76a26 --- /dev/null +++ b/input/README.md @@ -0,0 +1,109 @@ +# input/ — Benchmark Pipelines + Shared Lib + +Each `/` directory holds one OpenEvolve solver-parameter-tuning +pipeline. Common scaffolding lives under `_lib/` and is wired into each +bench via thin per-bench wrappers (keeps bench code <100 LOC for the +boilerplate parts). + +## Layout + +``` +input/ +├── run_phase.sh # single entry: ./run_phase.sh [flags] +├── _lib/ # shared library (importable as `_lib.*`) +│ ├── runtime.py # parallel_solvers / cascade_threshold / core_range +│ ├── subprocess_runner.py # run_solver(worker, problem, params, timeout, ...) +│ ├── extract_best.py # main(root, shared, phase_dirs) — CLI core +│ └── prepare_phase.py # main(root, shared, prior_phases, unified_file) +├── z3-bench/ +│ ├── raw-data/ # .smt2 + ____seed0.meta.jsonl +│ └── evolve/ +│ ├── bench.env # config sourced by ../../run_phase.sh +│ └── ... # baseline_params, evaluator, phases, etc. +└── cpsat-bench/ + ├── raw-data/ # .cpsat.pb + ____seed0.meta.jsonl + └── evolve/ + ├── bench.env + └── ... +``` + +## _lib/ — what's shared + +| module | purpose | per-bench wrapper | +|---|---|---| +| `_lib/runtime.py` | parse `OPENEVOLVE_PARALLEL_SOLVERS`, `OPENEVOLVE_CORE_RANGE`, `cascade_thresholds` from env + config.yaml | `/evolve/shared/runtime.py` (binds config path, re-exports) | +| `_lib/subprocess_runner.py` | spawn worker subprocess, taskset pin, timeout grace, parse stdout JSON | `/evolve/shared/_runner.py` (binds WORKER path, names `run_`) | +| `_lib/extract_best.py` | argparse + load best_program.py + write `phaseN_best.json` | `/evolve/extract_best.py` (passes `PHASE_DIRS` dict) | +| `_lib/prepare_phase.py` | merge `phaseN_best.json` files, rewrite unified phase's EVOLVE-BLOCK | `/evolve/prepare_phase_unified.py` (passes `PRIOR_PHASES` list) | +| `input/run_phase.sh` | single user-facing entry: `./run_phase.sh [--pin] [--extract-only] [--iterations]`. Resolves bench, sources `/evolve/bench.env`, runs flow. | `/evolve/bench.env` (5-line config: `PHASE_DIRS`, `PHASE_ITERS`, `UNIFIED_PREPARE_SCRIPT`, `SOLVER_CHECK_CMD`, `SOLVER_INSTALL_HINT`) | + +## Per-bench files NOT shared + +Stay per-bench because each is solver- or dataset-specific: + +- `shared/baseline_params.py` — `BASELINE` + `LOCKED` dicts (recorded params) +- `shared/__solve_worker.py` — solver Python binding glue +- `shared/score.py` — score mode (`decision` / `cost` / `time-only`) +- `shared/evaluator.py` — cascade scaffold + decisive-result set + key stats +- `build_samples.py` — meta.jsonl field paths +- `rebaseline_local.py` — local baseline measurement +- `phase{N}_/initial_program.py` — phase namespace + initial overrides +- `config.yaml` — bench knobs + LLM system message + +## Wiring pattern (per-bench wrapper) + +Each wrapper: +1. Computes `_INPUT_DIR` (relative to its own file). +2. Inserts it into `sys.path` so `from _lib import ...` works. +3. Calls / re-exports the shared function with bench-specific args. + +Example — `/evolve/shared/runtime.py`: + +```python +import pathlib, sys +_HERE = pathlib.Path(__file__).resolve().parent +_CONFIG_YAML = _HERE.parent / "config.yaml" +_INPUT_DIR = _HERE.parents[2] +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) +from _lib import runtime as _rt + +core_range = _rt.core_range +def parallel_solvers(default=1): return _rt.parallel_solvers(_CONFIG_YAML, default=default) +def cascade_threshold(i, d): return _rt.cascade_threshold(_CONFIG_YAML, i, d) +``` + +Same pattern for `_runner.py`, `extract_best.py`, `prepare_phase_unified.py`. + +## Adding a new bench + +1. Make `input//raw-data/` and populate it (e.g. `load_script.sh`). +2. Scaffold `/evolve/` using the [openevolve-pipeline skill](../.claude/skills/openevolve-pipeline/SKILL.md). +3. For each shared module, copy a per-bench wrapper from `z3-bench/` or + `cpsat-bench/` and adjust the bench-specific bits (worker path, phase + dirs, config path). + +## Environment knobs (work in both benches) + +| variable | use | +|---|---| +| `OPENEVOLVE_PARALLEL_SOLVERS` | concurrent solver subprocesses per stage (cores 1..N) | +| `OPENEVOLVE_CORE_RANGE` | explicit taskset core range `N-M` (overrides PARALLEL_SOLVERS for pinning; concurrency = range size) | +| `OPENEVOLVE_MAX_PROBLEMS` | cap stage problem count (testing) | +| `OPENEVOLVE_STATS_WEIGHT` | exponent on efficiency factor in score.py (0 disables) | +| `OPENEVOLVE_COST_WEIGHT` | (cost-mode only) exponent on cost_ratio | +| `OPENEVOLVE_PYTHON_BIN` | python used by solver worker subprocess | +| `SKIP_REBASELINE` | skip per-host baseline remeasurement (reuse `local_baseline.json`) | + +## Quick start (any bench) + +```bash +# (cpsat-bench example — swap to z3-bench by name) +bash input/cpsat-bench/raw-data/load_script.sh # populate raw-data +python input/cpsat-bench/evolve/build_samples.py # stage{1..4}_sample.json +python input/cpsat-bench/evolve/shared/baseline_params.py # sanity self-test +./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 +``` diff --git a/input/_lib/__init__.py b/input/_lib/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/input/_lib/extract_best.py b/input/_lib/extract_best.py new file mode 100644 index 0000000000..7a79536434 --- /dev/null +++ b/input/_lib/extract_best.py @@ -0,0 +1,109 @@ +""" +Generic per-phase winner extractor. Per-bench `extract_best.py` wrappers +provide PHASE_DIRS and call `main(root, shared, phase_dirs)`. + +After phase N completes, load get_phase_overrides() from its best_program.py +(default: openevolve_output/best/best_program.py) and write +shared/phaseN_best.json. The next phase's initial_program.py picks it up. + +--from-checkpoints scans openevolve_output/checkpoints/checkpoint_*/ and +picks the program with the highest combined_score (best_program_info.json). +""" +import argparse +import importlib.util +import json +import pathlib +import sys + + +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 main(root, shared, phase_dirs, argv=None): + """ + root — bench evolve dir (pathlib.Path) + shared — bench evolve/shared dir (pathlib.Path) + phase_dirs — {phase_int: "phaseN_"} (excludes the final/unified phase) + """ + choices = sorted(phase_dirs.keys()) + ap = argparse.ArgumentParser( + description="Extract get_phase_overrides() from phase's best_program.py.") + ap.add_argument("phase", type=int, choices=choices, 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) + + n = args.phase + phase_dir = pathlib.Path(root) / phase_dirs[n] + + if args.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) + + out = pathlib.Path(shared) / f"phase{n}_best.json" + out.write_text(json.dumps(overrides, indent=2, sort_keys=True) + "\n") + print(f"wrote {out.relative_to(pathlib.Path(root))} ({len(overrides)} keys)") diff --git a/input/_lib/load_bench_config.py b/input/_lib/load_bench_config.py new file mode 100644 index 0000000000..795c54d5f0 --- /dev/null +++ b/input/_lib/load_bench_config.py @@ -0,0 +1,105 @@ +""" +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: + bench: + phases: + - dir: phase1_x + iters: 60 # optional; null/omitted => fall back to max_iterations + - dir: phase2_y + ... + unified_prepare_script: prepare_phase_unified.py # optional + solver_check_cmd: 'python -c "from ortools.sat.python import cp_model"' + solver_install_hint: install pip install ortools + rebaseline_script: rebaseline_local.py # optional override + extract_best_script: extract_best.py # optional override + build_samples_script: build_samples.py # optional override + +Output (stdout): + export PHASE_DIRS='phase1_x phase2_y ...' + export PHASE_ITERS='60 80 40' # space-separated; missing iters -> empty slot + export UNIFIED_PREPARE_SCRIPT='prepare_phase_unified.py' + export SOLVER_CHECK_CMD='python -c "..."' + ... + +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_SCRIPT", bench.get("unified_prepare_script")) + _emit("SOLVER_CHECK_CMD", bench.get("solver_check_cmd")) + _emit("SOLVER_INSTALL_HINT", bench.get("solver_install_hint")) + _emit("REBASELINE_SCRIPT", bench.get("rebaseline_script")) + _emit("EXTRACT_BEST_SCRIPT", bench.get("extract_best_script")) + _emit("BUILD_SAMPLES_SCRIPT", bench.get("build_samples_script")) + + +if __name__ == "__main__": + main() diff --git a/input/_lib/prepare_phase.py b/input/_lib/prepare_phase.py new file mode 100644 index 0000000000..39a08219bf --- /dev/null +++ b/input/_lib/prepare_phase.py @@ -0,0 +1,65 @@ +""" +Materialize a unified phase's initial_program.py with a literal dict +containing the union of prior-phase winners (from shared/phaseN_best.json). + +Per-bench `prepare_phase_unified.py` wrappers call +`main(root, shared, prior_phases, unified_file)` after all prior phases +have completed (and extract_best.py ran for each). + +Rewrites only the EVOLVE-BLOCK section of the unified phase's initial_program.py. +""" +import json +import pathlib +import pprint +import re +import sys + +_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 main(root, shared, prior_phases, unified_file, dict_name="UNIFIED_OVERRIDES"): + """ + root — bench evolve dir (pathlib.Path) + shared — bench evolve/shared dir (pathlib.Path) + prior_phases — list of phase ints to merge (e.g. [1, 2, 3]) + unified_file — path to the unified phase's initial_program.py + dict_name — variable name written in the EVOLVE-BLOCK (default UNIFIED_OVERRIDES) + """ + unified_file = pathlib.Path(unified_file) + merged = {} + phase_counts = {} + for n in prior_phases: + d = _load(shared, n) + phase_counts[n] = len(d) + merged.update(d) + print("merged keys: " + " ".join(f"p{n}={c}" for n, c in phase_counts.items()) + + f" union={len(merged)}") + + src = unified_file.read_text() + 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) + print(f"wrote {unified_file.relative_to(pathlib.Path(root))} " + f"({len(merged)} keys in EVOLVE-BLOCK)") diff --git a/input/_lib/runtime.py b/input/_lib/runtime.py new file mode 100644 index 0000000000..7c8a84d8f2 --- /dev/null +++ b/input/_lib/runtime.py @@ -0,0 +1,93 @@ +""" +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 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/subprocess_runner.py b/input/_lib/subprocess_runner.py new file mode 100644 index 0000000000..b00e122480 --- /dev/null +++ b/input/_lib/subprocess_runner.py @@ -0,0 +1,101 @@ +""" +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. 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: + args = [_TASKSET, "-c", str(int(cpu_core))] + 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/evolve/README.md b/input/cpsat-bench/evolve/README.md new file mode 100644 index 0000000000..593b24c89a --- /dev/null +++ b/input/cpsat-bench/evolve/README.md @@ -0,0 +1,140 @@ +# cpsat-bench Parameter Tuning via OpenEvolve + +Tune OR-tools CP-SAT (`ortools.sat.python.cp_model`) parameters by +evolutionary search on the `input/cpsat-bench/raw-data` dataset (85 OPTIMAL +instances stored as binary `CpModelProto`). Goal: minimize wall-clock vs +baseline while preserving feasibility / objective. + +> OpenEvolve concept primer: see `input/z3-bench/evolve/OPENEVOLVE_INTRO.md` — +> z3-bench is the canonical reference impl; the pattern here is identical +> (cost-mode scoring instead of decision-mode is the only difference). + +## Layout + +``` +input/cpsat-bench/ +├── raw-data/ # flat layout (mirrors z3-bench) +│ ├── load_script.sh # gitlab pkg 71 downloader +│ ├── .cpsat.pb # binary CpModelProto +│ └── ____seed0.meta.jsonl +├── problems.jsonl # built by build_samples.py +└── evolve/ + ├── README.md # this file + ├── config.yaml # OpenEvolve config + custom knobs + ├── run_phase.sh # phase runner (chmod +x) + ├── build_samples.py # stage{1..4} sample selection + ├── extract_best.py # per-phase winner extraction + ├── prepare_phase_unified.py # materializes phase4 EVOLVE-BLOCK + ├── rebaseline_local.py # measure baseline on local box + ├── shared/ + │ ├── baseline_params.py # BASELINE + LOCKED + │ ├── score.py # cost mode + │ ├── runtime.py # config.yaml passthrough + │ ├── evaluator.py # cascade stages + │ ├── cpsat_runner.py # subprocess solver invoker + │ ├── _cpsat_solve_worker.py + │ ├── stage{1..4}_sample.json # built by build_samples.py + │ └── local_baseline.json # built by rebaseline_local.py + └── phase{1..4}_/ + └── initial_program.py # EVOLVE-BLOCK +``` + +## Evaluation flow (cascade) + +``` +LLM-mutated initial_program.py + ↓ +evaluator.py: + 1. get_params() → dict + 2. LOCKED-violation check (random_seed, num_search_workers, timeout_sec) + 3. stage1 (5 problems, runtime Q1+Q2): + per-problem solver run, invalid_param triggers early 0 + score → gate (cascade_thresholds[0]) → stage2 + 4. stage2 (5 problems, runtime Q3+Q4): + same, gate → stage3 + 5. stage3 (5 problems, runtime Q5): + same, gate (cascade_thresholds[2]) → stage4 (chained inside stage3) + 6. stage4 (20 broad-runtime problems): + final metrics + per-problem artifacts (top 20 entries surfaced) +``` + +## Quick start + +```bash +# 0) populate raw-data from gitlab package 71 (first time only) +cd input/cpsat-bench/raw-data && bash load_script.sh && cd - + +# 1) build sample files (re-run any time raw-data changes) +python input/cpsat-bench/evolve/build_samples.py + +# 2) sanity-check: does the runner reproduce baseline timings? +python input/cpsat-bench/evolve/shared/baseline_params.py + +# 3) optional: measure baseline on local box (captures objective_value +# for cost-mode scoring — first run picks it up automatically too) +python input/cpsat-bench/evolve/rebaseline_local.py + +# 4) run phases sequentially +./input/cpsat-bench/evolve/run_phase.sh 1 +./input/cpsat-bench/evolve/run_phase.sh 2 +./input/cpsat-bench/evolve/run_phase.sh 3 +./input/cpsat-bench/evolve/run_phase.sh 4 +``` + +After each non-final phase, `run_phase.sh` calls `extract_best.py` to write +`shared/phaseN_best.json`, which the next phase's `initial_program.py` loads. + +## Resume from checkpoint + +```bash +cd input/cpsat-bench/evolve/phase2_presolve/ +python /app/openevolve-run.py \ + initial_program.py \ + ../shared/evaluator.py \ + --config ../config.yaml \ + --checkpoint openevolve_output/checkpoints/checkpoint_50 \ + --iterations 100 +``` + +## Environment variables + +| variable | default | use | +|---|---|---| +| `CLAUDE_CODE_OAUTH_TOKEN` / `OPENAI_API_KEY` | — | LLM credential (claude_code provider) | +| `OPENEVOLVE_PARALLEL_SOLVERS` | 1 | concurrent solver subprocesses per stage (taskset-pinned on Linux to cores 1..N). Each CP-SAT uses num_search_workers=8 internally — size accordingly. | +| `OPENEVOLVE_CORE_RANGE` | unset | explicit taskset core range `N-M` (overrides PARALLEL_SOLVERS for pinning; concurrency = range size). e.g. `2-7` → 6 workers on cores 2..7. `run_phase.sh --pin 2-7` is sugar for this. | +| `OPENEVOLVE_MAX_PROBLEMS` | unlimited | cap stage problem count (testing) | +| `OPENEVOLVE_STATS_WEIGHT` | 0.333 | exponent on efficiency factor (0 disables) | +| `OPENEVOLVE_COST_WEIGHT` | 1.0 | exponent on cost_ratio in cost-mode score (0 disables cost factor) | +| `OPENEVOLVE_PYTHON_BIN` | `sys.executable` | python used by solver worker | +| `SKIP_REBASELINE` | 0 | skip per-host baseline remeasurement (reuse existing local_baseline.json) | + +## CPU pinning (Linux) + +For stable wall-clock measurement, isolate cores via +`scripts/host-isolate-cores.sh start` (from the openevolve repo root) and +run the docker container with `./docker-run.sh dev -s cpsat --pin 1-6`. +The runner already pins each worker subprocess with `taskset -c `. + +## Score formula (cost mode) + +``` +combined_score = geomean( (b_obj/v_obj)^COST_W * (b_ms/v_ms) ) + * solved_rate^2 + * efficiency^STATS_WEIGHT +``` + +- All 85 baselines are OPTIMAL, so if the variant also reaches OPTIMAL, the + cost ratio collapses to 1.0 and score reduces to geomean(time speedup). +- Variants that bail to FEASIBLE-but-worse-objective are penalized via the + cost ratio; variants that bail to UNKNOWN/INFEASIBLE contribute 1e-6 to + the geomean (~60× penalty across stage1's 5 problems). +- Efficiency factor multiplies based on `num_conflicts` (weight 2.0) and + `num_branches` (weight 1.5) ratios vs baseline. + +## Locked params + +`random_seed=0`, `num_search_workers=8`, `timeout_sec=-1`. + +Modifying any locked key in `get_params()` returns `combined_score=0` plus a +`locked_violated` artifact identifying the offending key. diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py new file mode 100644 index 0000000000..032205e9ee --- /dev/null +++ b/input/cpsat-bench/evolve/build_samples.py @@ -0,0 +1,225 @@ +""" +Build problems.jsonl + stage1/2/3/4 sample files from raw-data/. + +Layout (flat, mirroring z3-bench): + raw-data/.cpsat.pb binary CpModelProto + raw-data/____seed0.meta.jsonl one-line JSON with + problem_sha256, problem_filename, cpsat_applied_params, + cpsat_status (result, elapsed_ms), cpsat_response_stats, ... + +problems.jsonl is the full aggregate; sample selection applies a runtime cap +(MAX_BASELINE_MS) and a Tukey IQR outlier filter (k=3.0) so stage quintile +boundaries don't get distorted by long-tail monsters. + +Stages (decisive = OPTIMAL or FEASIBLE; this dataset is all OPTIMAL): + stage1 (5) center pick from runtime Q1+Q2 (fastest 40%) + stage2 (5) center pick from runtime Q3+Q4 (middle 40%) + stage3 (5) center pick from runtime Q5 (slowest 20%) + stage4 (20) quintile-spread broad sample, dedup vs stage1-3 +""" +import json +import pathlib + +_HERE = pathlib.Path(__file__).resolve().parent +_BENCH = _HERE.parent +_RAW = _BENCH / "raw-data" +_PROBLEMS = _BENCH / "problems.jsonl" +_STAGE1 = _HERE / "shared" / "stage1_sample.json" +_STAGE2 = _HERE / "shared" / "stage2_sample.json" +_STAGE3 = _HERE / "shared" / "stage3_sample.json" +_STAGE4 = _HERE / "shared" / "stage4_sample.json" + +STAGE1_N = 5 +STAGE2_N = 5 +STAGE3_N = 5 +STAGE4_N = 20 +N_BUCKETS = 5 +MAX_BASELINE_MS = 120_000 # cap — exclude > 2 min monsters from sample pool +OUTLIER_IQR_K = 3.0 + +STAGE1_STRATEGY = "center" +STAGE2_STRATEGY = "center" +STAGE3_STRATEGY = "center" +STAGE4_STRATEGY = "spread" + +_DECISIVE_RESULTS = {"OPTIMAL", "FEASIBLE"} + + +def _scan_raw(): + """Glob raw-data/*.meta.jsonl (one-line JSON per problem). The meta + already contains problem_sha256 + problem_filename; no derivation needed.""" + rows = [] + for path in sorted(_RAW.glob("*.meta.jsonl")): + with open(path) as f: + line = f.readline().strip() + if not line: + continue + d = json.loads(line) + rows.append(d) + return rows + + +def _runtime_key(d): + return (d.get("cpsat_status") or {}).get("elapsed_ms", 0) + + +def _result_key(d): + return (d.get("cpsat_status") or {}).get("result") + + +def _id_key(d): + return d["problem_sha256"] + + +def _drop_runtime_outliers(rows, k=OUTLIER_IQR_K): + ms_sorted = sorted(_runtime_key(d) for d in rows if _runtime_key(d) > 0) + n = len(ms_sorted) + if n < 4: + return list(rows), [] + q1 = ms_sorted[n // 4] + q3 = ms_sorted[(3 * n) // 4] + iqr = q3 - q1 + lo, hi = q1 - k * iqr, q3 + k * iqr + kept, dropped = [], [] + for d in rows: + ms = _runtime_key(d) + if ms <= 0 or lo <= ms <= hi: + kept.append(d) + else: + dropped.append(d) + return kept, dropped + + +def _pick(strategy, sorted_rows, n_pick): + if strategy == "center": + return _center_pick(sorted_rows, n_pick) + if strategy == "spread": + return _quintile_spread(sorted_rows, n_pick, N_BUCKETS) + raise ValueError(f"unknown sample strategy: {strategy!r}") + + +def _center_pick(sorted_rows, n_pick): + total = len(sorted_rows) + if total == 0 or n_pick <= 0: + return [] + if total <= n_pick: + return list(sorted_rows) + start = (total - n_pick) // 2 + return sorted_rows[start:start + n_pick] + + +def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): + total = len(sorted_rows) + if total == 0 or n_pick <= 0: + return [] + if total <= n_pick: + return list(sorted_rows) + per_bucket = n_pick // n_buckets + remainder = n_pick % n_buckets + picked = [] + for b in range(n_buckets): + lo = (b * total) // n_buckets + hi = ((b + 1) * total) // n_buckets + bucket = sorted_rows[lo:hi] + if not bucket: + continue + k = per_bucket + (1 if b < remainder else 0) + if k <= 0: + continue + if k == 1: + picked.append(bucket[len(bucket) // 2]) + else: + for j in range(k): + idx = round(j * (len(bucket) - 1) / (k - 1)) + picked.append(bucket[idx]) + return picked + + +def _summary(d): + return { + "sha": _id_key(d)[:12], + "baseline_result": _result_key(d), + "baseline_ms": _runtime_key(d), + } + + +def _write_sample(path, picks, label, criteria): + path.write_text( + json.dumps( + { + "selection": f"{len(picks)} {criteria}", + "source": str(_PROBLEMS.relative_to(_BENCH.parent)), + "sha256": [_id_key(d) for d in picks], + "summary": [_summary(d) for d in picks], + }, + indent=2, + ) + + "\n" + ) + print(f"wrote {path.relative_to(_BENCH.parent)} ({len(picks)} {label})") + + +def main(): + rows = _scan_raw() + if not rows: + raise SystemExit(f"no *.meta.jsonl found under {_RAW}") + print(f"scanned {len(rows)} problems") + + with open(_PROBLEMS, "w") as f: + for d in rows: + f.write(json.dumps(d) + "\n") + print(f"wrote {_PROBLEMS.relative_to(_BENCH.parent)} ({len(rows)} entries)") + + candidates = [d for d in rows if _runtime_key(d) <= MAX_BASELINE_MS] + print(f"sample pool: {len(candidates)} (skipped {len(rows) - len(candidates)} " + f"with baseline_ms > {MAX_BASELINE_MS}ms)") + + candidates, outliers = _drop_runtime_outliers(candidates) + if outliers: + print(f"dropped {len(outliers)} runtime outliers (Tukey IQR k={OUTLIER_IQR_K}):") + for d in sorted(outliers, key=_runtime_key): + print(f" {_id_key(d)[:12]} {int(_runtime_key(d)):>7}ms {_result_key(d)}") + + decided_by_rt = sorted( + (d for d in candidates if _result_key(d) in _DECISIVE_RESULTS), + key=_runtime_key, + ) + n_decided = len(decided_by_rt) + + def q_idx(i): + return (i * n_decided) // 5 + + pool_q12 = decided_by_rt[q_idx(0):q_idx(2)] + pool_q34 = decided_by_rt[q_idx(2):q_idx(4)] + pool_q5 = decided_by_rt[q_idx(4):q_idx(5)] + print(f"decisive-result runtime pool: {n_decided} | Q1+2={len(pool_q12)} | " + f"Q3+4={len(pool_q34)} | Q5={len(pool_q5)}") + + s1 = _pick(STAGE1_STRATEGY, pool_q12, STAGE1_N) + s2 = _pick(STAGE2_STRATEGY, pool_q34, STAGE2_N) + s3 = _pick(STAGE3_STRATEGY, pool_q5, STAGE3_N) + + # Stage4: broad spread across full decisive pool, dedup vs stage1-3. + used = {_id_key(d) for d in (s1 + s2 + s3)} + broad = sorted( + (d for d in candidates if _id_key(d) not in used), + key=_runtime_key, + ) + s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) + + _write_sample(_STAGE1, s1, "stage1", "decisive Q1+2 (fastest 40%)") + _write_sample(_STAGE2, s2, "stage2", "decisive Q3+4 (middle 40%)") + _write_sample(_STAGE3, s3, "stage3", "decisive Q5 (slowest 20%)") + _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") + + for label, picks in (("stage1", s1), ("stage2", s2), + ("stage3", s3), ("stage4", s4)): + print(f"\n{label}:") + for d in picks: + print(f" {_id_key(d)[:12]} " + f"{str(_result_key(d)):<10} " + f"{int(_runtime_key(d)):>7}ms") + + +if __name__ == "__main__": + main() diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml new file mode 100644 index 0000000000..9d3e6f0f07 --- /dev/null +++ b/input/cpsat-bench/evolve/config.yaml @@ -0,0 +1,139 @@ +# 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: 60 + - dir: phase2_presolve + iters: 100 + - dir: phase3_lp_cuts + iters: 80 + - dir: phase4_unified + iters: 40 + unified_prepare_script: prepare_phase_unified.py + solver_check_cmd: 'python -c "from ortools.sat.python import cp_model"' + solver_install_hint: "install: pip install ortools" + # optional script-name overrides (defaults shown): + # rebaseline_script: rebaseline_local.py + # extract_best_script: extract_best.py + # build_samples_script: build_samples.py + +# === 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. +# - Each pinned to a dedicated core via taskset (Linux). +# - Capped at len(problems_in_stage) at runtime. +# - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. +# - CP-SAT itself uses num_search_workers=8 internally (locked), so total +# core demand ≈ parallel_solvers × 8. Size accordingly. +parallel_solvers: 1 + +max_iterations: 60 +checkpoint_interval: 10 +log_level: "INFO" +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: 180 + retries: 2 + retry_delay: 10 + reasoning_effort: "medium" + - name: "claude-haiku-4-5" + provider: "claude_code" + weight: 0.2 + timeout: 120 + +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. + + 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. + + Hard rules: + - Do NOT modify locked keys: random_seed, num_search_workers, timeout_sec. + (evaluator returns combined_score=0 + locked_violated artifact on violation.) + - 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. + + 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, 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 — a 2x speedup with same conflict count + is hardware noise; a 2x with 10x fewer conflicts is a real find. + +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: 1800 # 30 min wall cap per variant — stage4 worst case + # ≈ 20 problems × 120s × 1.3 / parallel_solvers. + 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/extract_best.py b/input/cpsat-bench/evolve/extract_best.py new file mode 100644 index 0000000000..a7ad7446b1 --- /dev/null +++ b/input/cpsat-bench/evolve/extract_best.py @@ -0,0 +1,23 @@ +""" +Thin wrapper: calls _lib.extract_best.main with cpsat-bench phase map. +""" +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_SHARED = _HERE / "shared" +_INPUT_DIR = _HERE.parents[1] + +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) + +from _lib.extract_best import main # noqa: E402 + +PHASE_DIRS = { + 1: "phase1_search", + 2: "phase2_presolve", + 3: "phase3_lp_cuts", +} + +if __name__ == "__main__": + main(_HERE, _SHARED, PHASE_DIRS) 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..e8af25a38e --- /dev/null +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -0,0 +1,41 @@ +""" +Phase 1: tune CP-SAT search / subsolver knobs. + +Targeted namespace (LLM may add/remove/modify keys in the EVOLVE-BLOCK): + extra_subsolvers, ignore_subsolvers, interleave_search, + use_feasibility_jump, use_feasibility_pump + +Other params stay at BASELINE. +Do NOT modify locked keys (see baseline_params.LOCKED): + random_seed, num_search_workers, timeout_sec +Invalid solver keys cause evaluator to return 0 and surface the offending key. +""" +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 +SEARCH_OVERRIDES = { + "extra_subsolvers": ["default_lp", "no_lp"], + "ignore_subsolvers": ["max_lp"], + "interleave_search": True, + "use_feasibility_jump": False, + "use_feasibility_pump": False, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(SEARCH_OVERRIDES) + return p + + +def get_phase_overrides(): + """Used by extract_best.py — returns ONLY this phase's evolved dict.""" + return dict(SEARCH_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..d3049bfef5 --- /dev/null +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -0,0 +1,47 @@ +""" +Phase 2: tune CP-SAT presolve / probing knobs. + +Targeted namespace: + cp_model_probing_level, cp_model_presolve, symmetry_level, + presolve_use_bva, presolve_bve_threshold +(LLM may explore other presolve_* / probing_* keys.) + +Inherits phase1 winners (loaded from shared/phase1_best.json if present). +Do NOT modify locked keys. +""" +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 + + +def _load_prev(name): + p = _SHARED / name + if p.exists(): + return json.loads(p.read_text()) + return {} + + +_PHASE1 = _load_prev("phase1_best.json") + + +# EVOLVE-BLOCK-START +PRESOLVE_OVERRIDES = { + "cp_model_probing_level": 1, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(PRESOLVE_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(PRESOLVE_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..b13c67affb --- /dev/null +++ b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py @@ -0,0 +1,54 @@ +""" +Phase 3: tune CP-SAT LP / cuts / MIP-bridge knobs. + +Targeted namespace: + max_num_cuts, cut_level, + mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance +(LLM may explore other linearization_level, *_cuts toggles.) + +Inherits phase1+phase2 winners. +Do NOT modify locked keys. +""" +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 + + +def _load_prev(name): + p = _SHARED / name + if p.exists(): + return json.loads(p.read_text()) + return {} + + +_PHASE1 = _load_prev("phase1_best.json") +_PHASE2 = _load_prev("phase2_best.json") + + +# EVOLVE-BLOCK-START +LP_CUTS_OVERRIDES = { + "max_num_cuts": 3000, + "cut_level": 1, + "mip_max_bound": 1e+07, + "mip_var_scaling": 1, + "mip_check_precision": 1e-06, + "mip_drop_tolerance": 1e-07, +} +# EVOLVE-BLOCK-END + + +def get_params(): + p = dict(BASELINE) + p.update(_PHASE1) + p.update(_PHASE2) + p.update(LP_CUTS_OVERRIDES) + return p + + +def get_phase_overrides(): + return dict(LP_CUTS_OVERRIDES) 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..c1f2cfbadb --- /dev/null +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -0,0 +1,31 @@ +""" +Phase 4: unified refinement. + +EVOLVE-BLOCK below is auto-materialized by prepare_phase_unified.py from +the union of phase{1,2,3}_best.json winners. LLM may then tune all keys +jointly. + +Do NOT modify locked keys (random_seed, num_search_workers, timeout_sec). +""" +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 +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/input/cpsat-bench/evolve/prepare_phase_unified.py b/input/cpsat-bench/evolve/prepare_phase_unified.py new file mode 100644 index 0000000000..9a09e32b0a --- /dev/null +++ b/input/cpsat-bench/evolve/prepare_phase_unified.py @@ -0,0 +1,21 @@ +""" +Thin wrapper: calls _lib.prepare_phase.main with cpsat-bench phase config. +Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json. +""" +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_SHARED = _HERE / "shared" +_INPUT_DIR = _HERE.parents[1] +_UNIFIED_FILE = _HERE / "phase4_unified" / "initial_program.py" + +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) + +from _lib.prepare_phase import main # noqa: E402 + +PRIOR_PHASES = [1, 2, 3] + +if __name__ == "__main__": + main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE) diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py new file mode 100644 index 0000000000..3cae20787c --- /dev/null +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -0,0 +1,182 @@ +""" +Init-phase rebaseline: measure BASELINE on the union of +stage{1,2,3,4}_sample.json (on the local host, with current CP-SAT version) +and write shared/local_baseline.json. Captures elapsed_ms, stats, AND +objective_value — the last is critical for cost-mode scoring (variant cost +needs a baseline_obj to ratio against). + +Wall-clock varies by hardware / ortools version. raw-data timings were +recorded elsewhere; evaluator overlays this local file so per-problem +timeout = baseline_ms * 1.3 and speedup = local_baseline_ms / variant_ms +are calibrated for this box. + +Per-problem timeout = REBASELINE_TIMEOUT_S (1 hr safety floor). Never cut a +baseline run short — a truncated baseline poisons every variant comparison. + +Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). +""" +import json +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 / "shared")) + +from baseline_params import BASELINE # noqa: E402 +from cpsat_runner import run_cpsat # noqa: E402 +from runtime import parallel_solvers, core_range # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" +_STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" +_STAGE3_SAMPLE = _HERE / "shared" / "stage3_sample.json" +_STAGE4_SAMPLE = _HERE / "shared" / "stage4_sample.json" +_OUT = _HERE / "shared" / "local_baseline.json" + +REBASELINE_TIMEOUT_S = 3600 + + +def _load_problem_index(): + idx = {} + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + sha = d["problem_sha256"] + idx[sha] = { + "sha": sha, + "problem_filename": d["problem_filename"], + "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), + "raw_result": (d.get("cpsat_status") or {}).get("result"), + } + return idx + + +def _load_target_shas(): + if not _STAGE1_SAMPLE.exists(): + print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", + file=sys.stderr) + sys.exit(2) + ids = [] + seen = set() + for sample_path, label in ( + (_STAGE1_SAMPLE, "stage1"), + (_STAGE2_SAMPLE, "stage2"), + (_STAGE3_SAMPLE, "stage3"), + (_STAGE4_SAMPLE, "stage4"), + ): + if not sample_path.exists(): + print(f"WARN: {sample_path.name} missing — skipping {label}", file=sys.stderr) + continue + for sha in json.loads(sample_path.read_text())["sha256"]: + if sha not in seen: + ids.append(sha) + seen.add(sha) + return ids + + +def main(): + shas = _load_target_shas() + idx = _load_problem_index() + + 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 + path = _RAW_DIR / meta["problem_filename"] + if not path.exists(): + print(f"ERROR: input not found: {path}", file=sys.stderr) + return 2 + tasks.append((i, meta, path)) + + import queue as _queue + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(cores), len(tasks)) + cores = cores[:n_parallel] + print(f"rebaselining union of stage{{1,2,3,4}}_sample.json: {len(tasks)} problems") + print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (never cut short), " + f"parallel={n_parallel} cores={cores}") + print() + + _core_pool = _queue.Queue() + for _c in cores: + _core_pool.put(_c) + + def _solve(task): + i, meta, path = task + core = _core_pool.get() + try: + res = run_cpsat(path, BASELINE, REBASELINE_TIMEOUT_S, cpu_core=core) + finally: + _core_pool.put(core) + return i, meta, res, core + + t_start = time.monotonic() + completed = [] + if n_parallel == 1: + for task in tasks: + completed.append(_solve(task)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_solve, t) for t in tasks] + for fut in as_completed(futures): + completed.append(fut.result()) + completed.sort(key=lambda x: x[0]) + + out = {} + mismatch = 0 + for i, meta, res, core 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: + mismatch += 1 + + if invalid: + flag = f" INVALID_PARAM={invalid}" + elif ok: + flag = "" + else: + flag = " MISMATCH" + ratio = got_ms / max(meta["raw_ms"], 1) + print( + f" [{i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " + f"raw={meta['raw_result']:<10}/{int(meta['raw_ms']):>7}ms " + f"local={got_result:<10}/{got_ms:>7}ms ratio={ratio:.2f}x{flag} " + f"core={core}", + flush=True, + ) + + entry = { + "elapsed_ms": got_ms, + "result": got_result, + "matches_raw": ok, + "raw_elapsed_ms": meta["raw_ms"], + "stats": res.get("stats") or {}, + } + if "objective" in res: + entry["objective"] = res["objective"] + out[meta["sha"]] = entry + + elapsed = time.monotonic() - t_start + _OUT.write_text(json.dumps(out, indent=2) + "\n") + print() + print(f"wrote {_OUT.relative_to(_BENCH_DIR.parent)} " + f"({len(out)} entries, {mismatch} mismatches)") + print(f"total time: {elapsed:.1f}s") + if mismatch: + print(f"WARNING: {mismatch} problems had result mismatch — " + f"evaluator will keep raw_ms for those") + return 0 if mismatch == 0 else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py new file mode 100644 index 0000000000..6e05b14780 --- /dev/null +++ b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py @@ -0,0 +1,173 @@ +""" +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 + except ImportError as e: + emit({"result": "Unknown", "elapsed_ms": 0, + "error": f"ortools.sat.python.cp_model import: {e}"}) + return + + solver = cp_model.CpSolver() + + # Hard wall-clock cap on the solver side too. Parent subprocess.run adds + # an outer +15s grace. + try: + solver.parameters.max_time_in_seconds = float(timeout_s) + except Exception: + pass + + # 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(solver.parameters, k) + except AttributeError as e: + emit({"invalid_param": k, "error": str(e), + "result": "Unknown", "elapsed_ms": 0}) + return + 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(solver.parameters, 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 + + 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 + + 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/shared/baseline_params.py b/input/cpsat-bench/evolve/shared/baseline_params.py new file mode 100644 index 0000000000..ceac7d1717 --- /dev/null +++ b/input/cpsat-bench/evolve/shared/baseline_params.py @@ -0,0 +1,139 @@ +""" +Baseline CP-SAT parameters captured from raw-data/____seed0.meta.jsonl +(uniform `cpsat_applied_params` across all problems in package 71 instances). + +DO NOT MODIFY. Imported by all phases. + +Pkg 71 applied_params has 5 keys: timeout_sec, num_search_workers, random_seed, +interleave_search, tuned. `timeout_sec` and `tuned` are wrapper-level +(scheduler) — NOT real CpSolverParameters proto fields. Including them +would make every variant fail with invalid_param. Dropped from BASELINE +and from LOCKED; the worker also has both in its _DROP set as belt+braces. +""" + +BASELINE = { + "num_search_workers": 8, + "random_seed": 0, + "interleave_search": True, +} + +LOCKED = { + "random_seed": 0, + "num_search_workers": 8, +} + + +def _self_test(): + """ + Run stage1 problems with BASELINE in parallel; report per-problem ratio vs + recorded baseline_ms. + OK ratio in [0.5, 2.0] + WARN ratio out of band + FAIL result mismatch / 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 cpsat_runner import run_cpsat # noqa: E402 + from runtime import parallel_solvers, core_range # noqa: E402 + + bench = here.parent.parent + 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 + + ids = 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"]] = { + "problem_filename": d["problem_filename"], + "baseline_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), + "baseline_result": (d.get("cpsat_status") or {}).get("result"), + } + + tasks = [] + for i, pid in enumerate(ids): + meta = idx.get(pid) + if meta is None: + print(f"ERROR: {pid[:12]} not in problems.jsonl", file=sys.stderr) + return 2 + problem_path = raw_dir / meta["problem_filename"] + if not problem_path.exists(): + print(f"ERROR: input not found: {problem_path}", file=sys.stderr) + return 2 + tasks.append((i, pid, meta, problem_path)) + + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=5) + 1)) + n_parallel = min(len(cores), len(tasks)) + cores = cores[:n_parallel] + + print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel} " + f"cores={cores}") + print() + + def solve(t): + i, pid, meta, problem_path = t + timeout_s = max(30, math.ceil(meta["baseline_ms"] * 2 / 1000)) + core = cores[i % n_parallel] + r = run_cpsat(problem_path, BASELINE, timeout_s, cpu_core=core) + return i, pid, 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 + for i, pid, meta, r in results: + got_result = r.get("result", "Unknown") + got_ms = int(r.get("elapsed_ms", 0)) + 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"{pid[:12]:<14}{str(meta['baseline_result']):<10}{str(got_result):<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 + + +if __name__ == "__main__": + import sys + sys.exit(_self_test()) diff --git a/input/cpsat-bench/evolve/shared/cpsat_runner.py b/input/cpsat-bench/evolve/shared/cpsat_runner.py new file mode 100644 index 0000000000..86de9089c3 --- /dev/null +++ b/input/cpsat-bench/evolve/shared/cpsat_runner.py @@ -0,0 +1,24 @@ +""" +Thin wrapper: run_cpsat = subprocess_runner.run_solver bound to +_cpsat_solve_worker.py. + +Status strings produced by the worker: + OPTIMAL, FEASIBLE, INFEASIBLE, UNKNOWN, MODEL_INVALID +For cost mode, success dict also includes "objective" = solver.ObjectiveValue(). +""" +import pathlib +import sys + +_HERE = pathlib.Path(__file__).resolve().parent +_INPUT_DIR = _HERE.parents[2] +_WORKER = _HERE / "_cpsat_solve_worker.py" + +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) + +from _lib.subprocess_runner import run_solver # noqa: E402 + + +def run_cpsat(problem_path, params, timeout_s, python_bin=None, cpu_core=None): + return run_solver(_WORKER, problem_path, params, timeout_s, + python_bin=python_bin, cpu_core=cpu_core) diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py new file mode 100644 index 0000000000..fdf8d8472f --- /dev/null +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -0,0 +1,362 @@ +""" +OpenEvolve evaluator for cpsat-bench parameter tuning. + +Score mode: cost (minimize CP-SAT objective; see score.py). + +Per-problem timeout = max(MIN_TIMEOUT_S, ceil(baseline_ms * TIMEOUT_FACTOR / 1000)). +Locked params (see baseline_params.LOCKED) must not deviate — violation => combined_score=0. + +Environment overrides: + OPENEVOLVE_MAX_PROBLEMS cap stage problem count + OPENEVOLVE_PARALLEL_SOLVERS concurrent solver subprocesses (default 1) + OPENEVOLVE_PYTHON_BIN python for worker subprocess +""" +import importlib.util +import json +import math +import os +import pathlib +import sys +import traceback + +TIMEOUT_FACTOR = 1.3 +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 cpsat_runner import run_cpsat # noqa: E402 +from runtime import parallel_solvers, cascade_threshold, core_range # noqa: E402 + +from openevolve.evaluation_result import EvaluationResult # noqa: E402 + +_BENCH_DIR = _HERE.parent.parent +_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") + +_KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", "wall_time", "user_time") +_DECISIVE = ("OPTIMAL", "FEASIBLE") + + +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 _load_problems(): + 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.get("cpsat_status") or {}).get("elapsed_ms", 0) + baseline_result = (d.get("cpsat_status") or {}).get("result") + baseline_stats = d.get("cpsat_response_stats") or {} + baseline_objective = (d.get("cpsat_status") or {}).get("objective_value") + lo = local.get(sha) + 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["problem_filename"], + "baseline_ms": baseline_ms, + "baseline_result": baseline_result, + "baseline_stats": baseline_stats, + "baseline_objective": baseline_objective, + }) + return rows + + +def _filter_stage1(problems): + if not _STAGE1_SAMPLE.exists(): + return problems + keep = set(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) + return [p for p in problems if p["sha"] in keep] + + +def _filter_stage2(problems): + 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): + 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): + 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 locked keys (see baseline_params.LOCKED)."}, + ) + + if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: + problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] + + for p in problems: + input_path = _RAW_DIR / p["input_file"] + if not input_path.exists(): + return _err_result( + {"error": f"input not found: {p['input_file']}"}, + {"missing_file": str(input_path), "stage": stage_name}, + ) + + import queue as _queue + # Core pool: OPENEVOLVE_CORE_RANGE (e.g. "2-7") overrides; else + # cores 1..parallel_solvers() (core 0 reserved for kernel housekeeping). + _cores = core_range() + if _cores is None: + _cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(_cores), len(problems)) + _cores = _cores[:n_parallel] + _core_pool = _queue.Queue() + for _c in _cores: + _core_pool.put(_c) + + def _solve(idx_p): + idx, p = idx_p + input_path = _RAW_DIR / p["input_file"] + timeout_s = max(MIN_TIMEOUT_S, math.ceil(p["baseline_ms"] * TIMEOUT_FACTOR / 1000)) + core = _core_pool.get() + try: + r = run_cpsat(input_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): + # cost mode: baseline OPTIMAL → variant must reach OPTIMAL or FEASIBLE. + # bailing to UNKNOWN/INFEASIBLE/MODEL_INVALID on an OPTIMAL baseline = regression. + if "invalid_param" in r: + return False + if p["baseline_result"] not in _DECISIVE: + return False + return r.get("result") not in _DECISIVE + + def _invalid_err(r): + return _err_result( + {"error": f"invalid 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 feasibility on a problem baseline reached optimal. " + "Revert params that disable a needed subsolver or shorten search.", + }, + ) + + by_idx = {} + abort = None + if n_parallel == 1: + 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}", flush=True) + return _regression_err(p, r) + by_idx[idx] = (p, r) + else: + 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"(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) + + 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) + + metrics = score(results) + 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", {})}, + } + 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): + return _evaluate(program_path, _filter_stage1(_load_problems()), "stage1") + + +def evaluate_stage2(program_path): + return _evaluate(program_path, _filter_stage2(_load_problems()), "stage2") + + +def evaluate_stage3(program_path): + # openevolve cascade hardcodes 3 stages, so user-stage4 (broad runtime + # sample) is chained inside stage3 via the runtime cascade_threshold gate. + 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 + 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 cascade. + problems = _filter_stage4(_load_problems()) + return _evaluate(program_path, problems, "stage4") + + +def evaluate(program_path): + # Evolution loop entry: stage1 only. 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_rt.cascade_threshold(_CONFIG_YAML, index, default) diff --git a/input/cpsat-bench/evolve/shared/score.py b/input/cpsat-bench/evolve/shared/score.py new file mode 100644 index 0000000000..97937ed327 --- /dev/null +++ b/input/cpsat-bench/evolve/shared/score.py @@ -0,0 +1,136 @@ +""" +Scoring for cpsat-bench. + +Score mode: cost (minimize CP-SAT objective). + + combined = geomean(cost_ratio^COST_W * time_ratio) * solved_rate^2 * efficiency^STATS_WEIGHT + - both baseline+variant decisive (OPTIMAL or FEASIBLE) with objective values: + cost_ratio = (baseline_obj + EPS) / (variant_obj + EPS) [minimize] + time_ratio = baseline_ms / variant_ms + - status mismatch (e.g. variant UNKNOWN where baseline OPTIMAL): 1e-6 + - missing baseline_objective (rebaseline not yet run) falls back to time_ratio only + +Note: all 85 baseline runs in this dataset reach OPTIMAL, so when variant +ALSO reaches OPTIMAL, cost_ratio collapses to 1.0 and score reduces to +geomean(time_ratio). Cost mode mainly catches variants that bail to FEASIBLE +with a worse objective. + +Efficiency factor: cross-problem geomean of per-problem weighted geomean over +CP-SAT counters (num_conflicts, num_branches). Each ratio +(baseline+1)/(variant+1) clipped to [0.1, 10]. Lower variant work => efficiency > 1. + +Env overrides: + OPENEVOLVE_STATS_WEIGHT exponent on efficiency, default 0.333, 0 disables + OPENEVOLVE_COST_WEIGHT exponent on cost_ratio, default 1.0, 0 disables cost factor +""" +import math +import os + +_SCORE_MODE = "cost" + +_STATS_WEIGHTS = { + "num_conflicts": 2.0, + "num_branches": 1.5, +} +_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 + +_DECISIVE = ("OPTIMAL", "FEASIBLE") + + +def _efficiency(per_problem): + 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 _score_cost(per_problem): + cost_weight = float(os.environ.get("OPENEVOLVE_COST_WEIGHT", "1.0")) + cost_weight = max(0.0, min(cost_weight, 2.0)) + ratios = [] + solved = 0 + regressions = 0 + for p in per_problem: + b_ok = p["baseline_result"] in _DECISIVE + v_ok = p["result"] in _DECISIVE + b_cost = p.get("baseline_objective") + v_cost = p.get("objective") + time_r = p["baseline_ms"] / max(p["elapsed_ms"], 1) + if b_ok and 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) + else: + ratios.append(time_r) + else: + ratios.append(1e-6) + if b_ok and not v_ok: + regressions += 1 + geomean = math.exp(sum(math.log(r) for r in ratios) / len(ratios)) + solved_rate = solved / len(per_problem) + return geomean, solved_rate, solved, regressions + + +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, + } + + geomean, solved_rate, solved, regressions = _score_cost(per_problem) + + efficiency, eff_pairs = _efficiency(per_problem) + try: + stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0.333")) + except ValueError: + stats_weight = 0.0 + stats_weight = max(0.0, min(stats_weight, 2.0)) + + combined = geomean * (solved_rate ** 2.0) * (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/cpsat-bench/evolve/shared/stage1_sample.json b/input/cpsat-bench/evolve/shared/stage1_sample.json new file mode 100644 index 0000000000..c9618a84ad --- /dev/null +++ b/input/cpsat-bench/evolve/shared/stage1_sample.json @@ -0,0 +1,38 @@ +{ + "selection": "5 decisive Q1+2 (fastest 40%)", + "source": "cpsat-bench/problems.jsonl", + "sha256": [ + "0a450b93bbd0545e214c9a793193351e259a1e32771d669a198d0fba1efb72cf", + "99c5ac6d121d8b750f0bddfa010f32222a75f7d3c2235a87cc7883a876e3d654", + "bb9eb5259a99d62dd29494b30eecaa36ff1f6fe5698d4793423bef4137a7b1a9", + "71b18e5d4bd0b04f2c9fed4d07a1cb5c359ffd3fd7034036cedb8b182b135075", + "e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15" + ], + "summary": [ + { + "sha": "0a450b93bbd0", + "baseline_result": "OPTIMAL", + "baseline_ms": 791.232 + }, + { + "sha": "99c5ac6d121d", + 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"num_search_workers": 8, "random_seed": 0, "interleave_search": true, "tuned": true}, "cpsat_status": {"result": "OPTIMAL", "elapsed_ms": 3318.12}, "cpsat_response_stats": {"num_booleans": 6711, "num_conflicts": 4463, "num_branches": 93544, "num_binary_propagations": 3540206, "num_integer_propagations": 1562033, "num_restarts": 23, "wall_time": 3.31652, "user_time": 3.31652, "deterministic_time": 7.18218}, "solution_info": "no_lp", "problem_sha256": "ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047", "applied_params_hash": "56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024", "problem_filename": "ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 8580, "num_bool": 6402, "num_int": 2178, "num_constraints": 94129}, "cli_params": {"effort": "low", "seed": "0", "solver": "z3", "unsat_debug": "False", "m2_offset": "zero", "use_reboot": "True", "optimize_m1": 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"num_constraints": 19236}, "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": 668.915}, "cpsat_response_stats": {"num_booleans": 1392, "num_conflicts": 296, "num_branches": 6114, "num_binary_propagations": 388381, "num_integer_propagations": 188219, "num_restarts": 1, "wall_time": 0.667998, "user_time": 0.667998, "deterministic_time": 0.713344}, "solution_info": "quick_restart_no_lp", "problem_sha256": "f9e4e9cdb24462a34649e1e803328a7ce8332623aa743a6197d0c69af13b02d2", "applied_params_hash": "56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024", "problem_filename": "f9e4e9cdb24462a34649e1e803328a7ce8332623aa743a6197d0c69af13b02d2.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 1847, "num_bool": 1257, "num_int": 590, "num_constraints": 20644}, "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": 1364.16}, "cpsat_response_stats": {"num_booleans": 4262, "num_conflicts": 3410, "num_branches": 21289, "num_binary_propagations": 1326924, "num_integer_propagations": 489605, "num_restarts": 12, "wall_time": 1.36307, "user_time": 1.36307, "deterministic_time": 1.66269}, "solution_info": "no_lp", "problem_sha256": "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac", "applied_params_hash": "56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024", "problem_filename": "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac.cpsat.pb"} diff --git a/input/run_phase.sh b/input/run_phase.sh new file mode 100755 index 0000000000..83f09abd6b --- /dev/null +++ b/input/run_phase.sh @@ -0,0 +1,258 @@ +#!/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 +# standard flow: +# parse flags → rebaseline → solver check → openevolve-run → extract_best. +# +# Usage: +# ./input/run_phase.sh [] [--pin N-M] [--extract-only] +# [--iterations ] [extra flags] +# +# omitted → run ALL phases sequentially (1..N). +# numeric → run that single phase. +# +# Examples: +# ./input/run_phase.sh cpsat-bench # run all phases (1..4) +# ./input/run_phase.sh cpsat-bench --pin 2-7 # all phases, pinned +# ./input/run_phase.sh cpsat-bench 1 --pin 2-7 +# ./input/run_phase.sh z3-bench 4 +# ./input/run_phase.sh cpsat-bench 2 --extract-only +# +# Bench config (in /evolve/config.yaml under `bench:` key — see +# _lib/load_bench_config.py for schema). Loader exports these into env: +# PHASE_DIRS space-separated phase dir names, in order +# (last entry = "unified"/final phase, no extract step) +# PHASE_ITERS space-separated iter counts per phase (omitted => +# use config.yaml max_iterations only) +# UNIFIED_PREPARE_SCRIPT (optional) script name run before the last phase +# SOLVER_CHECK_CMD (optional) shell command returning 0 if solver works +# SOLVER_INSTALL_HINT (optional) message on solver check fail +# REBASELINE_SCRIPT (optional, default rebaseline_local.py) +# EXTRACT_BEST_SCRIPT (optional, default extract_best.py) +# BUILD_SAMPLES_SCRIPT (optional, default build_samples.py) +# +# Env overrides (any bench): +# SKIP_REBASELINE=1 reuse existing local_baseline.json +# OPENEVOLVE_PARALLEL_SOLVERS=N concurrent solver subprocesses +# OPENEVOLVE_CORE_RANGE=N-M taskset core range (also set via --pin) + +set -euo pipefail + +usage() { + echo "usage: $(basename "$0") [] [--pin N-M] [--extract-only] [--iterations N] [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 + +# Optional : numeric next arg. +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 $CONFIG_YAML" >&2 + exit 2 +fi + +# Load `bench:` section from config.yaml → exports PHASE_DIRS, PHASE_ITERS, +# UNIFIED_PREPARE_SCRIPT, SOLVER_CHECK_CMD, ... +_BENCH_EXPORTS="$(python "$INPUT_DIR/_lib/load_bench_config.py" "$CONFIG_YAML")" +eval "$_BENCH_EXPORTS" + +: "${PHASE_DIRS:?bench.phases missing in $CONFIG_YAML}" +REBASELINE_SCRIPT="${REBASELINE_SCRIPT:-rebaseline_local.py}" +EXTRACT_BEST_SCRIPT="${EXTRACT_BEST_SCRIPT:-extract_best.py}" +BUILD_SAMPLES_SCRIPT="${BUILD_SAMPLES_SCRIPT:-build_samples.py}" + +read -r -a _DIRS <<< "$PHASE_DIRS" +read -r -a _ITERS <<< "${PHASE_ITERS:-}" +N_PHASES=${#_DIRS[@]} +LAST_PHASE=$N_PHASES +PHASE_RANGE_STR="1..$N_PHASES" + +# Parse remaining flags +EXTRACT_ONLY=0 +PIN_RANGE="" +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 + ;; + -h|--help) + usage + exit 0 + ;; + *) + PASSTHROUGH+=("$1") + shift + ;; + esac +done +set -- "${PASSTHROUGH[@]+"${PASSTHROUGH[@]}"}" + +# Decide phase list +if [ -z "$PHASE" ]; then + if [ "$EXTRACT_ONLY" = "1" ]; then + echo "--extract-only requires explicit (refuses to bulk-aggregate all)" >&2 + exit 2 + fi + PHASES_TO_RUN=() + for ((i = 1; i <= N_PHASES; i++)); do + PHASES_TO_RUN+=("$i") + done + RUN_ALL=1 +else + if [ "$PHASE" -lt 1 ] || [ "$PHASE" -gt "$N_PHASES" ]; then + echo "phase must be in {$PHASE_RANGE_STR} (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" + echo "[run_phase] OPENEVOLVE_CORE_RANGE=$PIN_RANGE" +fi + +REPO_ROOT="$(cd "$INPUT_DIR/.." && pwd)" +RUNNER="$REPO_ROOT/openevolve-run.py" + +# ============ pre-flight (once, even in run-all mode) ============ + +if [ "$EXTRACT_ONLY" != "1" ]; then + if [ ! -f "$RUNNER" ]; then + echo "openevolve-run.py not found at $RUNNER" >&2 + exit 1 + fi + + if [ ! -f "$ROOT/shared/stage1_sample.json" ] || [ ! -f "$ROOT/shared/stage2_sample.json" ]; then + echo "[run_phase] sample json missing — running $BUILD_SAMPLES_SCRIPT first..." + python "$ROOT/$BUILD_SAMPLES_SCRIPT" + fi + + if [ -f "$ROOT/$REBASELINE_SCRIPT" ]; then + if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ -f "$ROOT/shared/local_baseline.json" ]; then + echo "[run_phase] SKIP_REBASELINE=1 — reusing existing local_baseline.json" + elif [ "${SKIP_REBASELINE:-0}" = "1" ]; then + echo "[run_phase] SKIP_REBASELINE=1 but local_baseline.json missing — running $REBASELINE_SCRIPT..." + python "$ROOT/$REBASELINE_SCRIPT" || \ + echo "warning: $REBASELINE_SCRIPT finished with mismatches; evaluator falls back to raw_ms for those." + else + echo "[run_phase] running $REBASELINE_SCRIPT (set SKIP_REBASELINE=1 to skip on re-runs)..." + python "$ROOT/$REBASELINE_SCRIPT" || \ + echo "warning: $REBASELINE_SCRIPT finished with mismatches; evaluator falls back to raw_ms for those." + fi + fi + + if [ -n "${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 + + # Re-using local_baseline across the phase loop is the right thing to do — + # force-set SKIP_REBASELINE=1 so the inner per-phase logic (if any) doesn't + # rerun. Currently the inner logic does NOT rerun (we did it above), but + # set anyway for any subscript that honors it. + export SKIP_REBASELINE=1 +fi + +# ============ phase runner (called per phase) ============ + +run_one_phase() { + local phase="$1" + local dir="${_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 (no extract step)" >&2 + return 2 + fi + echo "[run_phase] --extract-only phase $phase: aggregating from checkpoints..." + python "$ROOT/$EXTRACT_BEST_SCRIPT" "$phase" --from-checkpoints + return 0 + fi + + # Unified-prep on the last phase + if [ "$phase" -eq "$LAST_PHASE" ] && [ -n "${UNIFIED_PREPARE_SCRIPT:-}" ]; then + local missing=0 + for ((i = 1; i < LAST_PHASE; i++)); do + if [ ! -f "$ROOT/shared/phase${i}_best.json" ]; then + echo "phase $LAST_PHASE requires shared/phase${i}_best.json (run phase $i first)" >&2 + missing=1 + fi + done + [ "$missing" = "1" ] && return 1 + echo "[run_phase] materializing phase$LAST_PHASE EVOLVE-BLOCK via $UNIFIED_PREPARE_SCRIPT..." + python "$ROOT/$UNIFIED_PREPARE_SCRIPT" + fi + + cd "$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 "$iter") + fi + + python "$RUNNER" \ + initial_program.py \ + "$ROOT/shared/evaluator.py" \ + --config "$ROOT/config.yaml" \ + "${iter_flag[@]+"${iter_flag[@]}"}" \ + "$@" + + echo "[run_phase] phase $phase finished." + + if [ "$phase" -lt "$LAST_PHASE" ]; then + echo "[run_phase] extracting best overrides for phase $phase..." + python "$ROOT/$EXTRACT_BEST_SCRIPT" "$phase" + fi +} + +# ============ drive ============ + +for p in "${PHASES_TO_RUN[@]}"; do + run_one_phase "$p" "$@" +done + +if [ "$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/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index a2b041a796..b99bed10ae 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -1,6 +1,20 @@ -# Shared OpenEvolve config for all Z3 tuning phases. -# Per-phase iteration count is set in run_phase.sh. +# 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 + unified_prepare_script: prepare_phase4.py + solver_check_cmd: "command -v z3" + solver_install_hint: "install: apt-get install -y z3 or pip install z3-solver" + # === 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. diff --git a/input/z3-bench/evolve/extract_best.py b/input/z3-bench/evolve/extract_best.py index 109a5211a7..f7f611501f 100644 --- a/input/z3-bench/evolve/extract_best.py +++ b/input/z3-bench/evolve/extract_best.py @@ -1,24 +1,17 @@ """ -After phase N completes, extract get_phase_overrides() from its best_program.py -and write shared/phaseN_best.json. Phase N+1 auto-loads it. - -Default source: openevolve_output/best/best_program.py (created on normal phase -exit). For interrupted runs, pass --from-checkpoints to scan all -openevolve_output/checkpoints/checkpoint_*/ and pick the program with the -highest combined_score (read from best_program_info.json). - -Usage: - python extract_best.py - python extract_best.py --from-checkpoints +Thin wrapper: calls _lib.extract_best.main with z3-bench phase map. """ -import argparse -import importlib.util -import json import pathlib import sys -ROOT = pathlib.Path(__file__).resolve().parent -SHARED = ROOT / "shared" +_HERE = pathlib.Path(__file__).resolve().parent +_SHARED = _HERE / "shared" +_INPUT_DIR = _HERE.parents[1] + +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) + +from _lib.extract_best import main # noqa: E402 PHASE_DIRS = { 1: "phase1_opt_sls", @@ -26,92 +19,5 @@ 3: "phase3_smt", } - -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()) - score = 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 score > best_score: - best_score = score - 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 main(): - ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - ap.add_argument("phase", type=int, choices=[1, 2, 3], help="phase number") - ap.add_argument( - "--from-checkpoints", - action="store_true", - help="scan checkpoint_*/ dirs and pick highest combined_score " - "(use for interrupted runs without best/ dir)", - ) - args = ap.parse_args() - - n = args.phase - phase_dir = ROOT / PHASE_DIRS[n] - - if args.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__}, expected dict", file=sys.stderr) - sys.exit(1) - - out = SHARED / f"phase{n}_best.json" - out.write_text(json.dumps(overrides, indent=2, sort_keys=True) + "\n") - print(f"wrote {out.relative_to(ROOT)} ({len(overrides)} keys)") - - if __name__ == "__main__": - main() + main(_HERE, _SHARED, PHASE_DIRS) diff --git a/input/z3-bench/evolve/prepare_phase4.py b/input/z3-bench/evolve/prepare_phase4.py index 6583121468..0aa8996b7b 100644 --- a/input/z3-bench/evolve/prepare_phase4.py +++ b/input/z3-bench/evolve/prepare_phase4.py @@ -1,62 +1,21 @@ """ -Materialize phase4_unified/initial_program.py with a literal dict containing -the union of phase 1/2/3 winners (loaded from shared/phase{1,2,3}_best.json). - -Run AFTER phases 1, 2, 3 have completed (and extract_best.py ran for each). - -This rewrites only the EVOLVE-BLOCK section of phase4_unified/initial_program.py. +Thin wrapper: calls _lib.prepare_phase.main with z3-bench phase config. +Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json. """ -import json import pathlib -import pprint -import re import sys -ROOT = pathlib.Path(__file__).resolve().parent -SHARED = ROOT / "shared" -PHASE4_FILE = ROOT / "phase4_unified" / "initial_program.py" - -_EVOLVE_BLOCK_RE = re.compile( - r"(# EVOLVE-BLOCK-START\n).*?(# EVOLVE-BLOCK-END)", - re.DOTALL, -) - - -def _load(name): - f = SHARED / name - if not f.exists(): - print(f"missing: {f}", file=sys.stderr) - sys.exit(1) - return json.loads(f.read_text()) - +_HERE = pathlib.Path(__file__).resolve().parent +_SHARED = _HERE / "shared" +_INPUT_DIR = _HERE.parents[1] +_UNIFIED_FILE = _HERE / "phase4_unified" / "initial_program.py" -def main(): - p1 = _load("phase1_best.json") - p2 = _load("phase2_best.json") - p3 = _load("phase3_best.json") - merged = {} - merged.update(p1) - merged.update(p2) - merged.update(p3) - print(f"merged keys: p1={len(p1)} p2={len(p2)} p3={len(p3)} union={len(merged)}") +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) - src = PHASE4_FILE.read_text() - dict_repr = pprint.pformat(merged, width=100, sort_dicts=True) - new_block_body = ( - "# Auto-generated by prepare_phase4.py from union of phase 1/2/3 best.\n" - f"UNIFIED_OVERRIDES = {dict_repr}\n" - ) - new_src, n = _EVOLVE_BLOCK_RE.subn( - lambda m: m.group(1) + new_block_body + m.group(2), - src, - count=1, - ) - if n != 1: - print(f"EVOLVE-BLOCK markers not found in {PHASE4_FILE}", file=sys.stderr) - sys.exit(1) - PHASE4_FILE.write_text(new_src) - print(f"wrote {PHASE4_FILE.relative_to(ROOT)} ({len(merged)} keys in EVOLVE-BLOCK)") +from _lib.prepare_phase import main # noqa: E402 +PRIOR_PHASES = [1, 2, 3] if __name__ == "__main__": - main() + main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE) diff --git a/input/z3-bench/evolve/run_phase.sh b/input/z3-bench/evolve/run_phase.sh deleted file mode 100755 index 681e2abb36..0000000000 --- a/input/z3-bench/evolve/run_phase.sh +++ /dev/null @@ -1,110 +0,0 @@ -#!/bin/bash -# Run one phase of Z3 parameter evolution. -# Usage: -# ./run_phase.sh {1|2|3|4} [extra openevolve-run.py flags...] -# ./run_phase.sh {1|2|3} --extract-only # skip evolution; aggregate -# # results from existing -# # openevolve_output/checkpoints/ -# -# Iteration count comes from config.yaml (max_iterations). Override via -# `--iterations ` extra flag if needed. -# OpenEvolve outputs land in /openevolve_output/. - -set -euo pipefail - -if [ $# -lt 1 ]; then - echo "usage: $0 {1|2|3|4} [--extract-only] [extra flags]" >&2 - exit 2 -fi - -PHASE="$1" -shift - -EXTRACT_ONLY=0 -PASSTHROUGH=() -for arg in "$@"; do - case "$arg" in - --extract-only) EXTRACT_ONLY=1 ;; - *) PASSTHROUGH+=("$arg") ;; - esac -done -set -- "${PASSTHROUGH[@]+"${PASSTHROUGH[@]}"}" - -case "$PHASE" in - 1) DIR="phase1_opt_sls" ;; - 2) DIR="phase2_sat" ;; - 3) DIR="phase3_smt" ;; - 4) DIR="phase4_unified" ;; - *) echo "phase must be 1, 2, 3, or 4 (got $PHASE)" >&2; exit 2 ;; -esac - -ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -REPO_ROOT="$(cd "$ROOT/../../.." && pwd)" -RUNNER="$REPO_ROOT/openevolve-run.py" - -# --extract-only: skip evolution + setup, jump straight to aggregation. -if [ "$EXTRACT_ONLY" = "1" ]; then - if [ "$PHASE" -eq 4 ]; then - echo "--extract-only not supported for phase 4 (no extract step)" >&2 - exit 2 - fi - echo "[run_phase] --extract-only: aggregating from checkpoints (phase $PHASE)..." - python "$ROOT/extract_best.py" "$PHASE" --from-checkpoints - echo "[run_phase] extract-only done. next: $0 $((PHASE + 1))" - exit 0 -fi - -if [ ! -f "$RUNNER" ]; then - echo "openevolve-run.py not found at $RUNNER" >&2 - exit 1 -fi - -if [ ! -f "$ROOT/shared/stage1_sample.json" ] || [ ! -f "$ROOT/shared/stage2_sample.json" ]; then - echo "sample json missing — running build_samples.py first..." - python "$ROOT/build_samples.py" -fi - -if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ -f "$ROOT/shared/local_baseline.json" ]; then - echo "SKIP_REBASELINE=1 set — reusing existing local_baseline.json" -elif [ "${SKIP_REBASELINE:-0}" = "1" ]; then - echo "SKIP_REBASELINE=1 set but local_baseline.json missing — running rebaseline_local.py..." - python "$ROOT/rebaseline_local.py" || \ - echo "warning: rebaseline_local.py finished with mismatches; evaluator will fall back to raw_ms for those." -else - echo "running rebaseline_local.py (~5 min, 20 problems)... set SKIP_REBASELINE=1 to skip" - python "$ROOT/rebaseline_local.py" || \ - echo "warning: rebaseline_local.py finished with mismatches; evaluator will fall back to raw_ms for those." -fi - -if ! command -v z3 >/dev/null 2>&1; then - echo "warning: z3 binary not on PATH. install: apt-get install -y z3 or pip install z3-solver" >&2 -fi - -if [ "$PHASE" -eq 4 ]; then - if [ ! -f "$ROOT/shared/phase1_best.json" ] \ - || [ ! -f "$ROOT/shared/phase2_best.json" ] \ - || [ ! -f "$ROOT/shared/phase3_best.json" ]; then - echo "phase 4 requires phase 1/2/3 best.json files in shared/." >&2 - echo "run phases 1-3 first." >&2 - exit 1 - fi - echo "[run_phase] materializing phase4 EVOLVE-BLOCK..." - python "$ROOT/prepare_phase4.py" -fi - -cd "$ROOT/$DIR" -echo "[run_phase] phase=$PHASE dir=$DIR cwd=$(pwd) (iterations from config.yaml)" - -python "$RUNNER" \ - initial_program.py \ - "$ROOT/shared/evaluator.py" \ - --config "$ROOT/config.yaml" \ - "$@" - -echo "[run_phase] phase $PHASE finished." - -if [ "$PHASE" -lt 4 ]; then - echo "[run_phase] extracting best overrides..." - python "$ROOT/extract_best.py" "$PHASE" - echo "[run_phase] next: $0 $((PHASE + 1))" -fi diff --git a/input/z3-bench/evolve/shared/runtime.py b/input/z3-bench/evolve/shared/runtime.py index 1aeb1571f8..ba708c1055 100644 --- a/input/z3-bench/evolve/shared/runtime.py +++ b/input/z3-bench/evolve/shared/runtime.py @@ -1,66 +1,25 @@ """ -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. +Thin wrapper around input/_lib/runtime.py. +Binds config.yaml path; re-exports parallel_solvers / cascade_threshold / core_range. """ -import os import pathlib +import sys _HERE = pathlib.Path(__file__).resolve().parent -_CONFIG_YAML = _HERE.parent / "config.yaml" # input/z3-bench/evolve/config.yaml +_CONFIG_YAML = _HERE.parent / "config.yaml" +_INPUT_DIR = _HERE.parents[2] # input/ -_cache = None +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) +from _lib import runtime as _rt # noqa: E402 -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 +core_range = _rt.core_range 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 + return _rt.parallel_solvers(_CONFIG_YAML, default=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 + return _rt.cascade_threshold(_CONFIG_YAML, index, default) diff --git a/input/z3-bench/evolve/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json index 10c50a7c36..2f7e6d7d1f 100644 --- a/input/z3-bench/evolve/shared/stage1_sample.json +++ b/input/z3-bench/evolve/shared/stage1_sample.json @@ -2,20 +2,13 @@ "selection": "5 SAT runtime Q1+2 (fastest 40%)", "source": "z3-bench/problems.jsonl", "sha256": [ - "187551dceaf0c039b8f6f31e2dfcea9fe819db8317dc1d4564bf84fd38129500", "b6393502fdb5834104bcd66ed1a29e6150c036beb3c8a185f04a6341b3fd3102", + "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", "a4658d3a051a31bc6d19ea051195dacfaaea52a34530d63756eab51df3168f97", - "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07", - "a47edaf1aecb80c732ed45ddfc8a6f2a665f0ac175af4982158e7af567d60d1c" + "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", + "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07" ], "summary": [ - { - "sha": "187551dceaf0", - "num_hard_constraints": 24937, - "num_variables": 5752, - "baseline_result": "Sat", - "baseline_ms": 294 - }, { "sha": "b6393502fdb5", "num_hard_constraints": 25205, @@ -23,6 +16,13 @@ "baseline_result": "Sat", "baseline_ms": 312 }, + { + "sha": "17cc4d349ce3", + "num_hard_constraints": 15243, + "num_variables": 3932, + "baseline_result": "Sat", + "baseline_ms": 366 + }, { "sha": "a4658d3a051a", "num_hard_constraints": 25205, @@ -31,18 +31,18 @@ "baseline_ms": 597 }, { - "sha": "aa358daf4226", + "sha": "872cbdb83f73", "num_hard_constraints": 59387, "num_variables": 10244, "baseline_result": "Sat", - "baseline_ms": 1674 + "baseline_ms": 1298 }, { - "sha": "a47edaf1aecb", - "num_hard_constraints": 112978, - "num_variables": 17097, + "sha": "aa358daf4226", + "num_hard_constraints": 59387, + "num_variables": 10244, "baseline_result": "Sat", - "baseline_ms": 2343 + "baseline_ms": 1674 } ] } diff --git a/input/z3-bench/evolve/shared/stage2_sample.json b/input/z3-bench/evolve/shared/stage2_sample.json index 1d17490816..f49a539fa8 100644 --- a/input/z3-bench/evolve/shared/stage2_sample.json +++ b/input/z3-bench/evolve/shared/stage2_sample.json @@ -2,20 +2,13 @@ "selection": "5 SAT runtime Q3+4 (middle 40%)", "source": "z3-bench/problems.jsonl", "sha256": [ - "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", "6aab51935a092a1d7f05bb431d3d05e0de50c19f5f983026a6d14e7c4b9cf240", + "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", - "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c" + "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", + "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12" ], "summary": [ - { - "sha": "8a304b9c461a", - "num_hard_constraints": 112796, - "num_variables": 17043, - "baseline_result": "Sat", - "baseline_ms": 3053 - }, { "sha": "6aab51935a09", "num_hard_constraints": 113172, @@ -24,25 +17,32 @@ "baseline_ms": 3681 }, { - "sha": "069af7891076", + "sha": "75ee534dbc92", "num_hard_constraints": 105784, "num_variables": 32912, "baseline_result": "Sat", - "baseline_ms": 13885 + "baseline_ms": 13086 }, { - "sha": "4a02d7846394", + "sha": "069af7891076", "num_hard_constraints": 105784, "num_variables": 32912, "baseline_result": "Sat", - "baseline_ms": 14428 + "baseline_ms": 13885 + }, + { + "sha": "318bc251e0bd", + "num_hard_constraints": 106100, + "num_variables": 33068, + "baseline_result": "Sat", + "baseline_ms": 14232 }, { - "sha": "03b7e129d625", + "sha": "493242c37992", "num_hard_constraints": 106100, "num_variables": 33068, "baseline_result": "Sat", - "baseline_ms": 16518 + "baseline_ms": 14353 } ] } diff --git a/input/z3-bench/evolve/shared/stage3_sample.json b/input/z3-bench/evolve/shared/stage3_sample.json index d9954ac298..720a79a9c9 100644 --- a/input/z3-bench/evolve/shared/stage3_sample.json +++ b/input/z3-bench/evolve/shared/stage3_sample.json @@ -6,7 +6,7 @@ "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44", "5250d459bef23286b67be03e7abec394ceb5d337f40885df8ac4e76eb48d33f8", "86efb0762e6d99d9ade8f6718be01a366a53704b6ee199bf0e91af310c68d6f3", - "76bcc496430708686148bd23c53bb24a2b4e9a6d1f28adb8b2e072b47871b3ea" + "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" ], "summary": [ { @@ -38,11 +38,11 @@ "baseline_ms": 80512 }, { - "sha": "76bcc4964307", - "num_hard_constraints": 696736, - "num_variables": 83621, + "sha": "a7df812c620d", + "num_hard_constraints": 696365, + "num_variables": 83551, "baseline_result": "Sat", - "baseline_ms": 112662 + "baseline_ms": 93445 } ] } diff --git a/input/z3-bench/evolve/shared/stage4_sample.json b/input/z3-bench/evolve/shared/stage4_sample.json index 81a1bbd6f4..5dfe3cfec6 100644 --- a/input/z3-bench/evolve/shared/stage4_sample.json +++ b/input/z3-bench/evolve/shared/stage4_sample.json @@ -5,23 +5,23 @@ "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", + "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", - "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", + "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678", "21069961e8dc3386828e2608f2ece27db08b11be6e2da9ebadbc40675687e1fb", + "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84", - "42bc61a62698412bbad582db74dd0c9d92710d69f358348ae993deaf2e8d2c08", - "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", + "29efe6d38d7b8fcf41ed69b73f5a0bb370f782ab61ffe564fbe41512ea75ce5f", "65444d69388120f98e21ea06a1cf388e621dbc77318828bd673832e90963c568", - "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", - "7ac5a84f68bc6e1488f9c6074a42505a6c95fa8df4f615c4a0ae54359dd3b416", + "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", + "86468fd861ffa391b567ca57dc57c9b95b8577942d3fd2587e244073bdee43ef", "f54dba052fbf975e36b4f6dae5b47802965275f27cc276cd8a6199f763a7b8a4", "3b58bd47434b81258f5d6b182dde28df64a155b70a2aa790c4d608ec8f11ab65", "c5d46b1e315a5c5e6cf895acac137541641d31b9bc77909ede85575c6876b0db", "be1c308d797798dce2f3663ad9790822ce632331f6d9c7309a738ab8e438804e", - "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" + "76bcc496430708686148bd23c53bb24a2b4e9a6d1f28adb8b2e072b47871b3ea" ], "summary": [ { @@ -45,6 +45,13 @@ "baseline_result": "Unsat", "baseline_ms": 268 }, + { + "sha": "505a2e36055c", + "num_hard_constraints": 24937, + "num_variables": 5752, + "baseline_result": "Sat", + "baseline_ms": 307 + }, { "sha": "4fb703b12127", "num_hard_constraints": 15034, @@ -52,13 +59,6 @@ "baseline_result": "Unsat", "baseline_ms": 311 }, - { - "sha": "17cc4d349ce3", - "num_hard_constraints": 15243, - "num_variables": 3932, - "baseline_result": "Sat", - "baseline_ms": 366 - }, { "sha": "133383a624ef", "num_hard_constraints": 24797, @@ -67,11 +67,11 @@ "baseline_ms": 480 }, { - "sha": "872cbdb83f73", - "num_hard_constraints": 59387, - "num_variables": 10244, + "sha": "aeec9f77b092", + "num_hard_constraints": 112978, + "num_variables": 17097, "baseline_result": "Sat", - "baseline_ms": 1298 + "baseline_ms": 2193 }, { "sha": "0f6f9aa28440", @@ -87,6 +87,13 @@ "baseline_result": "Sat", "baseline_ms": 2900 }, + { + "sha": "8a304b9c461a", + "num_hard_constraints": 112796, + "num_variables": 17043, + "baseline_result": "Sat", + "baseline_ms": 3053 + }, { "sha": "d8d5ffa7d824", "num_hard_constraints": 112812, @@ -95,39 +102,32 @@ "baseline_ms": 3350 }, { - "sha": "42bc61a62698", + "sha": "29efe6d38d7b", "num_hard_constraints": 105784, "num_variables": 32912, "baseline_result": "Unsat", - "baseline_ms": 12692 + "baseline_ms": 12712 }, { - "sha": "75ee534dbc92", + "sha": "65444d693881", "num_hard_constraints": 105784, "num_variables": 32912, - "baseline_result": "Sat", - "baseline_ms": 13086 + "baseline_result": "Unsat", + "baseline_ms": 13089 }, { - "sha": "65444d693881", + "sha": "4a02d7846394", "num_hard_constraints": 105784, "num_variables": 32912, - "baseline_result": "Unsat", - "baseline_ms": 13089 + "baseline_result": "Sat", + "baseline_ms": 14428 }, { - "sha": "318bc251e0bd", + "sha": "86468fd861ff", "num_hard_constraints": 106100, "num_variables": 33068, "baseline_result": "Sat", - "baseline_ms": 14232 - }, - { - "sha": "7ac5a84f68bc", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Unsat", - "baseline_ms": 14992 + "baseline_ms": 15671 }, { "sha": "f54dba052fbf", @@ -158,11 +158,11 @@ "baseline_ms": 33752 }, { - "sha": "a7df812c620d", - "num_hard_constraints": 696365, - "num_variables": 83551, + "sha": "76bcc4964307", + "num_hard_constraints": 696736, + "num_variables": 83621, "baseline_result": "Sat", - "baseline_ms": 93445 + "baseline_ms": 112662 } ] } diff --git a/input/z3-bench/evolve/shared/z3_runner.py b/input/z3-bench/evolve/shared/z3_runner.py index 7113fe0441..69c71a52a9 100644 --- a/input/z3-bench/evolve/shared/z3_runner.py +++ b/input/z3-bench/evolve/shared/z3_runner.py @@ -1,97 +1,27 @@ """ -Run a single SMT2 file through Z3 with given parameters. +Thin wrapper: run_z3 = subprocess_runner.run_solver bound to _z3_solve_worker.py. -Implementation: spawn `_z3_solve_worker.py` as a subprocess and communicate -via stdout JSON. 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. +Worker uses the z3 Python binding (z3.set_param + Optimize/Solver). Python +binding is preferred over CLI because z3 4.13.x CLI rejects globals like +`threads`, `parallel.enable`, `sls.parallel` as positional `key=value`, +which the binding accepts. Matches the original benchmark's +`applied_params_hash 543b29...` setup. -Subprocess isolation gives: hard wall-clock timeout, crash containment, -memory reclaim between problems. +Status strings: "Sat" / "Unsat" / "Unknown". """ -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 +_HERE = pathlib.Path(__file__).resolve().parent +_INPUT_DIR = _HERE.parents[2] +_WORKER = _HERE / "_z3_solve_worker.py" +if str(_INPUT_DIR) not in sys.path: + sys.path.insert(0, str(_INPUT_DIR)) -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": {}, - } +from _lib.subprocess_runner import run_solver # noqa: E402 - 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 +def run_z3(smt2_path, params, timeout_s, python_bin=None, cpu_core=None): + return run_solver(_WORKER, smt2_path, params, timeout_s, + python_bin=python_bin, cpu_core=cpu_core) From 9f789df4045cb72f107137570a6461063a65880f Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 20 May 2026 18:56:26 +0900 Subject: [PATCH 16/42] fix core allocation error --- input/README.md | 16 +++++++++----- input/run_phase.sh | 1 + input/z3-bench/evolve/rebaseline_local.py | 20 +++++++++-------- .../z3-bench/evolve/shared/baseline_params.py | 17 ++++++++------ input/z3-bench/evolve/shared/evaluator.py | 22 +++++++++---------- 5 files changed, 44 insertions(+), 32 deletions(-) diff --git a/input/README.md b/input/README.md index 4025d76a26..44c3f2f81c 100644 --- a/input/README.md +++ b/input/README.md @@ -9,21 +9,22 @@ boilerplate parts). ``` input/ -├── run_phase.sh # single entry: ./run_phase.sh [flags] +├── run_phase.sh # single entry: ./run_phase.sh [] [flags] ├── _lib/ # shared library (importable as `_lib.*`) │ ├── runtime.py # parallel_solvers / cascade_threshold / core_range │ ├── subprocess_runner.py # run_solver(worker, problem, params, timeout, ...) │ ├── extract_best.py # main(root, shared, phase_dirs) — CLI core -│ └── prepare_phase.py # main(root, shared, prior_phases, unified_file) +│ ├── prepare_phase.py # main(root, shared, prior_phases, unified_file) +│ └── load_bench_config.py # parse `bench:` section of config.yaml → bash exports ├── z3-bench/ │ ├── raw-data/ # .smt2 + ____seed0.meta.jsonl │ └── evolve/ -│ ├── bench.env # config sourced by ../../run_phase.sh +│ ├── config.yaml # openevolve + `bench:` (phases / scripts / solver check) │ └── ... # baseline_params, evaluator, phases, etc. └── cpsat-bench/ ├── raw-data/ # .cpsat.pb + ____seed0.meta.jsonl └── evolve/ - ├── bench.env + ├── config.yaml # same └── ... ``` @@ -35,7 +36,7 @@ input/ | `_lib/subprocess_runner.py` | spawn worker subprocess, taskset pin, timeout grace, parse stdout JSON | `/evolve/shared/_runner.py` (binds WORKER path, names `run_`) | | `_lib/extract_best.py` | argparse + load best_program.py + write `phaseN_best.json` | `/evolve/extract_best.py` (passes `PHASE_DIRS` dict) | | `_lib/prepare_phase.py` | merge `phaseN_best.json` files, rewrite unified phase's EVOLVE-BLOCK | `/evolve/prepare_phase_unified.py` (passes `PRIOR_PHASES` list) | -| `input/run_phase.sh` | single user-facing entry: `./run_phase.sh [--pin] [--extract-only] [--iterations]`. Resolves bench, sources `/evolve/bench.env`, runs flow. | `/evolve/bench.env` (5-line config: `PHASE_DIRS`, `PHASE_ITERS`, `UNIFIED_PREPARE_SCRIPT`, `SOLVER_CHECK_CMD`, `SOLVER_INSTALL_HINT`) | +| `input/run_phase.sh` | single user-facing entry: `./run_phase.sh [] [--pin] [--extract-only] [--iterations]`. Phase optional → runs ALL phases sequentially. Reads `bench:` section of `/evolve/config.yaml` via `_lib/load_bench_config.py`. | `/evolve/config.yaml` — `bench:` key with `phases[].dir/iters`, `unified_prepare_script`, `solver_check_cmd`, `solver_install_hint` | ## Per-bench files NOT shared @@ -102,6 +103,11 @@ Same pattern for `_runner.py`, `extract_best.py`, `prepare_phase_unified bash input/cpsat-bench/raw-data/load_script.sh # populate raw-data python input/cpsat-bench/evolve/build_samples.py # stage{1..4}_sample.json python input/cpsat-bench/evolve/shared/baseline_params.py # sanity self-test + +# Run all phases (1..N) with one command: +./input/run_phase.sh cpsat-bench --pin 2-7 + +# Or step phase-by-phase: ./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 diff --git a/input/run_phase.sh b/input/run_phase.sh index 83f09abd6b..f33181ca4c 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -192,6 +192,7 @@ fi run_one_phase() { local phase="$1" + shift local dir="${_DIRS[$((phase - 1))]}" local iter="" if [ "${#_ITERS[@]}" -ge "$phase" ]; then diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py index 294a4159e9..babea9b3ab 100644 --- a/input/z3-bench/evolve/rebaseline_local.py +++ b/input/z3-bench/evolve/rebaseline_local.py @@ -32,7 +32,7 @@ from baseline_params import BASELINE # noqa: E402 from z3_runner import run_z3 # noqa: E402 -from runtime import parallel_solvers # noqa: E402 +from runtime import parallel_solvers, core_range # noqa: E402 _BENCH_DIR = _HERE.parent _RAW_DIR = _BENCH_DIR / "raw-data" @@ -109,20 +109,22 @@ def main(): tasks.append((i, meta, smt2_path)) import queue as _queue - n_parallel = min(parallel_solvers(default=1), len(tasks)) + # Cores leased from a queue so each in-flight task holds a unique slot. + # OPENEVOLVE_CORE_RANGE (e.g. "1-5") overrides; else cores 1..N from + # parallel_solvers (core 0 reserved for kernel housekeeping). + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(cores), len(tasks)) + cores = cores[:n_parallel] print(f"rebaselining stage1+stage2+stage3+stage4 samples: {len(tasks)} problems " f"(union of stage{{1,2,3,4}}_sample.json)") print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (effectively unbounded " - f"— never cut a baseline run short), parallel={n_parallel} (taskset core pin)") + f"— never cut a baseline run short), parallel={n_parallel} cores={cores}") print() - # Cores leased from a queue so each in-flight task holds a unique slot. - # 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 measurement matches the pin envelope variants - # will see during evolve. _core_pool = _queue.Queue() - for _c in range(1, n_parallel + 1): + for _c in cores: _core_pool.put(_c) def _solve(task): diff --git a/input/z3-bench/evolve/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py index e731c4469d..27e7a04796 100644 --- a/input/z3-bench/evolve/shared/baseline_params.py +++ b/input/z3-bench/evolve/shared/baseline_params.py @@ -62,7 +62,7 @@ def _self_test(): 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 + from runtime import parallel_solvers, core_range # noqa: E402 bench = here.parent.parent # input/z3-bench/ raw_dir = bench / "raw-data" @@ -97,18 +97,21 @@ def _self_test(): return 2 tasks.append((i, sha, meta, smt2)) - n_parallel = min(parallel_solvers(default=5), len(tasks)) + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=5) + 1)) + n_parallel = min(len(cores), len(tasks)) + cores = cores[:n_parallel] - print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel}, " - f"taskset core pin") + print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel} " + f"cores={cores}") 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 + core = cores[i % n_parallel] r = run_z3(smt2, BASELINE, timeout_s, cpu_core=core) return i, sha, meta, r @@ -147,7 +150,7 @@ def solve(t): 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}") + f"{cores[i % n_parallel]:>4} {status}") seq_estimate = sum_got_ms / 1000 print() diff --git a/input/z3-bench/evolve/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py index 5481d66f0f..e03857c54b 100644 --- a/input/z3-bench/evolve/shared/evaluator.py +++ b/input/z3-bench/evolve/shared/evaluator.py @@ -50,7 +50,7 @@ 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 runtime import parallel_solvers, cascade_threshold, core_range # noqa: E402 from openevolve.evaluation_result import EvaluationResult # noqa: E402 @@ -218,18 +218,18 @@ def _evaluate(program_path, problems, 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). + # Cores leased from a queue.Queue so each in-flight task holds a unique + # slot (idx % n_parallel would collide when len(problems) > n_parallel). + # OPENEVOLVE_CORE_RANGE (e.g. "1-5") overrides; else cores + # 1..parallel_solvers() (core 0 reserved for kernel housekeeping). import queue as _queue - n_parallel = min(parallel_solvers(default=1), len(problems)) + _cores = core_range() + if _cores is None: + _cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(_cores), len(problems)) + _cores = _cores[:n_parallel] _core_pool = _queue.Queue() - for _c in range(1, n_parallel + 1): + for _c in _cores: _core_pool.put(_c) def _solve(idx_p): From 493918502556468b44dda7fb59856fb23ca6ada2 Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 21 May 2026 12:09:47 +0900 Subject: [PATCH 17/42] add verify on cpsat --- input/README.md | 5 + input/cpsat-bench/evolve/final_verify.py | 240 +++++++++++++++++++++++ 2 files changed, 245 insertions(+) create mode 100644 input/cpsat-bench/evolve/final_verify.py diff --git a/input/README.md b/input/README.md index 44c3f2f81c..ca56bcbf47 100644 --- a/input/README.md +++ b/input/README.md @@ -112,4 +112,9 @@ python input/cpsat-bench/evolve/shared/baseline_params.py # sanity self-test ./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 + +# Final verify on the unified winner (fresh LOCAL baseline per problem): +python input/cpsat-bench/evolve/final_verify.py \ + input/cpsat-bench/evolve/phase4_unified/openevolve_output/best/best_program.py +# (writes final_verify.json next to best_program.py) ``` diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py new file mode 100644 index 0000000000..556f6ffda0 --- /dev/null +++ b/input/cpsat-bench/evolve/final_verify.py @@ -0,0 +1,240 @@ +""" +Final verification for cpsat-bench: on a final-test sample, measure LOCAL +baseline elapsed_ms (+ objective_value) and then run the optimized program. +Report per-problem cost_ratio + time speedup using the fresh local baseline +(not the raw-data baseline recorded on a different machine). + +Usage: + python final_verify.py + +Example: + python input/cpsat-bench/evolve/final_verify.py \\ + input/cpsat-bench/evolve/phase4_unified/openevolve_output/best/best_program.py + +Sample selection (in priority order): + 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. + Hand-edit or generate this to pin a specific subset for verification. + 2. Fall back to ALL problems in problems.jsonl. + +Order of operations: + for each problem p in final sample: + run BASELINE on p → record base_ms_local, base_obj_local + run params on p → record variant_ms, variant_obj + cost_ratio = base_obj_local / variant_obj (cost mode, minimize) + speedup = base_ms_local / variant_ms (when status decisive) + +Baseline + variant are run back-to-back per problem so they share the same +warm cache / system noise. Concurrency = config parallel_solvers (taskset +pinned via OPENEVOLVE_CORE_RANGE or 1..N). +""" +import importlib.util +import json +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 / "shared")) + +from baseline_params import BASELINE, LOCKED # noqa: E402 +from score import score # noqa: E402 +from cpsat_runner import run_cpsat # noqa: E402 +from runtime import parallel_solvers, core_range # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" + +TIMEOUT_S = 300 +_DECISIVE = ("OPTIMAL", "FEASIBLE") + + +def _load_get_params(program_path): + spec = importlib.util.spec_from_file_location("program", program_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if not hasattr(module, "get_params"): + print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) + sys.exit(2) + return module.get_params() + + +def _load_problem_index(): + idx = {} + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + idx[d["problem_sha256"]] = { + "sha": d["problem_sha256"], + "problem_filename": d["problem_filename"], + "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), + "raw_result": (d.get("cpsat_status") or {}).get("result"), + } + return idx + + +def _resolve_sample(idx): + if _FINAL_SAMPLE.exists(): + shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) + source = f"shared/final_sample.json ({len(shas)} SHAs)" + else: + shas = list(idx.keys()) + source = f"problems.jsonl (full {len(shas)})" + metas = [] + for sha in shas: + meta = idx.get(sha) + if meta is None: + print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) + sys.exit(2) + pb = _RAW_DIR / meta["problem_filename"] + if not pb.exists(): + print(f"ERROR: missing {pb}", file=sys.stderr) + sys.exit(2) + metas.append((meta, pb)) + return metas, source + + +def main(): + if len(sys.argv) != 2: + print(__doc__, file=sys.stderr) + return 2 + + program_path = pathlib.Path(sys.argv[1]).resolve() + if not program_path.exists(): + print(f"ERROR: {program_path} not found", file=sys.stderr) + return 2 + + variant_params = _load_get_params(program_path) + violations = {k: variant_params.get(k) for k in LOCKED + if variant_params.get(k) != LOCKED[k]} + if violations: + print(f"ERROR: locked params violated: {violations}", file=sys.stderr) + return 2 + + idx = _load_problem_index() + metas, source = _resolve_sample(idx) + + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(cores), len(metas)) + cores = cores[:n_parallel] + + print(f"final verify: {program_path}") + print(f" sample : {source}") + print(f" params : {len(variant_params)} keys, " + f"{sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} differ from BASELINE") + print(f" parallel solvers : {n_parallel} cores={cores}") + print(f" per-problem timeout : {TIMEOUT_S}s × 2 (baseline + variant)") + print() + + def _measure(idx_meta): + i, meta, pb = idx_meta + core = cores[i % n_parallel] if n_parallel > 0 else None + b = run_cpsat(pb, BASELINE, TIMEOUT_S, cpu_core=core) + v = run_cpsat(pb, variant_params, TIMEOUT_S, cpu_core=core) + return i, meta, b, v + + tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] + t_start = time.monotonic() + completed = [] + if n_parallel == 1: + for t in tasks: + completed.append(_measure(t)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_measure, t) for t in tasks] + for fut in as_completed(futures): + completed.append(fut.result()) + completed.sort(key=lambda x: x[0]) + elapsed = time.monotonic() - t_start + + results = [] + for i, meta, b, v in completed: + base_ms_local = int(b.get("elapsed_ms", 0)) + base_result = b.get("result", "Unknown") + base_obj = b.get("objective") + var_ms = int(v.get("elapsed_ms", 0)) + var_result = v.get("result", "Unknown") + var_obj = v.get("objective") + var_invalid = v.get("invalid_param") + + if var_invalid: + flag = f" invalid={var_invalid}" + speedup = 0.0 + cost_ratio = 0.0 + elif var_result not in _DECISIVE or base_result not in _DECISIVE: + flag = f" STATUS_MISMATCH (base={base_result} variant={var_result})" + speedup = 0.0 + cost_ratio = 0.0 + else: + speedup = base_ms_local / max(var_ms, 1) + if base_obj is not None and var_obj is not None: + cost_ratio = (float(base_obj) + 1e-9) / (float(var_obj) + 1e-9) + else: + cost_ratio = 1.0 + flag = "" + + print( + f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} " + f"base_local={base_result:<10}/{base_ms_local:>7}ms" + f"{('/obj=' + format(base_obj, '.3g')) if base_obj is not None else ''} " + f"variant={var_result:<10}/{var_ms:>7}ms" + f"{('/obj=' + format(var_obj, '.3g')) if var_obj is not None else ''} " + f"speedup={speedup:.2f}x cost_ratio={cost_ratio:.3f}{flag}", + flush=True, + ) + results.append({ + "sha": meta["sha"], + "input_file": meta["problem_filename"], + "baseline_ms": base_ms_local, + "baseline_result": base_result, + "baseline_objective": base_obj, + "result": var_result, + "elapsed_ms": var_ms, + "objective": var_obj, + "timeout": bool(v.get("timeout")), + "raw_baseline_ms": meta["raw_ms"], + "stats": v.get("stats") or {}, + "baseline_stats": b.get("stats") or {}, + }) + + metrics = score(results) + print() + print("== summary (cost-mode, vs fresh LOCAL baseline) ==") + print(f" solved : {metrics['solved']}/{metrics['total']}") + print(f" regressions : {metrics['regressions']}") + print(f" geomean (cost×time) : {metrics['geomean_speedup']:.3f}") + print(f" solved_rate : {metrics['solved_rate']:.3f}") + print(f" efficiency : {metrics.get('efficiency', 1.0):.3f}") + print(f" combined_score : {metrics['combined_score']:.3f}") + print(f" wall-clock : {elapsed:.1f}s") + + out_path = program_path.parent / "final_verify.json" + out_path.write_text(json.dumps({ + "program": str(program_path), + "sample_source": source, + "metrics": metrics, + "per_problem": [ + { + "sha": r["sha"][:12], + "base_result": r["baseline_result"], + "got_result": r["result"], + "base_local_ms": r["baseline_ms"], + "variant_ms": r["elapsed_ms"], + "base_obj": r["baseline_objective"], + "variant_obj": r["objective"], + "raw_baseline_ms": r["raw_baseline_ms"], + "timeout": r["timeout"], + } + for r in results + ], + }, indent=2) + "\n") + print(f" wrote {out_path}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) From da666068d96043d87f4d34e61b6bb08e4b1c1e8e Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 21 May 2026 13:25:04 +0900 Subject: [PATCH 18/42] update regression score logic --- input/cpsat-bench/evolve/final_verify.py | 31 ++++++++++++++++----- input/cpsat-bench/evolve/shared/score.py | 34 +++++++++++++++++++----- 2 files changed, 52 insertions(+), 13 deletions(-) diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py index 556f6ffda0..c2a2603009 100644 --- a/input/cpsat-bench/evolve/final_verify.py +++ b/input/cpsat-bench/evolve/final_verify.py @@ -161,16 +161,27 @@ def _measure(idx_meta): var_obj = v.get("objective") var_invalid = v.get("invalid_param") + speedup = base_ms_local / max(var_ms, 1) if var_invalid: - flag = f" invalid={var_invalid}" + flag = f" INVALID_PARAM={var_invalid}" speedup = 0.0 cost_ratio = 0.0 - elif var_result not in _DECISIVE or base_result not in _DECISIVE: - flag = f" STATUS_MISMATCH (base={base_result} variant={var_result})" + elif base_result not in _DECISIVE and var_result not in _DECISIVE: + # Both failed to reach decisive status — uncomparable, NOT a regression. + # score.py skips this from geomean. + flag = f" BOTH_NONDECISIVE ({base_result}=base, {var_result}=variant) — skipped from score" + cost_ratio = 0.0 + elif base_result in _DECISIVE and var_result not in _DECISIVE: + # Baseline solved, variant didn't → real regression. + flag = f" REGRESSION (base={base_result} variant={var_result})" speedup = 0.0 cost_ratio = 0.0 + elif base_result not in _DECISIVE and var_result in _DECISIVE: + # Variant solved a problem baseline couldn't — bonus, but cost mode + # can't ratio it. Show but don't score. + flag = f" VARIANT_WIN_UNCOMPARABLE (base={base_result})" + cost_ratio = 0.0 else: - speedup = base_ms_local / max(var_ms, 1) if base_obj is not None and var_obj is not None: cost_ratio = (float(base_obj) + 1e-9) / (float(var_obj) + 1e-9) else: @@ -204,10 +215,16 @@ def _measure(idx_meta): metrics = score(results) print() print("== summary (cost-mode, vs fresh LOCAL baseline) ==") - print(f" solved : {metrics['solved']}/{metrics['total']}") - print(f" regressions : {metrics['regressions']}") + print(f" total problems : {metrics['total']}") + print(f" comparable : {metrics['comparable']} " + f"(baseline reached decisive status; geomean computed over this subset)") + print(f" uncomparable : {metrics['uncomparable']} " + f"(baseline non-decisive; skipped from score)") + print(f" solved : {metrics['solved']}/{metrics['comparable']}") + print(f" regressions : {metrics['regressions']} " + f"(baseline OK, variant failed)") print(f" geomean (cost×time) : {metrics['geomean_speedup']:.3f}") - print(f" solved_rate : {metrics['solved_rate']:.3f}") + print(f" solved_rate : {metrics['solved_rate']:.3f} (over comparable)") print(f" efficiency : {metrics.get('efficiency', 1.0):.3f}") print(f" combined_score : {metrics['combined_score']:.3f}") print(f" wall-clock : {elapsed:.1f}s") diff --git a/input/cpsat-bench/evolve/shared/score.py b/input/cpsat-bench/evolve/shared/score.py index 97937ed327..714020c930 100644 --- a/input/cpsat-bench/evolve/shared/score.py +++ b/input/cpsat-bench/evolve/shared/score.py @@ -69,18 +69,35 @@ def _efficiency(per_problem): def _score_cost(per_problem): + """ + Cost mode scoring rules: + - baseline NOT decisive → uncomparable; skip from geomean entirely. + (Baseline never reached OPTIMAL/FEASIBLE within timeout → variant has + no target to beat. Counting it as 1e-6 unfairly tanks the geomean.) + - baseline decisive + variant decisive → ratio = cost_ratio^W * time_ratio + (or just time_ratio if either objective_value is unknown). + - baseline decisive + variant NOT decisive → 1e-6 + regression++ (real loss). + + Returns: (geomean, solved_rate, solved, regressions, comparable) + comparable = problems where baseline was decisive (i.e. counted in geomean). + solved_rate is over `comparable` (not the total input list). + """ cost_weight = float(os.environ.get("OPENEVOLVE_COST_WEIGHT", "1.0")) cost_weight = max(0.0, min(cost_weight, 2.0)) ratios = [] solved = 0 regressions = 0 + comparable = 0 for p in per_problem: b_ok = p["baseline_result"] in _DECISIVE + if not b_ok: + continue # uncomparable + comparable += 1 v_ok = p["result"] in _DECISIVE b_cost = p.get("baseline_objective") v_cost = p.get("objective") time_r = p["baseline_ms"] / max(p["elapsed_ms"], 1) - if b_ok and v_ok: + 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) @@ -90,11 +107,12 @@ def _score_cost(per_problem): ratios.append(time_r) else: ratios.append(1e-6) - if b_ok and not v_ok: - regressions += 1 + regressions += 1 + if not ratios: + return 1.0, 0.0, 0, 0, 0 geomean = math.exp(sum(math.log(r) for r in ratios) / len(ratios)) - solved_rate = solved / len(per_problem) - return geomean, solved_rate, solved, regressions + solved_rate = solved / comparable + return geomean, solved_rate, solved, regressions, comparable def score(per_problem): @@ -106,13 +124,15 @@ def score(per_problem): "solved_rate": 0.0, "regressions": 0, "solved": 0, + "comparable": 0, "total": 0, + "uncomparable": 0, "efficiency": 1.0, "efficiency_pairs": 0, "stats_weight": 0.0, } - geomean, solved_rate, solved, regressions = _score_cost(per_problem) + geomean, solved_rate, solved, regressions, comparable = _score_cost(per_problem) efficiency, eff_pairs = _efficiency(per_problem) try: @@ -129,7 +149,9 @@ def score(per_problem): "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), From 89950c88708a301e483e92651d40a7c4ea4c2af6 Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 21 May 2026 14:25:50 +0900 Subject: [PATCH 19/42] add final bench --- input/cpsat-bench/evolve/benchmark_final.py | 340 ++++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 input/cpsat-bench/evolve/benchmark_final.py diff --git a/input/cpsat-bench/evolve/benchmark_final.py b/input/cpsat-bench/evolve/benchmark_final.py new file mode 100644 index 0000000000..d111fc08e5 --- /dev/null +++ b/input/cpsat-bench/evolve/benchmark_final.py @@ -0,0 +1,340 @@ +""" +Repeated-trial benchmark of BASELINE vs final-best on the main test sample. + +For each problem in the sample, run BASELINE N times AND the optimized +program's params N times (default 20 each, back-to-back on the same core +to share warm-cache / system-noise envelope). Aggregate mean / median / +stddev / min / max per case and report speedup of variant vs baseline. + +Parallelism comes from config.yaml `parallel_solvers` (env +OPENEVOLVE_PARALLEL_SOLVERS overrides) or OPENEVOLVE_CORE_RANGE. Each +problem is pinned to one core; the 40 runs for that problem stay serial +on that core. Different problems run concurrently across cores. + +Usage: + python benchmark_final.py [--iters N] [--timeout S] + +Outputs (next to ): + benchmark_final.json summary metrics + benchmark_final_iterations.csv long-form: per-run row + benchmark_final_summary.csv per-problem aggregates + +Sample selection (same as final_verify.py): + 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. + 2. Fall back to ALL problems in problems.jsonl. +""" +import argparse +import csv +import importlib.util +import json +import pathlib +import statistics +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed + +_HERE = pathlib.Path(__file__).resolve().parent +sys.path.insert(0, str(_HERE / "shared")) + +from baseline_params import BASELINE, LOCKED # noqa: E402 +from cpsat_runner import run_cpsat # noqa: E402 +from runtime import parallel_solvers, core_range # noqa: E402 + +_BENCH_DIR = _HERE.parent +_RAW_DIR = _BENCH_DIR / "raw-data" +_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" +_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" + +_DEFAULT_ITERS = 20 +_DEFAULT_TIMEOUT_S = 300 +_DECISIVE = ("OPTIMAL", "FEASIBLE") + + +def _load_get_params(program_path): + spec = importlib.util.spec_from_file_location("program", program_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if not hasattr(module, "get_params"): + print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) + sys.exit(2) + return module.get_params() + + +def _load_problem_index(): + idx = {} + with open(_PROBLEMS_JSONL) as f: + for line in f: + d = json.loads(line) + idx[d["problem_sha256"]] = { + "sha": d["problem_sha256"], + "problem_filename": d["problem_filename"], + "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), + "raw_result": (d.get("cpsat_status") or {}).get("result"), + } + return idx + + +def _resolve_sample(idx): + if _FINAL_SAMPLE.exists(): + shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) + source = f"shared/final_sample.json ({len(shas)} SHAs)" + else: + shas = list(idx.keys()) + source = f"problems.jsonl (full {len(shas)})" + metas = [] + for sha in shas: + meta = idx.get(sha) + if meta is None: + print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) + sys.exit(2) + pb = _RAW_DIR / meta["problem_filename"] + if not pb.exists(): + print(f"ERROR: missing {pb}", file=sys.stderr) + sys.exit(2) + metas.append((meta, pb)) + return metas, source + + +def _aggregate(runs): + """runs = list of dicts {elapsed_ms, result, objective?, timeout, ...}. + Returns aggregate stats over only the decisive runs (others as 'failed'). + """ + decisive = [r for r in runs if r["result"] in _DECISIVE] + ms_list = [r["elapsed_ms"] for r in decisive] + obj_list = [r["objective"] for r in decisive + if r.get("objective") is not None] + agg = { + "n_total": len(runs), + "n_decisive": len(decisive), + "n_failed": len(runs) - len(decisive), + } + if ms_list: + agg["mean_ms"] = statistics.mean(ms_list) + agg["median_ms"] = statistics.median(ms_list) + agg["min_ms"] = min(ms_list) + agg["max_ms"] = max(ms_list) + agg["stddev_ms"] = statistics.stdev(ms_list) if len(ms_list) > 1 else 0.0 + else: + agg.update({"mean_ms": 0, "median_ms": 0, "min_ms": 0, "max_ms": 0, "stddev_ms": 0.0}) + if obj_list: + agg["mean_obj"] = statistics.mean(obj_list) + agg["min_obj"] = min(obj_list) + agg["max_obj"] = max(obj_list) + return agg + + +def _run_repeat(problem_path, params, n, timeout_s, core, tag): + runs = [] + for i in range(n): + r = run_cpsat(problem_path, params, timeout_s, cpu_core=core) + runs.append({ + "iter": i + 1, + "elapsed_ms": int(r.get("elapsed_ms", 0)), + "result": r.get("result", "Unknown"), + "objective": r.get("objective"), + "timeout": bool(r.get("timeout")), + "invalid_param": r.get("invalid_param"), + "error": r.get("error"), + }) + return runs + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("program", help="path to best_program.py (must expose get_params())") + ap.add_argument("--iters", type=int, default=_DEFAULT_ITERS, + help=f"runs per (problem, variant) pair (default {_DEFAULT_ITERS})") + ap.add_argument("--timeout", type=int, default=_DEFAULT_TIMEOUT_S, + help=f"per-run timeout in seconds (default {_DEFAULT_TIMEOUT_S})") + args = ap.parse_args() + + program_path = pathlib.Path(args.program).resolve() + if not program_path.exists(): + print(f"ERROR: {program_path} not found", file=sys.stderr) + return 2 + + variant_params = _load_get_params(program_path) + violations = {k: variant_params.get(k) for k in LOCKED + if variant_params.get(k) != LOCKED[k]} + if violations: + print(f"ERROR: locked params violated: {violations}", file=sys.stderr) + return 2 + + idx = _load_problem_index() + metas, source = _resolve_sample(idx) + + cores = core_range() + if cores is None: + cores = list(range(1, parallel_solvers(default=1) + 1)) + n_parallel = min(len(cores), len(metas)) + cores = cores[:n_parallel] + + n_iters = args.iters + timeout_s = args.timeout + + n_total_runs = len(metas) * n_iters * 2 + print(f"benchmark final: {program_path}") + print(f" sample : {source}") + print(f" iters per case : {n_iters} baseline + {n_iters} variant = {n_iters * 2}") + print(f" total runs : {len(metas)} problems × {n_iters * 2} = {n_total_runs}") + print(f" per-run timeout : {timeout_s}s") + print(f" parallel solvers: {n_parallel} cores={cores}") + print(f" params diff : {sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} keys differ from BASELINE") + print() + + def _worker(idx_meta): + i, meta, pb = idx_meta + core = cores[i % n_parallel] + t0 = time.monotonic() + baseline_runs = _run_repeat(pb, BASELINE, n_iters, timeout_s, core, "baseline") + variant_runs = _run_repeat(pb, variant_params, n_iters, timeout_s, core, "variant") + dt = time.monotonic() - t0 + return i, meta, baseline_runs, variant_runs, core, dt + + tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] + t_start = time.monotonic() + completed = [] + if n_parallel == 1: + for t in tasks: + completed.append(_worker(t)) + i, meta, b_runs, v_runs, core, dt = completed[-1] + b_agg = _aggregate(b_runs) + v_agg = _aggregate(v_runs) + sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + print(f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} core={core} " + f"base_mean={b_agg['mean_ms']:>7.0f}ms (n_ok={b_agg['n_decisive']}/{n_iters}) " + f"variant_mean={v_agg['mean_ms']:>7.0f}ms (n_ok={v_agg['n_decisive']}/{n_iters}) " + f"speedup={sp:.2f}x (took {dt:.1f}s)", + flush=True) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_worker, t) for t in tasks] + for fut in as_completed(futures): + completed.append(fut.result()) + i, meta, b_runs, v_runs, core, dt = completed[-1] + b_agg = _aggregate(b_runs) + v_agg = _aggregate(v_runs) + sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + print(f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} core={core} " + f"base_mean={b_agg['mean_ms']:>7.0f}ms (n_ok={b_agg['n_decisive']}/{n_iters}) " + f"variant_mean={v_agg['mean_ms']:>7.0f}ms (n_ok={v_agg['n_decisive']}/{n_iters}) " + f"speedup={sp:.2f}x (took {dt:.1f}s)", + flush=True) + completed.sort(key=lambda x: x[0]) + wall = time.monotonic() - t_start + + # ----- write CSVs ----- + out_dir = program_path.parent + iter_csv = out_dir / "benchmark_final_iterations.csv" + summary_csv = out_dir / "benchmark_final_summary.csv" + json_path = out_dir / "benchmark_final.json" + + with open(iter_csv, "w", newline="") as f: + w = csv.writer(f) + w.writerow(["sha", "variant", "iter", "elapsed_ms", "result", + "objective", "timeout", "invalid_param", "error"]) + for _, meta, b_runs, v_runs, _, _ in completed: + sha = meta["sha"] + for r in b_runs: + w.writerow([sha, "baseline", r["iter"], r["elapsed_ms"], r["result"], + r["objective"] if r["objective"] is not None else "", + int(r["timeout"]), + r["invalid_param"] or "", + (r["error"] or "")[:200]]) + for r in v_runs: + w.writerow([sha, "variant", r["iter"], r["elapsed_ms"], r["result"], + r["objective"] if r["objective"] is not None else "", + int(r["timeout"]), + r["invalid_param"] or "", + (r["error"] or "")[:200]]) + + summary_rows = [] + for _, meta, b_runs, v_runs, _, _ in completed: + b_agg = _aggregate(b_runs) + v_agg = _aggregate(v_runs) + mean_sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + med_sp = (b_agg["median_ms"] / max(v_agg["median_ms"], 1)) if v_agg["median_ms"] else 0 + summary_rows.append({ + "sha": meta["sha"], + "n_iters": n_iters, + "baseline_n_decisive": b_agg["n_decisive"], + "baseline_mean_ms": round(b_agg["mean_ms"], 1), + "baseline_median_ms": round(b_agg["median_ms"], 1), + "baseline_min_ms": round(b_agg["min_ms"], 1), + "baseline_max_ms": round(b_agg["max_ms"], 1), + "baseline_stddev_ms": round(b_agg["stddev_ms"], 1), + "variant_n_decisive": v_agg["n_decisive"], + "variant_mean_ms": round(v_agg["mean_ms"], 1), + "variant_median_ms": round(v_agg["median_ms"], 1), + "variant_min_ms": round(v_agg["min_ms"], 1), + "variant_max_ms": round(v_agg["max_ms"], 1), + "variant_stddev_ms": round(v_agg["stddev_ms"], 1), + "mean_speedup": round(mean_sp, 3), + "median_speedup": round(med_sp, 3), + }) + + with open(summary_csv, "w", newline="") as f: + w = csv.DictWriter(f, fieldnames=list(summary_rows[0].keys())) + w.writeheader() + w.writerows(summary_rows) + + # ----- overall aggregate ----- + # speedup geomean over problems with both means valid + import math + valid = [r for r in summary_rows + if r["baseline_mean_ms"] > 0 and r["variant_mean_ms"] > 0 + and r["baseline_n_decisive"] > 0 and r["variant_n_decisive"] > 0] + if valid: + geo_mean = math.exp(sum(math.log(r["mean_speedup"]) for r in valid) / len(valid)) + geo_med = math.exp(sum(math.log(r["median_speedup"]) for r in valid) / len(valid)) + arith_mean = sum(r["mean_speedup"] for r in valid) / len(valid) + else: + geo_mean = geo_med = arith_mean = 0.0 + + n_baseline_failed = sum(r["baseline_n_decisive"] < n_iters for r in summary_rows) + n_variant_failed = sum(r["variant_n_decisive"] < n_iters for r in summary_rows) + n_baseline_all_failed = sum(r["baseline_n_decisive"] == 0 for r in summary_rows) + n_variant_all_failed = sum(r["variant_n_decisive"] == 0 for r in summary_rows) + + json_path.write_text(json.dumps({ + "program": str(program_path), + "sample_source": source, + "iters_per_case": n_iters, + "timeout_s": timeout_s, + "n_parallel": n_parallel, + "cores": cores, + "wall_clock_s": round(wall, 1), + "n_problems": len(metas), + "summary": { + "geomean_speedup_mean": round(geo_mean, 3), + "geomean_speedup_median": round(geo_med, 3), + "arith_mean_speedup": round(arith_mean, 3), + "n_problems_with_valid_speedup": len(valid), + "n_baseline_partial_fail": n_baseline_failed, + "n_baseline_total_fail": n_baseline_all_failed, + "n_variant_partial_fail": n_variant_failed, + "n_variant_total_fail": n_variant_all_failed, + }, + "per_problem": summary_rows, + }, indent=2) + "\n") + + print() + print("== overall (mean-of-means across problems) ==") + print(f" problems with valid speedup : {len(valid)}/{len(metas)}") + print(f" geomean mean-speedup : {geo_mean:.3f}x") + print(f" geomean median-speedup : {geo_med:.3f}x") + print(f" arith mean-speedup : {arith_mean:.3f}x") + print(f" baseline partial-fail cases : {n_baseline_failed} (≥1 of {n_iters} runs non-decisive)") + print(f" baseline total-fail cases : {n_baseline_all_failed} (all {n_iters} runs non-decisive)") + print(f" variant partial-fail cases : {n_variant_failed}") + print(f" variant total-fail cases : {n_variant_all_failed}") + print(f" wall-clock : {wall:.1f}s") + print() + print(f" wrote {iter_csv}") + print(f" wrote {summary_csv}") + print(f" wrote {json_path}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) From 22d0a99f938d0f848dc6d371ecaeafe833024f6b Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 21 May 2026 16:39:56 +0900 Subject: [PATCH 20/42] sample clustering --- input/cpsat-bench/evolve/build_samples.py | 90 +++++++++++++++---- .../evolve/shared/stage1_sample.json | 28 +++--- .../evolve/shared/stage2_sample.json | 32 +++---- .../evolve/shared/stage3_sample.json | 32 +++---- .../evolve/shared/stage4_sample.json | 12 +-- 5 files changed, 126 insertions(+), 68 deletions(-) diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index 032205e9ee..b788df8651 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -12,9 +12,12 @@ boundaries don't get distorted by long-tail monsters. Stages (decisive = OPTIMAL or FEASIBLE; this dataset is all OPTIMAL): - stage1 (5) center pick from runtime Q1+Q2 (fastest 40%) - stage2 (5) center pick from runtime Q3+Q4 (middle 40%) - stage3 (5) center pick from runtime Q5 (slowest 20%) + Runtime is clustered into N_BUCKETS via 1D k-means (Lloyd's). Clusters + are ordered by ascending centroid (c1=fastest, c5=slowest), then merged: + + stage1 (5) center pick from clusters c1+c2 (fast group) + stage2 (5) center pick from clusters c3+c4 (mid group) + stage3 (5) center pick from cluster c5 (slow group) stage4 (20) quintile-spread broad sample, dedup vs stage1-3 """ import json @@ -108,6 +111,54 @@ def _center_pick(sorted_rows, n_pick): return sorted_rows[start:start + n_pick] +def _cluster_runtime(sorted_rows, k, max_iter=100): + """1D Lloyd's k-means on runtime. Input must be runtime-sorted ascending. + Returns list of k buckets (lists of rows), ordered by ascending centroid.""" + n = len(sorted_rows) + if k <= 0: + return [] + if n == 0: + return [[] for _ in range(k)] + if n <= k: + buckets = [[r] for r in sorted_rows] + buckets += [[] for _ in range(k - n)] + return buckets + + values = [_runtime_key(d) for d in sorted_rows] + # init centroids at k-quantile midpoints over the sorted values. + centroids = [values[((2 * i + 1) * n) // (2 * k)] for i in range(k)] + labels = [0] * n + + for _ in range(max_iter): + changed = False + for i, v in enumerate(values): + 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 i, v in enumerate(values): + sums[labels[i]] += v + counts[labels[i]] += 1 + for c in range(k): + if counts[c] > 0: + centroids[c] = sums[c] / counts[c] + + order = sorted(range(k), key=lambda c: centroids[c]) + rank = {old: new for new, old in enumerate(order)} + buckets = [[] for _ in range(k)] + for i, lbl in enumerate(labels): + buckets[rank[lbl]].append(sorted_rows[i]) + return buckets + + def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): total = len(sorted_rows) if total == 0 or n_pick <= 0: @@ -186,18 +237,25 @@ def main(): ) n_decided = len(decided_by_rt) - def q_idx(i): - return (i * n_decided) // 5 + clusters = _cluster_runtime(decided_by_rt, N_BUCKETS) + pool_c12 = clusters[0] + clusters[1] + pool_c34 = clusters[2] + clusters[3] + pool_c5 = clusters[4] + + def _bucket_range(b): + if not b: + return "empty" + return f"{int(_runtime_key(b[0]))}-{int(_runtime_key(b[-1]))}ms" - pool_q12 = decided_by_rt[q_idx(0):q_idx(2)] - pool_q34 = decided_by_rt[q_idx(2):q_idx(4)] - pool_q5 = decided_by_rt[q_idx(4):q_idx(5)] - print(f"decisive-result runtime pool: {n_decided} | Q1+2={len(pool_q12)} | " - f"Q3+4={len(pool_q34)} | Q5={len(pool_q5)}") + print(f"decisive-result runtime pool: {n_decided} | clusters: " + + " | ".join(f"c{i+1}({len(b)},{_bucket_range(b)})" + for i, b in enumerate(clusters))) + print(f"stage pools: c1+2={len(pool_c12)} | c3+4={len(pool_c34)} | " + f"c5={len(pool_c5)}") - s1 = _pick(STAGE1_STRATEGY, pool_q12, STAGE1_N) - s2 = _pick(STAGE2_STRATEGY, pool_q34, STAGE2_N) - s3 = _pick(STAGE3_STRATEGY, pool_q5, STAGE3_N) + s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) + s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) + s3 = _pick(STAGE3_STRATEGY, pool_c5, STAGE3_N) # Stage4: broad spread across full decisive pool, dedup vs stage1-3. used = {_id_key(d) for d in (s1 + s2 + s3)} @@ -207,9 +265,9 @@ def q_idx(i): ) s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) - _write_sample(_STAGE1, s1, "stage1", "decisive Q1+2 (fastest 40%)") - _write_sample(_STAGE2, s2, "stage2", "decisive Q3+4 (middle 40%)") - _write_sample(_STAGE3, s3, "stage3", "decisive Q5 (slowest 20%)") + _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") + _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") + _write_sample(_STAGE3, s3, "stage3", "decisive runtime cluster c5 (slow group)") _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") for label, picks in (("stage1", s1), ("stage2", s2), diff --git a/input/cpsat-bench/evolve/shared/stage1_sample.json b/input/cpsat-bench/evolve/shared/stage1_sample.json index c9618a84ad..1ae95ce24a 100644 --- a/input/cpsat-bench/evolve/shared/stage1_sample.json +++ b/input/cpsat-bench/evolve/shared/stage1_sample.json @@ -1,24 +1,14 @@ { - "selection": "5 decisive Q1+2 (fastest 40%)", + "selection": "5 decisive runtime clusters c1+c2 (fast group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ - "0a450b93bbd0545e214c9a793193351e259a1e32771d669a198d0fba1efb72cf", - "99c5ac6d121d8b750f0bddfa010f32222a75f7d3c2235a87cc7883a876e3d654", "bb9eb5259a99d62dd29494b30eecaa36ff1f6fe5698d4793423bef4137a7b1a9", "71b18e5d4bd0b04f2c9fed4d07a1cb5c359ffd3fd7034036cedb8b182b135075", - "e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15" + "e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15", + "aec821f70ed62b536c07791cb8cb7cc48661194d53fe27e8b65b139c13aec39a", + "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac" ], "summary": [ - { - "sha": "0a450b93bbd0", - "baseline_result": "OPTIMAL", - "baseline_ms": 791.232 - }, - { - "sha": "99c5ac6d121d", - "baseline_result": "OPTIMAL", - "baseline_ms": 798.026 - }, { "sha": "bb9eb5259a99", "baseline_result": "OPTIMAL", @@ -33,6 +23,16 @@ "sha": "e848603289cc", "baseline_result": "OPTIMAL", "baseline_ms": 1251.32 + }, + { + "sha": "aec821f70ed6", + "baseline_result": "OPTIMAL", + "baseline_ms": 1317.35 + }, + { + "sha": "fa619c63ddf9", + "baseline_result": "OPTIMAL", + "baseline_ms": 1364.16 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage2_sample.json b/input/cpsat-bench/evolve/shared/stage2_sample.json index 3395ba5f88..80ea8e73c8 100644 --- a/input/cpsat-bench/evolve/shared/stage2_sample.json +++ b/input/cpsat-bench/evolve/shared/stage2_sample.json @@ -1,38 +1,38 @@ { - "selection": "5 decisive Q3+4 (middle 40%)", + "selection": "5 decisive runtime clusters c3+c4 (mid group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ - "66754667bf567d1f18152078c6781cbd453cccac416c9d06db5087e6a10c51f0", - "47888d1b56a42fa20c5c9457a9527199b1d09ec2b5528820a793234e19f5328e", - "ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047", - "9f330e3d7528eea46bbfbd640e4dc0f7d732bf1fdc1dd9e39c323406b9cede01", - "a27f90201b577988626385f9254e8b786910326e6e8fb7b6cb1544a744743db8" + "b6027761fc923f4b03b977ca7530948fa7351cd7d5ffe1f0eff7e3b62577325e", + "321b859ee1b2e3f9874968cf86a89765dcc94f1daa4f4081f8ad231b50911dd7", + "97c67ce44962a7b1afacbdf7610259b634c74cf7953cf2b5766bfccf3e347b13", + "7b4c2358a5ad5bc41a3c0dcd67791b76b50cf252d8ee23e3290bf043f327ffb2", + "d0cb084bf73c6a8bf5cd88cfb595b6f89b05a25e54817adec872eb5b699f83c6" ], "summary": [ { - "sha": "66754667bf56", + "sha": "b6027761fc92", "baseline_result": "OPTIMAL", - "baseline_ms": 3208.92 + "baseline_ms": 3431.8 }, { - "sha": "47888d1b56a4", + "sha": "321b859ee1b2", "baseline_result": "OPTIMAL", - "baseline_ms": 3264.07 + "baseline_ms": 3472.19 }, { - "sha": "ed8349c414d6", + "sha": "97c67ce44962", "baseline_result": "OPTIMAL", - "baseline_ms": 3318.12 + "baseline_ms": 3578.34 }, { - "sha": "9f330e3d7528", + "sha": "7b4c2358a5ad", "baseline_result": "OPTIMAL", - "baseline_ms": 3351.44 + "baseline_ms": 3598.47 }, { - "sha": "a27f90201b57", + "sha": "d0cb084bf73c", "baseline_result": "OPTIMAL", - "baseline_ms": 3366.15 + "baseline_ms": 3731.05 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage3_sample.json b/input/cpsat-bench/evolve/shared/stage3_sample.json index 823925ec58..96f6563ed4 100644 --- a/input/cpsat-bench/evolve/shared/stage3_sample.json +++ b/input/cpsat-bench/evolve/shared/stage3_sample.json @@ -1,38 +1,38 @@ { - "selection": "5 decisive Q5 (slowest 20%)", + "selection": "5 decisive runtime cluster c5 (slow group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ - "6ecbb5bc3a035ad7b51348fac58112aa35296e0e6ae9405119a1296fb9384045", - "90e16615aa9873fcba0b9ffeaec5584d26877834691f2b3011096e29281d26ec", - "b18203536680dcbad9276ec9d8db50f6ef463cbcbbea5aaeabff959652209e3c", - "0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc", - "1f71e23e551c8ed60353acbeaeb177c66d93eba8dd1d4127d654ea0aa634ce54" + "1f71e23e551c8ed60353acbeaeb177c66d93eba8dd1d4127d654ea0aa634ce54", + "7d5ef007c15860b85ba57ea7a09ccb08ac2d31f296bc47936d625e7af0883661", + "bef168aea12ee9d5a0b12880cb3c69ebc80ff968b9f2500a907c5f3f16f0d369", + "5216fc2158f95b1730c1fd3689b0b5a0d9415f13f1dee52b6266266215e21ed9", + "b43c4de7b33424859986111645c0f3535ce55c97d536fdfc7eda147261d92ad3" ], "summary": [ { - "sha": "6ecbb5bc3a03", + "sha": "1f71e23e551c", "baseline_result": "OPTIMAL", - "baseline_ms": 5580.53 + "baseline_ms": 7400.86 }, { - "sha": "90e16615aa98", + "sha": "7d5ef007c158", "baseline_result": "OPTIMAL", - "baseline_ms": 5772.7 + "baseline_ms": 7474.2 }, { - "sha": "b18203536680", + "sha": "bef168aea12e", "baseline_result": "OPTIMAL", - "baseline_ms": 6300.32 + "baseline_ms": 7530.89 }, { - "sha": "0072ac871059", + "sha": "5216fc2158f9", "baseline_result": "OPTIMAL", - "baseline_ms": 6896.45 + "baseline_ms": 9137.48 }, { - "sha": "1f71e23e551c", + "sha": "b43c4de7b334", "baseline_result": "OPTIMAL", - "baseline_ms": 7400.86 + "baseline_ms": 9668 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage4_sample.json b/input/cpsat-bench/evolve/shared/stage4_sample.json index f2e60a9da1..2693731abd 100644 --- a/input/cpsat-bench/evolve/shared/stage4_sample.json +++ b/input/cpsat-bench/evolve/shared/stage4_sample.json @@ -15,12 +15,12 @@ "cf629228e69ab5f293c8c30459175cccac150e85ec84539bfd8d5dc27540cf1a", "674b484eb726690bc9d4878cb217bc6b22f30eac4f8d512be6e2713a303545dd", "4e4bc1ad622d2addafb58df0886f20fabf9c872e18cba83057410255a6e2f356", - "321b859ee1b2e3f9874968cf86a89765dcc94f1daa4f4081f8ad231b50911dd7", + "ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047", "c6a6b2aab48ad0047d3e14c25d630fb1e22e3844608af935b5e481c1965a8010", "12e68b0b5089d173f44977d0bc3a9a85c4639b3691b74fce9d76d473e3231d38", "62c6e2f566388ddc13097f1369c9ad4569c1b4f54f888066e8ab2bfe96d3a0b4", "14ea5ccece18b8bffb5947b963c7d0f9e5a2f1c56d1ccd70e1493be39380ba78", - "bef168aea12ee9d5a0b12880cb3c69ebc80ff968b9f2500a907c5f3f16f0d369", + "90e16615aa9873fcba0b9ffeaec5584d26877834691f2b3011096e29281d26ec", "a49d307cfcf3ca9dff031a54cca31086a1882b7c0e65c1da60de651a8c9a79b8" ], "summary": [ @@ -90,9 +90,9 @@ "baseline_ms": 3107.88 }, { - "sha": "321b859ee1b2", + "sha": "ed8349c414d6", "baseline_result": "OPTIMAL", - "baseline_ms": 3472.19 + "baseline_ms": 3318.12 }, { "sha": "c6a6b2aab48a", @@ -115,9 +115,9 @@ "baseline_ms": 4358.96 }, { - "sha": "bef168aea12e", + "sha": "90e16615aa98", "baseline_result": "OPTIMAL", - "baseline_ms": 7530.89 + "baseline_ms": 5772.7 }, { "sha": "a49d307cfcf3", From 045a56138099ba02f9b06e642cc05dcb99bb2e03 Mon Sep 17 00:00:00 2001 From: hdson Date: Fri, 22 May 2026 10:44:57 +0900 Subject: [PATCH 21/42] cpsat update phase --- input/_lib/runtime.py | 23 +++++++++ input/_lib/subprocess_runner.py | 18 ++++++- input/cpsat-bench/evolve/benchmark_final.py | 36 +++++++++----- input/cpsat-bench/evolve/config.yaml | 19 ++++++-- input/cpsat-bench/evolve/final_verify.py | 39 ++++++++++----- .../evolve/phase1_search/initial_program.py | 15 +++++- .../evolve/phase2_presolve/initial_program.py | 12 ++++- .../evolve/phase3_lp_cuts/initial_program.py | 31 +++++++++--- .../evolve/phase4_unified/initial_program.py | 14 +++++- .../evolve/shared/baseline_params.py | 14 +++++- input/cpsat-bench/evolve/shared/evaluator.py | 47 +++++++++++++++---- input/cpsat-bench/evolve/shared/runtime.py | 1 + 12 files changed, 215 insertions(+), 54 deletions(-) diff --git a/input/_lib/runtime.py b/input/_lib/runtime.py index 7c8a84d8f2..c2ae7aaace 100644 --- a/input/_lib/runtime.py +++ b/input/_lib/runtime.py @@ -77,6 +77,29 @@ def core_range(): 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. diff --git a/input/_lib/subprocess_runner.py b/input/_lib/subprocess_runner.py index b00e122480..7c87dbef38 100644 --- a/input/_lib/subprocess_runner.py +++ b/input/_lib/subprocess_runner.py @@ -39,14 +39,28 @@ def run_solver(worker_path, problem_path, params, timeout_s, """ See module docstring for the contract. - cpu_core: optional Linux taskset pin. Ignored on macOS / no util-linux. + 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: - args = [_TASKSET, "-c", str(int(cpu_core))] + args + 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: diff --git a/input/cpsat-bench/evolve/benchmark_final.py b/input/cpsat-bench/evolve/benchmark_final.py index d111fc08e5..ea2ab53a56 100644 --- a/input/cpsat-bench/evolve/benchmark_final.py +++ b/input/cpsat-bench/evolve/benchmark_final.py @@ -38,7 +38,7 @@ from baseline_params import BASELINE, LOCKED # noqa: E402 from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range # noqa: E402 +from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 _BENCH_DIR = _HERE.parent _RAW_DIR = _BENCH_DIR / "raw-data" @@ -50,14 +50,14 @@ _DECISIVE = ("OPTIMAL", "FEASIBLE") -def _load_get_params(program_path): +def _load_program(program_path): spec = importlib.util.spec_from_file_location("program", program_path) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) if not hasattr(module, "get_params"): print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) sys.exit(2) - return module.get_params() + return module def _load_problem_index(): @@ -153,9 +153,12 @@ def main(): print(f"ERROR: {program_path} not found", file=sys.stderr) return 2 - variant_params = _load_get_params(program_path) - violations = {k: variant_params.get(k) for k in LOCKED - if variant_params.get(k) != LOCKED[k]} + program = _load_program(program_path) + variant_params = program.get_params() + _phase_locked = getattr(program, "PHASE_LOCKED", None) + _lock = _phase_locked if isinstance(_phase_locked, dict) else LOCKED + violations = {k: variant_params.get(k) for k in _lock + if variant_params.get(k) != _lock[k]} if violations: print(f"ERROR: locked params violated: {violations}", file=sys.stderr) return 2 @@ -166,8 +169,13 @@ def main(): cores = core_range() if cores is None: cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(cores), len(metas)) - cores = cores[:n_parallel] + workers_per_solve = int(variant_params.get("num_search_workers", 1) or 1) + blocks = alloc_core_blocks(cores, workers_per_solve) + if not blocks: + blocks = [list(cores)] if cores else [None] + n_parallel = min(len(blocks), len(metas)) + blocks = blocks[:n_parallel] + baseline_cores = [(b[0] if isinstance(b, (list, tuple)) and b else b) for b in blocks] n_iters = args.iters timeout_s = args.timeout @@ -178,18 +186,20 @@ def main(): print(f" iters per case : {n_iters} baseline + {n_iters} variant = {n_iters * 2}") print(f" total runs : {len(metas)} problems × {n_iters * 2} = {n_total_runs}") print(f" per-run timeout : {timeout_s}s") - print(f" parallel solvers: {n_parallel} cores={cores}") + print(f" parallel solvers: {n_parallel} variant_workers={workers_per_solve} blocks={blocks}") print(f" params diff : {sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} keys differ from BASELINE") print() def _worker(idx_meta): i, meta, pb = idx_meta - core = cores[i % n_parallel] + slot = i % n_parallel + v_core = blocks[slot] + b_core = baseline_cores[slot] t0 = time.monotonic() - baseline_runs = _run_repeat(pb, BASELINE, n_iters, timeout_s, core, "baseline") - variant_runs = _run_repeat(pb, variant_params, n_iters, timeout_s, core, "variant") + baseline_runs = _run_repeat(pb, BASELINE, n_iters, timeout_s, b_core, "baseline") + variant_runs = _run_repeat(pb, variant_params, n_iters, timeout_s, v_core, "variant") dt = time.monotonic() - t0 - return i, meta, baseline_runs, variant_runs, core, dt + return i, meta, baseline_runs, variant_runs, v_core, dt tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] t_start = time.monotonic() diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index 9d3e6f0f07..327060f2b4 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -26,12 +26,13 @@ bench: # === 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. -# - Each pinned to a dedicated core via taskset (Linux). -# - Capped at len(problems_in_stage) at runtime. +# - 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. -# - CP-SAT itself uses num_search_workers=8 internally (locked), so total -# core demand ≈ parallel_solvers × 8. Size accordingly. -parallel_solvers: 1 +parallel_solvers: 8 max_iterations: 60 checkpoint_interval: 10 @@ -78,11 +79,19 @@ prompt: 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: diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py index c2a2603009..4c6d93d03b 100644 --- a/input/cpsat-bench/evolve/final_verify.py +++ b/input/cpsat-bench/evolve/final_verify.py @@ -40,7 +40,7 @@ from baseline_params import BASELINE, LOCKED # noqa: E402 from score import score # noqa: E402 from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range # noqa: E402 +from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 _BENCH_DIR = _HERE.parent _RAW_DIR = _BENCH_DIR / "raw-data" @@ -51,14 +51,14 @@ _DECISIVE = ("OPTIMAL", "FEASIBLE") -def _load_get_params(program_path): +def _load_program(program_path): spec = importlib.util.spec_from_file_location("program", program_path) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) if not hasattr(module, "get_params"): print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) sys.exit(2) - return module.get_params() + return module def _load_problem_index(): @@ -106,9 +106,14 @@ def main(): print(f"ERROR: {program_path} not found", file=sys.stderr) return 2 - variant_params = _load_get_params(program_path) - violations = {k: variant_params.get(k) for k in LOCKED - if variant_params.get(k) != LOCKED[k]} + program = _load_program(program_path) + variant_params = program.get_params() + # Use the program's PHASE_LOCKED if it exposes one (later phases typically + # lock num_search_workers higher than baseline); fall back to global LOCKED. + _phase_locked = getattr(program, "PHASE_LOCKED", None) + _lock = _phase_locked if isinstance(_phase_locked, dict) else LOCKED + violations = {k: variant_params.get(k) for k in _lock + if variant_params.get(k) != _lock[k]} if violations: print(f"ERROR: locked params violated: {violations}", file=sys.stderr) return 2 @@ -119,22 +124,32 @@ def main(): cores = core_range() if cores is None: cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(cores), len(metas)) - cores = cores[:n_parallel] + workers_per_solve = int(variant_params.get("num_search_workers", 1) or 1) + blocks = alloc_core_blocks(cores, workers_per_solve) + if not blocks: + blocks = [list(cores)] if cores else [None] + n_parallel = min(len(blocks), len(metas)) + blocks = blocks[:n_parallel] + # Baseline run still uses workers=1 (single core). Give it the first core + # of each block so it's pinned within the same NUMA neighborhood as the + # variant for fair comparison. + baseline_cores = [(b[0] if isinstance(b, (list, tuple)) and b else b) for b in blocks] print(f"final verify: {program_path}") print(f" sample : {source}") print(f" params : {len(variant_params)} keys, " f"{sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} differ from BASELINE") - print(f" parallel solvers : {n_parallel} cores={cores}") + print(f" parallel solvers : {n_parallel} variant_workers={workers_per_solve} blocks={blocks}") print(f" per-problem timeout : {TIMEOUT_S}s × 2 (baseline + variant)") print() def _measure(idx_meta): i, meta, pb = idx_meta - core = cores[i % n_parallel] if n_parallel > 0 else None - b = run_cpsat(pb, BASELINE, TIMEOUT_S, cpu_core=core) - v = run_cpsat(pb, variant_params, TIMEOUT_S, cpu_core=core) + slot = i % n_parallel if n_parallel > 0 else 0 + v_core = blocks[slot] if n_parallel > 0 else None + b_core = baseline_cores[slot] if n_parallel > 0 else None + b = run_cpsat(pb, BASELINE, TIMEOUT_S, cpu_core=b_core) + v = run_cpsat(pb, variant_params, TIMEOUT_S, cpu_core=v_core) return i, meta, b, v tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] diff --git a/input/cpsat-bench/evolve/phase1_search/initial_program.py b/input/cpsat-bench/evolve/phase1_search/initial_program.py index e8af25a38e..5d2d578566 100644 --- a/input/cpsat-bench/evolve/phase1_search/initial_program.py +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -6,8 +6,12 @@ use_feasibility_jump, use_feasibility_pump Other params stay at BASELINE. -Do NOT modify locked keys (see baseline_params.LOCKED): - random_seed, num_search_workers, timeout_sec +This phase pins num_search_workers=1 (single-worker search) so other knobs +are evaluated without multi-thread / multi-subsolver noise. Phase 3 raises +the worker count to explore subsolver-mix effects. + +Do NOT modify locked keys (see PHASE_LOCKED below + baseline_params.LOCKED): + random_seed, num_search_workers Invalid solver keys cause evaluator to return 0 and surface the offending key. """ import pathlib @@ -19,6 +23,12 @@ from baseline_params import BASELINE # noqa: E402 +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 1, +} + + # EVOLVE-BLOCK-START SEARCH_OVERRIDES = { "extra_subsolvers": ["default_lp", "no_lp"], @@ -33,6 +43,7 @@ def get_params(): p = dict(BASELINE) p.update(SEARCH_OVERRIDES) + p.update(PHASE_LOCKED) # re-enforce phase lock last return p diff --git a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py index d3049bfef5..bd63601882 100644 --- a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -7,7 +7,10 @@ (LLM may explore other presolve_* / probing_* keys.) Inherits phase1 winners (loaded from shared/phase1_best.json if present). -Do NOT modify locked keys. +Like phase1, num_search_workers stays at 1 — presolve effects must be measured +without multi-worker search masking them. + +Do NOT modify locked keys (random_seed, num_search_workers). """ import json import pathlib @@ -19,6 +22,12 @@ from baseline_params import BASELINE # noqa: E402 +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": 1, +} + + def _load_prev(name): p = _SHARED / name if p.exists(): @@ -40,6 +49,7 @@ def get_params(): p = dict(BASELINE) p.update(_PHASE1) p.update(PRESOLVE_OVERRIDES) + p.update(PHASE_LOCKED) # phase1 may have stored workers; re-pin to 1 return p diff --git a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py index b13c67affb..b83c2c2bb5 100644 --- a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py +++ b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py @@ -1,13 +1,23 @@ """ -Phase 3: tune CP-SAT LP / cuts / MIP-bridge knobs. +Phase 3: tune CP-SAT LP / cuts / MIP-bridge AND subsolver-mix knobs. + +Worker count is RAISED to PHASE3_WORKERS (default 8) for this phase: many +subsolvers (max_lp, no_lp, core, quick_restart, reduced_costs, lb_tree_search, +probing_search, …) only activate when num_search_workers is large enough. +Phase 3's job is to find the best subsolver combination + LP/cuts tuning that +works at parallel-search scale. Targeted namespace: - max_num_cuts, cut_level, - mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance -(LLM may explore other linearization_level, *_cuts toggles.) + max_num_cuts, cut_level, linearization_level, + mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance, + extra_subsolvers, ignore_subsolvers, diversify_lns_params, repair_hint +(LLM may explore other *_cuts toggles and subsolver list contents.) + +Inherits phase1+phase2 winners, but num_search_workers is re-pinned to +PHASE3_WORKERS at the end of get_params() — phases 1/2 ran at workers=1 so +their wins still need to be re-validated at workers=PHASE3_WORKERS here. -Inherits phase1+phase2 winners. -Do NOT modify locked keys. +Do NOT modify locked keys (random_seed, num_search_workers). """ import json import pathlib @@ -19,6 +29,14 @@ from baseline_params import BASELINE # noqa: E402 +PHASE3_WORKERS = 8 + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": PHASE3_WORKERS, +} + + def _load_prev(name): p = _SHARED / name if p.exists(): @@ -47,6 +65,7 @@ def get_params(): p.update(_PHASE1) p.update(_PHASE2) p.update(LP_CUTS_OVERRIDES) + p.update(PHASE_LOCKED) # pin workers=PHASE3_WORKERS for this phase return p diff --git a/input/cpsat-bench/evolve/phase4_unified/initial_program.py b/input/cpsat-bench/evolve/phase4_unified/initial_program.py index c1f2cfbadb..2323f85179 100644 --- a/input/cpsat-bench/evolve/phase4_unified/initial_program.py +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -5,7 +5,10 @@ the union of phase{1,2,3}_best.json winners. LLM may then tune all keys jointly. -Do NOT modify locked keys (random_seed, num_search_workers, timeout_sec). +num_search_workers stays at PHASE4_WORKERS (= phase3 setting) so unified +tuning happens at the same parallel-search scale as phase3. + +Do NOT modify locked keys (random_seed, num_search_workers). """ import pathlib import sys @@ -16,6 +19,14 @@ from baseline_params import BASELINE # noqa: E402 +PHASE4_WORKERS = 8 + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": PHASE4_WORKERS, +} + + # EVOLVE-BLOCK-START UNIFIED_OVERRIDES = {} # EVOLVE-BLOCK-END @@ -24,6 +35,7 @@ def get_params(): p = dict(BASELINE) p.update(UNIFIED_OVERRIDES) + p.update(PHASE_LOCKED) return p diff --git a/input/cpsat-bench/evolve/shared/baseline_params.py b/input/cpsat-bench/evolve/shared/baseline_params.py index ceac7d1717..f6608e6769 100644 --- a/input/cpsat-bench/evolve/shared/baseline_params.py +++ b/input/cpsat-bench/evolve/shared/baseline_params.py @@ -9,17 +9,27 @@ (scheduler) — NOT real CpSolverParameters proto fields. Including them would make every variant fail with invalid_param. Dropped from BASELINE and from LOCKED; the worker also has both in its _DROP set as belt+braces. + +Worker-count policy (2026-05 revision): + - BASELINE.num_search_workers = 1 (minimum) so phases 1+2 isolate other + knobs from parallel-search noise. + - Each phase declares its own PHASE_LOCKED in its initial_program.py, + pinning num_search_workers to the phase-specific value. Phase 3 raises + it (typically 8) to let the LLM explore subsolver variations that only + activate with multi-worker search. + - Global LOCKED only enforces random_seed; worker count is enforced per-phase. """ BASELINE = { - "num_search_workers": 8, + "num_search_workers": 1, "random_seed": 0, "interleave_search": True, } +# Global locks enforced for all phases. Per-phase locks (incl. num_search_workers) +# live in each phase's initial_program.py as PHASE_LOCKED. LOCKED = { "random_seed": 0, - "num_search_workers": 8, } diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index fdf8d8472f..78f2093448 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -28,7 +28,9 @@ from baseline_params import BASELINE, LOCKED # noqa: E402 from score import score # noqa: E402 from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, cascade_threshold, core_range # noqa: E402 +from runtime import ( # noqa: E402 + parallel_solvers, cascade_threshold, core_range, alloc_core_blocks, +) from openevolve.evaluation_result import EvaluationResult # noqa: E402 @@ -153,13 +155,17 @@ def _evaluate(program_path, problems, stage_name): "full_traceback": traceback.format_exc()[-2000:], "stage": stage_name}, ) - violations = {k: params.get(k) for k in LOCKED if params.get(k) != LOCKED[k]} + # Per-phase lock (worker count varies by phase) overrides the global lock. + phase_locked = getattr(program, "PHASE_LOCKED", None) + locked = phase_locked if isinstance(phase_locked, dict) else LOCKED + 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, + {"locked_violated": violations, "locked_expected": locked, "stage": stage_name, - "suggestion": "Do not modify locked keys (see baseline_params.LOCKED)."}, + "suggestion": "Do not modify locked keys (see PHASE_LOCKED in " + "this phase's initial_program.py)."}, ) if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: @@ -176,14 +182,25 @@ def _evaluate(program_path, problems, stage_name): import queue as _queue # Core pool: OPENEVOLVE_CORE_RANGE (e.g. "2-7") overrides; else # cores 1..parallel_solvers() (core 0 reserved for kernel housekeeping). + # + # When params["num_search_workers"] = W > 1, each variant solve needs W + # cores. We floor-chunk the core list into W-sized blocks; concurrency = + # number of blocks. Leftover cores at the tail are dropped to keep every + # solve's CPU budget identical (comparable timings). _cores = core_range() if _cores is None: _cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(_cores), len(problems)) - _cores = _cores[:n_parallel] + workers_per_solve = int(params.get("num_search_workers", 1) or 1) + _blocks = alloc_core_blocks(_cores, workers_per_solve) + if not _blocks: + # Not enough cores for even one block at workers_per_solve. Fall back + # to a single block of all available cores (still respect taskset pin). + _blocks = [list(_cores)] if _cores else [None] + n_parallel = min(len(_blocks), len(problems)) + _blocks = _blocks[:n_parallel] _core_pool = _queue.Queue() - for _c in _cores: - _core_pool.put(_c) + for _b in _blocks: + _core_pool.put(_b) def _solve(idx_p): idx, p = idx_p @@ -231,6 +248,16 @@ def _regression_err(p, r): }, ) + def _fmt_core(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"core_blocks={[_fmt_core(b) for b in _blocks]}", flush=True) + by_idx = {} abort = None if n_parallel == 1: @@ -238,7 +265,7 @@ def _regression_err(p, r): 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) + f"(core={_fmt_core(core)})", flush=True) if "invalid_param" in r: return _invalid_err(r) if _is_regression(p, r): @@ -257,7 +284,7 @@ def _regression_err(p, r): 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) + f"(core={_fmt_core(core)})", flush=True) if "invalid_param" in r: abort = ("invalid", p, r) elif _is_regression(p, r): diff --git a/input/cpsat-bench/evolve/shared/runtime.py b/input/cpsat-bench/evolve/shared/runtime.py index ba708c1055..0f15616ab7 100644 --- a/input/cpsat-bench/evolve/shared/runtime.py +++ b/input/cpsat-bench/evolve/shared/runtime.py @@ -15,6 +15,7 @@ from _lib import runtime as _rt # noqa: E402 core_range = _rt.core_range +alloc_core_blocks = _rt.alloc_core_blocks def parallel_solvers(default=1): From b55056183389c07ddb14bc919d53c4a647b49764 Mon Sep 17 00:00:00 2001 From: hdson Date: Fri, 22 May 2026 11:03:51 +0900 Subject: [PATCH 22/42] fix rebase logic --- input/cpsat-bench/evolve/rebaseline_local.py | 267 ++++++++++++++----- input/cpsat-bench/evolve/shared/evaluator.py | 68 ++++- input/run_phase.sh | 78 +++++- 3 files changed, 330 insertions(+), 83 deletions(-) diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py index 3cae20787c..68d0542458 100644 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -5,18 +5,46 @@ objective_value — the last is critical for cost-mode scoring (variant cost needs a baseline_obj to ratio against). +MULTI-WORKER BASELINES (2026-05 revision): + Each phase's initial_program.py pins num_search_workers via PHASE_LOCKED. + Comparing a workers=8 variant against a workers=1 baseline conflates + param-tuning gain with multi-thread parallelism gain. To keep speedup + honest, we capture one baseline PER unique worker count discovered across + the phase files, and store them under `by_workers` in the output. + + Auto-discovery: walks sibling phase*_*/initial_program.py and unions every + PHASE_LOCKED["num_search_workers"]. Override with --workers 1,8 if needed. + +Output schema (shared/local_baseline.json): + { + "": { + "raw_result": "OPTIMAL", + "raw_elapsed_ms": 4321, + "by_workers": { + "1": {"elapsed_ms": ..., "result": ..., "stats": {...}, + "objective": ..., "matches_raw": true}, + "8": {"elapsed_ms": ..., "result": ..., "stats": {...}, + "objective": ..., "matches_raw": true} + } + } + } + Wall-clock varies by hardware / ortools version. raw-data timings were recorded elsewhere; evaluator overlays this local file so per-problem timeout = baseline_ms * 1.3 and speedup = local_baseline_ms / variant_ms -are calibrated for this box. +are calibrated for this box AND for the variant's worker count. Per-problem timeout = REBASELINE_TIMEOUT_S (1 hr safety floor). Never cut a baseline run short — a truncated baseline poisons every variant comparison. -Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). +Concurrency = floor(len(core_pool) / W) per worker count. W=1 fills the +pool fully; W=8 typically runs sequentially on small hosts. """ +import argparse +import importlib.util import json import pathlib +import queue as _queue import sys import time from concurrent.futures import ThreadPoolExecutor, as_completed @@ -26,7 +54,7 @@ from baseline_params import BASELINE # noqa: E402 from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range # noqa: E402 +from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 _BENCH_DIR = _HERE.parent _RAW_DIR = _BENCH_DIR / "raw-data" @@ -78,7 +106,113 @@ def _load_target_shas(): return ids +def _discover_phase_workers(): + """Union of PHASE_LOCKED['num_search_workers'] across sibling phase dirs. + + Returns sorted list of unique worker counts (defaults to [1] if nothing + discovered, so the script still produces a usable baseline).""" + workers = set() + for prog in sorted(_HERE.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.relative_to(_HERE)}: {e}", + file=sys.stderr) + continue + pl = getattr(mod, "PHASE_LOCKED", None) + if isinstance(pl, dict) and "num_search_workers" in pl: + try: + workers.add(int(pl["num_search_workers"])) + 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 + try: + w = int(tok) + except ValueError: + raise SystemExit(f"--workers: bad integer {tok!r}") + 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 _measure_at_workers(tasks, w, cores): + """Run baseline for one worker count W across all tasks. Returns + list of (i, meta, res, core_block) tuples in submission order.""" + blocks = alloc_core_blocks(cores, w) + if not blocks: + blocks = [list(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["num_search_workers"] = w + + def _fmt(b): + if isinstance(b, (list, tuple)): + return ",".join(str(x) for x in b) + return str(b) if b is not None else "-" + + print(f" workers={w}: parallel={n_parallel} blocks={[_fmt(b) for b in blocks]}", + flush=True) + + def _solve(task): + i, meta, path = task + block = pool.get() + try: + res = run_cpsat(path, params, REBASELINE_TIMEOUT_S, cpu_core=block) + finally: + pool.put(block) + return i, meta, res, block + + out = [] + if n_parallel == 1: + for task in tasks: + out.append(_solve(task)) + else: + with ThreadPoolExecutor(max_workers=n_parallel) as ex: + futures = [ex.submit(_solve, 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 main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument( + "--workers", + type=str, + default=None, + help="comma-separated worker counts (e.g. '1,8'). Default: union of " + "PHASE_LOCKED['num_search_workers'] across sibling phase dirs.", + ) + args = ap.parse_args() + + if args.workers: + worker_counts = _parse_workers_arg(args.workers) + print(f"[rebaseline] worker counts (from --workers): {worker_counts}") + else: + worker_counts = _discover_phase_workers() + print(f"[rebaseline] worker counts (auto-discovered): {worker_counts}") + shas = _load_target_shas() idx = _load_problem_index() @@ -94,88 +228,75 @@ def main(): return 2 tasks.append((i, meta, path)) - import queue as _queue cores = core_range() if cores is None: cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(cores), len(tasks)) - cores = cores[:n_parallel] - print(f"rebaselining union of stage{{1,2,3,4}}_sample.json: {len(tasks)} problems") - print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (never cut short), " - f"parallel={n_parallel} cores={cores}") - print() - - _core_pool = _queue.Queue() - for _c in cores: - _core_pool.put(_c) - def _solve(task): - i, meta, path = task - core = _core_pool.get() - try: - res = run_cpsat(path, BASELINE, REBASELINE_TIMEOUT_S, cpu_core=core) - finally: - _core_pool.put(core) - return i, meta, res, core + print(f"rebaselining union of stage{{1,2,3,4}}_sample.json: " + f"{len(tasks)} problems × {len(worker_counts)} worker counts") + print(f" per-problem timeout = {REBASELINE_TIMEOUT_S}s (never cut short), " + f"core pool = {cores}") + print() - t_start = time.monotonic() - completed = [] - if n_parallel == 1: - for task in tasks: - completed.append(_solve(task)) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_solve, t) for t in tasks] - for fut in as_completed(futures): - completed.append(fut.result()) - completed.sort(key=lambda x: x[0]) - - out = {} - mismatch = 0 - for i, meta, res, core 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: - mismatch += 1 - - if invalid: - flag = f" INVALID_PARAM={invalid}" - elif ok: - flag = "" - else: - flag = " MISMATCH" - ratio = got_ms / max(meta["raw_ms"], 1) - print( - f" [{i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " - f"raw={meta['raw_result']:<10}/{int(meta['raw_ms']):>7}ms " - f"local={got_result:<10}/{got_ms:>7}ms ratio={ratio:.2f}x{flag} " - f"core={core}", - flush=True, - ) - - entry = { - "elapsed_ms": got_ms, - "result": got_result, - "matches_raw": ok, + # results[sha]["by_workers"][str(w)] = {...} + results = {} + for meta in (m for _, m, _ in tasks): + results[meta["sha"]] = { + "raw_result": meta["raw_result"], "raw_elapsed_ms": meta["raw_ms"], - "stats": res.get("stats") or {}, + "by_workers": {}, } - if "objective" in res: - entry["objective"] = res["objective"] - out[meta["sha"]] = entry + + t_start = time.monotonic() + mismatch_total = 0 + for w in worker_counts: + completed = _measure_at_workers(tasks, w, cores) + 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: + mismatch_total += 1 + + if invalid: + flag = f" INVALID_PARAM={invalid}" + elif ok: + flag = "" + else: + flag = " MISMATCH" + 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)) + print( + f" [W={w} {i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " + f"raw={meta['raw_result']:<10}/{int(meta['raw_ms']):>7}ms " + f"local={got_result:<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 elapsed = time.monotonic() - t_start - _OUT.write_text(json.dumps(out, indent=2) + "\n") + _OUT.write_text(json.dumps(results, indent=2) + "\n") print() print(f"wrote {_OUT.relative_to(_BENCH_DIR.parent)} " - f"({len(out)} entries, {mismatch} mismatches)") + f"({len(results)} entries × {len(worker_counts)} W = " + f"{len(results) * len(worker_counts)} runs, {mismatch_total} mismatches)") print(f"total time: {elapsed:.1f}s") - if mismatch: - print(f"WARNING: {mismatch} problems had result mismatch — " - f"evaluator will keep raw_ms for those") - return 0 if mismatch == 0 else 1 + if mismatch_total: + print(f"WARNING: {mismatch_total} (problem, W) pairs had result mismatch — " + f"evaluator will fall back to raw_ms for those.") + return 0 if mismatch_total == 0 else 1 if __name__ == "__main__": diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 78f2093448..bb60554f46 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -56,7 +56,34 @@ def _load_program(path): return module -def _load_problems(): +def _pick_local_baseline(lo_entry, workers): + """Select the right local baseline entry for `workers`. + + New schema: {"by_workers": {"1": {...}, "8": {...}}, ...}. + Legacy schema (flat keys: elapsed_ms/stats/objective/matches_raw) is + treated as workers=1 measurement. + + Returns the inner dict (with matches_raw/elapsed_ms/stats/objective) + or None if no usable entry exists for that worker count. + """ + if not isinstance(lo_entry, dict): + return None + bw = lo_entry.get("by_workers") + if isinstance(bw, dict) and bw: + key = str(int(workers)) + if key in bw: + return bw[key] + # No exact match. Return None so caller falls back to raw_ms; we + # intentionally do NOT cross-substitute (e.g. W=8 → W=1) because the + # whole point of by_workers is to keep speedup ratios honest. + return None + # Legacy flat schema — treat as W=1 only. + if "elapsed_ms" in lo_entry and int(workers) == 1: + return lo_entry + return None + + +def _load_problems(workers=1): local = {} if _LOCAL_BASELINE.exists(): local = json.loads(_LOCAL_BASELINE.read_text()) @@ -69,7 +96,7 @@ def _load_problems(): baseline_result = (d.get("cpsat_status") or {}).get("result") baseline_stats = d.get("cpsat_response_stats") or {} baseline_objective = (d.get("cpsat_status") or {}).get("objective_value") - lo = local.get(sha) + 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 @@ -351,25 +378,51 @@ def _fmt_core(c): return EvaluationResult(metrics=metrics, artifacts=artifacts) +def _peek_workers(program_path): + """Resolve num_search_workers for baseline lookup BEFORE running _evaluate. + + Prefer PHASE_LOCKED (authoritative); fall back to get_params(). On any + failure, return 1 — _evaluate's own program-load handles the real error + reporting later.""" + try: + program = _load_program(program_path) + except Exception: + return 1 + pl = getattr(program, "PHASE_LOCKED", None) + if isinstance(pl, dict) and "num_search_workers" in pl: + try: + return int(pl["num_search_workers"]) + except (TypeError, ValueError): + pass + try: + params = program.get_params() + return int(params.get("num_search_workers", 1) or 1) + except Exception: + return 1 + + def evaluate_stage1(program_path): - return _evaluate(program_path, _filter_stage1(_load_problems()), "stage1") + w = _peek_workers(program_path) + return _evaluate(program_path, _filter_stage1(_load_problems(w)), "stage1") def evaluate_stage2(program_path): - return _evaluate(program_path, _filter_stage2(_load_problems()), "stage2") + w = _peek_workers(program_path) + return _evaluate(program_path, _filter_stage2(_load_problems(w)), "stage2") def evaluate_stage3(program_path): # openevolve cascade hardcodes 3 stages, so user-stage4 (broad runtime # sample) is chained inside stage3 via the runtime cascade_threshold gate. - problems3 = _filter_stage3(_load_problems()) + w = _peek_workers(program_path) + problems3 = _filter_stage3(_load_problems(w)) 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()) + problems4 = _filter_stage4(_load_problems(w)) r4 = _evaluate(program_path, problems4, "stage4") if not isinstance(r4, EvaluationResult): return r4 @@ -380,7 +433,8 @@ def evaluate_stage3(program_path): def evaluate_stage4(program_path): # Standalone entry for manual / final-verify use. Not invoked by cascade. - problems = _filter_stage4(_load_problems()) + w = _peek_workers(program_path) + problems = _filter_stage4(_load_problems(w)) return _evaluate(program_path, problems, "stage4") diff --git a/input/run_phase.sh b/input/run_phase.sh index f33181ca4c..1d0bd786d8 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -162,10 +162,82 @@ if [ "$EXTRACT_ONLY" != "1" ]; then fi if [ -f "$ROOT/$REBASELINE_SCRIPT" ]; then - if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ -f "$ROOT/shared/local_baseline.json" ]; then + # Bench-specific: if phase initial_program.py files declare PHASE_LOCKED + # with num_search_workers, the rebaseline script captures one baseline + # per unique W. SKIP_REBASELINE=1 only reuses an existing file when its + # schema covers every required W; legacy/incomplete files force a + # fresh rebaseline. + rebaseline_needed=0 + if [ ! -f "$ROOT/shared/local_baseline.json" ]; then + rebaseline_needed=1 + reason="missing local_baseline.json" + else + # Determine required worker counts from phase modules. Returns + # space-separated ints (e.g. "1 8") or empty if none declared. + REQ_WORKERS="$( + python - "$ROOT" <<'PY' +import importlib.util +import pathlib +import sys +root = pathlib.Path(sys.argv[1]) +workers = set() +for prog in sorted(root.glob("phase*_*/initial_program.py")): + try: + spec = importlib.util.spec_from_file_location(prog.parent.name, prog) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + except Exception: + continue + pl = getattr(mod, "PHASE_LOCKED", None) + if isinstance(pl, dict) and "num_search_workers" in pl: + try: + workers.add(int(pl["num_search_workers"])) + except (TypeError, ValueError): + pass +print(" ".join(str(w) for w in sorted(workers))) +PY + )" + if [ -n "$REQ_WORKERS" ]; then + # Check that local_baseline.json's by_workers covers each W. + MISSING="$( + python - "$ROOT/shared/local_baseline.json" "$REQ_WORKERS" <<'PY' +import json +import sys +path, req = sys.argv[1], sys.argv[2].split() +try: + data = json.load(open(path)) +except Exception: + print("unreadable") + sys.exit(0) +if not isinstance(data, dict) or not data: + print("empty") + sys.exit(0) +missing = [] +for w in req: + covered = False + for v in data.values(): + if isinstance(v, dict) and isinstance(v.get("by_workers"), dict) and w in v["by_workers"]: + covered = True + break + if not covered: + missing.append(w) +print(",".join(missing)) +PY + )" + if [ "$MISSING" = "unreadable" ] || [ "$MISSING" = "empty" ]; then + rebaseline_needed=1 + reason="local_baseline.json $MISSING" + elif [ -n "$MISSING" ]; then + rebaseline_needed=1 + reason="local_baseline.json missing worker counts: $MISSING (required: $REQ_WORKERS)" + fi + fi + fi + + if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ "$rebaseline_needed" = "0" ]; then echo "[run_phase] SKIP_REBASELINE=1 — reusing existing local_baseline.json" - elif [ "${SKIP_REBASELINE:-0}" = "1" ]; then - echo "[run_phase] SKIP_REBASELINE=1 but local_baseline.json missing — running $REBASELINE_SCRIPT..." + elif [ "${SKIP_REBASELINE:-0}" = "1" ] && [ "$rebaseline_needed" = "1" ]; then + echo "[run_phase] SKIP_REBASELINE=1 but $reason — forcing rebaseline." python "$ROOT/$REBASELINE_SCRIPT" || \ echo "warning: $REBASELINE_SCRIPT finished with mismatches; evaluator falls back to raw_ms for those." else From d484866d0b1d217792fdf151fc53d34254802a8d Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 26 May 2026 14:08:10 +0900 Subject: [PATCH 23/42] [feat] add statistics for cp-sat --- input/cpsat-bench/Statistics/analyze.py | 462 +++++++++++++++++ .../Statistics/analyze_outliers.py | 466 ++++++++++++++++++ .../Statistics/outliers_report.txt | 82 +++ input/cpsat-bench/Statistics/outliers_top.csv | 31 ++ input/cpsat-bench/Statistics/per_instance.csv | 431 ++++++++++++++++ .../Statistics/plots/01_runtime_hist.png | Bin 0 -> 28111 bytes .../Statistics/plots/02_features_hist.png | Bin 0 -> 39650 bytes .../plots/03_runtime_vs_size_scatter.png | Bin 0 -> 134188 bytes .../Statistics/plots/04_box_by_status.png | Bin 0 -> 27562 bytes .../Statistics/plots/05_box_by_hash.png | Bin 0 -> 76659 bytes .../plots/06_heatmap_vars_constraints.png | Bin 0 -> 29040 bytes .../Statistics/plots/07_residual_scatter.png | Bin 0 -> 93573 bytes .../plots/08_outlier_constraint_kinds.png | Bin 0 -> 32677 bytes input/cpsat-bench/Statistics/report.txt | 61 +++ 14 files changed, 1533 insertions(+) create mode 100644 input/cpsat-bench/Statistics/analyze.py create mode 100644 input/cpsat-bench/Statistics/analyze_outliers.py create mode 100644 input/cpsat-bench/Statistics/outliers_report.txt create mode 100644 input/cpsat-bench/Statistics/outliers_top.csv create mode 100644 input/cpsat-bench/Statistics/per_instance.csv create mode 100644 input/cpsat-bench/Statistics/plots/01_runtime_hist.png create mode 100644 input/cpsat-bench/Statistics/plots/02_features_hist.png create mode 100644 input/cpsat-bench/Statistics/plots/03_runtime_vs_size_scatter.png create mode 100644 input/cpsat-bench/Statistics/plots/04_box_by_status.png create mode 100644 input/cpsat-bench/Statistics/plots/05_box_by_hash.png create mode 100644 input/cpsat-bench/Statistics/plots/06_heatmap_vars_constraints.png create mode 100644 input/cpsat-bench/Statistics/plots/07_residual_scatter.png create mode 100644 input/cpsat-bench/Statistics/plots/08_outlier_constraint_kinds.png create mode 100644 input/cpsat-bench/Statistics/report.txt diff --git a/input/cpsat-bench/Statistics/analyze.py b/input/cpsat-bench/Statistics/analyze.py new file mode 100644 index 0000000000..fec46c2e35 --- /dev/null +++ b/input/cpsat-bench/Statistics/analyze.py @@ -0,0 +1,462 @@ +#!/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 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") + 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}") + + 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..f5edd22232 --- /dev/null +++ b/input/cpsat-bench/Statistics/analyze_outliers.py @@ -0,0 +1,466 @@ +#!/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 + + +# ---------- 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") + 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") + + _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..dc39a4ecc8 --- /dev/null +++ b/input/cpsat-bench/Statistics/outliers_report.txt @@ -0,0 +1,82 @@ +# CP-SAT runtime outlier diagnosis +records: 430, regression-kept: 430 +log10 baseline: log10(elapsed_ms) = -2.949*log10(vars) + 5.262*log10(cons) + -10.116 +R^2 = 0.855, RMSE(log10) = 0.266 +top-30 outliers slowdown range: 2.4x .. 19.8x + +## Top outliers (residual = log10(actual/predicted)) +sha hash status elapsed_ms vars cons pred_ms resid conflicts branches +9c28c175 fadf9341 OPTIMAL 5,307,350.0 20881 236975 267,796.7 1.30 949,858 3,779,328 +ea6187c1 fadf9341 OPTIMAL 4,689,630.0 20881 236975 267,796.7 1.24 1,180,208 4,605,661 +64e85d06 5df8f0e7 OPTIMAL 5,707,370.0 20867 246453 329,832.1 1.24 1,489,558 5,574,793 +5651d412 5df8f0e7 OPTIMAL 4,481,100.0 20867 246453 329,832.1 1.13 891,686 3,512,483 +fe2b12d0 fadf9341 OPTIMAL 3,111,920.0 20867 236494 265,473.2 1.07 632,852 2,956,918 +b14c571a 56ab92f3 OPTIMAL 1,873,900.0 20844 233581 249,521.1 0.88 738,946 2,570,390 +cdc476df 56ab92f3 OPTIMAL 1,549,180.0 20830 233076 247,184.6 0.80 443,078 1,855,860 +5f84583f fadf9341 OPTIMAL 6,436.6 1979 21981 1,027.2 0.80 436 9,745 +073ef8ba 56ab92f3 OPTIMAL 1,496,560.0 20830 233100 247,318.6 0.78 531,653 2,141,669 +7e5aa401 56ab92f3 OPTIMAL 1,482,010.0 20844 233557 249,386.2 0.77 548,234 2,019,729 +96e1d05e 56ab92f3 OPTIMAL 1,443,320.0 20844 233581 249,521.1 0.76 700,829 2,655,203 +484ac3f3 56ab92f3 OPTIMAL 1,380,120.0 20844 233557 249,386.2 0.74 498,838 1,688,437 +26f53ed6 fadf9341 OPTIMAL 856,786.0 17076 192805 163,700.3 0.72 218,233 1,168,305 +083beb02 56ab92f3 OPTIMAL 686,113.0 17058 190773 155,305.1 0.65 210,392 952,603 +bc676605 5df8f0e7 OPTIMAL 837,165.0 17070 199450 195,858.0 0.63 213,359 1,171,620 +9c548616 5df8f0e7 OPTIMAL 1,312,210.0 20853 245972 327,105.4 0.60 261,411 1,407,886 +303a6733 fadf9341 OPTIMAL 3,530.8 1850 20766 929.1 0.58 379 11,971 +d8130c88 5df8f0e7 OPTIMAL 3,748.3 1850 21650 1,157.0 0.51 531 12,139 +e2b2952c 5df8f0e7 OPTIMAL 2,894.6 1739 20421 1,021.0 0.45 229 7,101 +471adab2 5df8f0e7 OPTIMAL 3,311.7 1863 21835 1,185.3 0.45 303 10,282 +202fbcb0 5df8f0e7 OPTIMAL 3,024.1 1674 20236 1,089.0 0.44 310 10,067 +b040ac13 5df8f0e7 OPTIMAL 3,052.7 1766 20923 1,108.7 0.44 228 10,193 +c0a0dc04 fadf9341 OPTIMAL 4,920.2 2199 25921 1,792.5 0.44 712 19,587 +834873ba 5df8f0e7 OPTIMAL 3,081.6 1850 21650 1,157.0 0.43 794 13,010 +a87f850d fadf9341 OPTIMAL 180.5 715 7423 68.3 0.42 560 4,544 +c4fe2cc0 fadf9341 OPTIMAL 4,839.6 2326 26958 1,867.2 0.41 2,116 24,405 +9c6904d7 5df8f0e7 OPTIMAL 3,212.3 1977 22871 1,269.6 0.40 822 11,048 +7b9053fb 5df8f0e7 OPTIMAL 2,916.7 1859 21771 1,174.5 0.40 399 10,761 +c0bbae62 fadf9341 OPTIMAL 400,652.0 17070 192562 162,786.1 0.39 122,197 544,974 +63f1e1c6 5df8f0e7 OPTIMAL 2,893.8 1863 21835 1,185.3 0.39 382 10,557 + +## Scalar feature comparison: outlier median vs baseline median +feature outlier_med base_med ratio +n_vars 79,153.00 22,524.00 3.51 +n_cons 191,667.50 53,456.00 3.59 +n_enforce 156,156.00 42,812.00 3.65 +linear_coef_max 1.00 1.00 1.00 +linear_coef_abs_sum 139,178.00 35,995.00 3.87 +linear_terms_max 161.00 66.00 2.44 +linear_terms_total 139,178.00 35,995.00 3.87 +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 5,499.00 1,941.00 2.83 +domain_max 1,000,000,000.00 1,000,000,000.00 1.00 +objective_terms 0.00 0.00 0.00 +n_assumptions 0.00 0.00 0.00 +n_search_strategy 0.00 0.00 0.00 +_num_conflicts 166,294.50 2,016.00 82.49 +_num_branches 748,788.50 56,588.00 13.23 +_num_binary_propagations 199,726,371.50 3,507,452.00 56.94 +_num_integer_propagations 52,432,900.00 1,511,411.50 34.69 +_num_restarts 668.50 18.00 37.14 + +## Constraint kinds: outlier vs baseline mean per instance +kind out_mean base_mean ratio out_share +at_most_one 421.2 231.8 1.82 1.00 +bool_and 27,732.8 12,744.8 2.18 1.00 +bool_or 42,792.7 18,794.1 2.28 1.00 +linear 59,773.4 22,868.6 2.61 1.00 + +## Interpretation hints +Features where outliers differ substantially (>=2x or <=0.5x): + - n_vars: outlier median 79,153.0 vs baseline 22,524.0 (3.51x) + - n_cons: outlier median 191,667.5 vs baseline 53,456.0 (3.59x) + - n_enforce: outlier median 156,156.0 vs baseline 42,812.0 (3.65x) + - linear_coef_abs_sum: outlier median 139,178.0 vs baseline 35,995.0 (3.87x) + - linear_terms_max: outlier median 161.0 vs baseline 66.0 (2.44x) + - linear_terms_total: outlier median 139,178.0 vs baseline 35,995.0 (3.87x) + - wide_int_count: outlier median 5,499.0 vs baseline 1,941.0 (2.83x) + - _num_conflicts: outlier median 166,294.5 vs baseline 2,016.0 (82.49x) + - _num_branches: outlier median 748,788.5 vs baseline 56,588.0 (13.23x) + - _num_binary_propagations: outlier median 199,726,371.5 vs baseline 3,507,452.0 (56.94x) + - _num_integer_propagations: outlier median 52,432,900.0 vs baseline 1,511,411.5 (34.69x) + - _num_restarts: outlier median 668.5 vs baseline 18.0 (37.14x) diff --git a/input/cpsat-bench/Statistics/outliers_top.csv b/input/cpsat-bench/Statistics/outliers_top.csv new file mode 100644 index 0000000000..256fe5193b --- /dev/null +++ b/input/cpsat-bench/Statistics/outliers_top.csv @@ -0,0 +1,31 @@ +_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 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+7b9053fb13e462eae04cb54e9e64f7929247a3ece739347a45fdc8deacd46646,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.39503502501284604,2916.72,1859,21771,399,10761,460076,229866,3,9180,21771,17453,1.0,26,14792,2000000001.0,2.0,771,1000000000,False,0,0,0,9180,7449,5039,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.39114990000400063,400652,17070,192562,122197,544974,158611075,37718100,536,79465,192562,157008,1.0,161,139823,2000000001.0,2.0,5505,1000000000,False,0,0,0,88116,63141,40687,618,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +63f1e1c6c7452e11af1db799f7a380dabc9c9b6edb7083f59d57c4952dfdb16d,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.38764579685835177,2893.75,1863,21835,382,10557,472961,227459,3,9208,21835,17518,1.0,26,14816,2000000001.0,2.0,773,1000000000,False,0,0,0,9198,7475,5059,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/input/cpsat-bench/Statistics/per_instance.csv b/input/cpsat-bench/Statistics/per_instance.csv new file mode 100644 index 0000000000..1ed9ea5aa7 --- /dev/null +++ b/input/cpsat-bench/Statistics/per_instance.csv @@ -0,0 +1,431 @@ +problem_sha256,applied_params_hash,status,elapsed_ms,num_variables,num_bool,num_int,num_constraints,num_workers,num_conflicts,num_branches,deterministic_time +0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6896.45,4352,2861,1491,51623,8,3843,79884,15.5139 +018944a5119d79b5bdb7d0f820725cb3e77a31d5a38971d95a209aada532a096,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1619.97,2565,1783,782,28940,8,6524,31795,3.34384 +024c5d8184d58d42edd90236a3922645eb1d0d94f1691afc6068b1d66a87ac5a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2818.52,1977,1377,600,22871,8,466,10294,1.87483 +0324fb5c1ded91eeb6343304c5b7ec103f1475463de93455f7e05db48fc927e9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3153.47,4053,2932,1121,44889,8,2310,45318,6.42205 +03290f094ddaee42319d3fce34ebe0773ab0c7b9178d512c34a7a674fa6df88b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4071.48,2199,1403,796,25969,8,10692,110220,5.38499 +053c5aa97705ce165e112c926e29a13534fbff557d9b76ed1a39c7e200ca3dbe,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1757.49,2736,1938,798,30419,8,752,13177,1.81954 +064e7f90eab7005c302ae3626c590ea2ea39dbf29fbfe2e34ce291c2df2ce620,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3432.26,2583,1793,790,30380,8,719,18056,3.00165 +06fbcfd06f4072d8d6f383d95876de2effc910f204c4919171cc0133cc4c61b9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5832.93,3243,2301,942,36071,8,6222,47499,5.09695 +072937c5444c0281a85dd5a43662dc4191d5103b014c7ff6a20acb50e48d8db2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,651.086,1856,1264,592,20575,8,326,9895,0.978946 +073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1496560.0,20830,16429,4401,233100,8,531653,2141669,5148.16 +07455570bdf201ce07c60133a216975122a176ca884afa8c1807cb3f4dbdf806,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2408.23,2583,1793,790,28808,8,806,17531,1.88466 +0776535f7fe277d05332ab5ee5a53406868e0bbca6f2b620ff23da961bc074a6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,76005.7,11130,8496,2634,126364,8,21705,179333,96.2732 +083beb025bc59908b1bdace2fac047f8f204cdb3fc4f947c0af0327a928abdcf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,686113,17058,13335,3723,190773,8,210392,952603,1489.62 +0976039f182f3d2a82c23ebc237b080434e5f24b43329696d42000dc1865231e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,19431.4,5306,3673,1633,62598,8,18082,110936,28.5748 +09b3336890b5addd1fa433349acab7d0176679c2f186defa993924904829995b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2863.91,3239,2301,938,35861,8,973,27904,5.14864 +0a450b93bbd0545e214c9a793193351e259a1e32771d669a198d0fba1efb72cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,791.232,1975,1377,598,21682,8,618,9748,1.17229 +0a65b64b80dcebb76c352dc449f577b4714d5ecc4a7ab1636d9e5af952bea067,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11540.4,4117,2621,1496,49739,8,3451,61663,14.9515 +0aacd1b2a9a7a61f87d99ef0728de2625def376d3645fe070371a3420c7a589a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3376.2,3920,2783,1137,44084,8,1006,39304,6.19883 +0b5a5d8fe6093b3e6c45425af340542f2a3bd34542ee682b454a49b4aa67705c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,21570,5306,3673,1633,59452,8,20491,154670,39.738 +0bee8fdc0a4660e484baf6cff49eabb8f4a2c3168c967946e6db71687d0511bc,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,15283.6,5306,3673,1633,62598,8,11744,95041,21.2578 +0c68f43a998e91f6c74281e46e857667f366541be2284fd71fc4c1ffd82996a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3163.12,2350,1548,802,28441,8,332,15805,2.86989 +0ccee639439a7f01de07d3403b825b7544b7f26135afdff0360ce08772e3dcff,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3179.9,3309,2177,1132,38578,8,592,29758,5.42069 +0cf8551fd210cf5de103cc4a8d940381b56720edb72d5d99de9b58aa2f2569ae,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8478.85,5099,3604,1495,58973,8,1828,60444,8.28154 +0f76b306846b01e1e74c68b600f2ba9fe1e5d46af433f651fa2f0ce318f7727f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1793.37,1979,1377,602,21981,8,421,9067,1.30256 +0f9d15a24c955761f758c013de78aff07607ff19742191782f36b8c2fae2168d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3133.96,3220,2288,932,36135,8,5755,41690,6.06757 +101813dbc2cd82c9225e1d67ae8811a9df8e03a45923d78f1d21c83cb40e3dfa,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5920.07,4058,2932,1126,47233,8,1675,41046,5.62571 +11aca2d6d83b06bda52d2422b0e2b6b838e96a546f38dc6ebc30ac2e06d5a4e9,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3212.64,2729,1936,793,32083,8,1944,23123,3.16658 +126d36a0c356addecc5d63023bcb7bc80dea30317e5aba3571793a5c61ebaf9c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,956.451,1766,1167,599,20139,8,688,11031,0.823945 +12e68b0b5089d173f44977d0bc3a9a85c4639b3691b74fce9d76d473e3231d38,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4031.48,4352,2861,1491,51623,8,1735,58712,8.22221 +146c661d0892f93dc45fe43d3bdf5e9dac1b905efe3b42cd4d7e009969203e29,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8340.34,5366,3525,1841,67205,8,2182,67141,7.58171 +14e56e8a3ffccdd8d7dee84a4b0c7beff6e12fcc60194b80f09f4f7d76cada03,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11915,5310,3673,1637,59718,8,9685,111280,22.8773 +14ea5ccece18b8bffb5947b963c7d0f9e5a2f1c56d1ccd70e1493be39380ba78,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4358.96,4582,3316,1266,51584,8,2446,59082,8.16986 +168f8a25314745f152b4c4d27441a8a1d4c98e6b6fef5fe601d482eab77bafd5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7334.94,5109,3610,1499,61718,8,1881,53873,7.7169 +16ddfc872fb328839f72f9bd7d6d59da1d99fd679081747c6254398c487fb1dc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,639.354,1670,1070,600,19247,8,264,9558,0.780845 +184cec13061b02fc944b3ee3d77582c469339ff6a52e0dc4a832626fd13442fd,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9272.03,5310,3673,1637,59718,8,7031,70527,16.1077 +19248a80fe2178fd62092756a8cc72ade4d6558a05e6b272bc0f5a48812762ed,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5989.49,5277,3652,1625,58767,8,2626,63032,8.33573 +19935b04b1afdc8890b51f69ff535944eee70782e95845e9762310d6aa40eec1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,579.064,1236,794,442,13884,8,305,4852,0.491991 +1a1c06a66af7443f6136b4ba851fd267ab7fff909953005391a5e81fc0a57af0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1500.26,2731,1941,790,30201,8,633,15281,2.17348 +1ad17a6edd2a5346387af674a30925c64333696d3516ead1e2523db8f70164f2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6436.84,4046,2924,1122,44899,8,1543,44055,6.42034 +1b7b53e5826b74b03f9dc2164811a7f976f22d2cfe33eafa2359179b8a91b6f0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1075.14,1860,1264,596,20792,8,472,9986,1.00964 +1bbeb46bf12047759193db1b6d07e587c7274c1c0b7623c1f146535eb7920965,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4447.05,3226,2288,938,36510,8,2759,36801,6.49673 +1bcfca2846ceffabd1af6106173e4ac515273c3cd36e3f223a7a622745a941ad,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1852.25,2325,1530,795,26394,8,560,15937,1.41769 +1c4137672b9f46074a8bea1111d80bb4049b3297d0b47be31ce79efd1df60327,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2781.61,2325,1530,795,27378,8,430,15591,2.72813 +1cbb4fb20584a2df3ff85a980e992daccf8381fb6da66657c08a35a9f6f61fe3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5391.62,3243,2301,942,37775,8,1172,28223,5.21599 +1da421e660098ad22f7cf645e4955a7d621856372d3d89ace5722c48699b9b5d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,623.536,1856,1264,592,20594,8,302,9945,0.856701 +1dbf42ee67c4b55c5a86c29d821e82932d84939a718d266e518494e0ffd44caf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2326.58,2735,1941,794,30473,8,851,12692,1.78377 +1e97b710f28cc2ce69da71bcd101dcb4bd35a9030f0a0b6d1955e36a58b0b65b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,12651.7,8580,6402,2178,94129,8,9916,105448,40.5777 +1f71e23e551c8ed60353acbeaeb177c66d93eba8dd1d4127d654ea0aa634ce54,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7400.86,5354,3525,1829,63693,8,4016,100243,16.5819 +202fbcb0eaf3a2173b0d1f86dc29d0d1f31403f91ef9ffc3b4539a004d4291d0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3024.07,1674,1070,604,20236,8,310,10067,2.45745 +21b4ac699ad2cbd802516f108e297eae44fcaa4fa6917198659e3bf8d0f2324b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1752.13,2730,1938,792,30076,8,1102,17773,2.87432 +2370931b8c81ddd151a53b0df69696c2453222fb8710bd50c12646f411267648,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,473.166,1325,878,447,14241,8,187,4573,0.369579 +25142c3ac9e62f0301845be186d7838f243977b50106dcdb7b7269608537967d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16208.8,5366,3525,1841,64265,8,4652,95995,16.4779 +256f7a0f25c2590dd389f9974a8fb93c3d0b1d4b98149a7b04e290b770a21d17,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7271.21,5298,3673,1625,59077,8,12081,81515,23.7767 +26f1613b2617a48822770702a0bbac8cc1c0384a271769c883357b944c5ebe8a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,182.409,715,461,254,7651,8,0,0,0.0310824 +26f53ed6235393586f5beac50e9c02ddb551c8112f394470fa9f3c1764d3c5b0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,856786,17076,13341,3735,192805,8,218233,1168305,1441.38 +27507e95cd8529c2ce7e9fcadb691f9c84506e44508d53f94b13bfaa503d7763,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,45439.7,8588,6402,2186,97536,8,23149,144434,64.4278 +27a639ae4f3c7b232b758f7a4b7a8193bbdf2375bc6ad9f0ed832df3259ed18c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1373.64,1860,1266,594,20658,8,419,10594,1.17965 +2915f65a6cfc4ccf2288beea5e5ebb3d1feee0493a7b8fb1b0bbe6115110f04c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10447,7837,5854,1983,85434,8,11186,96516,32.4593 +2a397e2e9ffb11e6b1bdd4bd271d454a5d960113f85ee54f45618eca730a98b2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4173.09,3243,2301,942,36127,8,843,26130,4.73902 +2a3d88962c8df3c83dccaa767c32c47d92ce214b908873271a271a052038d528,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,540.252,1316,869,447,14599,8,183,4384,0.336128 +2a6fe0572eb447f61b2b572c3baf70606f1ce4104009bbe1ecf2db6e0554834a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5430.81,3313,2177,1136,38711,8,578,29008,4.12991 +2abe830de840839cc8700da827e94f67e8231e6dbc5fdba2042dc0a5cfc022ff,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1961.23,3094,2149,945,33706,8,4561,34650,2.96945 +2b23fb6ce337942ebf3b5f99dddf477b2018725b28bccdc7e9a216501a3e1be8,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,89192,10404,7751,2653,121524,8,35393,255576,143.2 +2b46c9bcb4db1d88bc5550d8ed871f0057565ff847c85dc046ff6685df86097d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6416.71,4058,2932,1126,45101,8,2508,46309,7.0559 +2c03def2b6183b004d5bef89a2575278be78259c02e94ea24efac5475d14ed39,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1809.76,2713,1924,789,30408,8,712,13423,1.76708 +2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1361.71,1864,1266,598,20886,8,964,13872,1.28691 +2c48be2530d7932fabd485c4b3ece1fca3639c6cf6d593f0c44e0b8e56601c3b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,47175.5,11135,8504,2631,126459,8,10043,145482,56.8539 +2cf09323b6357707934b8bab13217c744b0633a6bf11f0d7b51b0961f33f9264,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4828,3104,2156,948,35170,8,844,31973,4.86196 +2cff9dca070f35f4b8632474dbe9ed80dff49bec7ad39db8f3dd1046c4d6acb4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4467.43,5073,3597,1476,57478,8,2835,59220,8.37719 +2d527c24410310a430c63e56560a9f6346660fde96829775a284645f63c86f17,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,695.523,1860,1266,594,20658,8,517,10517,1.0021 +2d8643eec9c4c62aeac7390db2bd48954a840858967aad0bbeb00b0424328b76,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11725.9,5079,3597,1482,57732,8,3735,74766,13.7156 +2e8cdd720fcb4ef0bb3bacbef8633ae847d1bca269122e5c51a98e830b9d03bf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3120.77,2350,1548,802,27313,8,418,15636,2.72092 +2ed7c12aa205b907de5d68f8daaa7fed1802f93aaad89e247b789fffd4c42d33,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16990.7,7843,5854,1989,85855,8,7633,97024,30.8048 +2f5479c6c8eed384098dac360bc532cf1934e9f1efb2b3686c5e03c680ba178d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,10429,5306,3673,1633,59452,8,7701,75544,16.0411 +303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3530.81,1850,1257,593,20766,8,379,11971,1.76994 +31ef7d4aea2e6b1e902710d43ae9fc51e98680924221e5c4362ef1ef4651eaa3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3386.23,4049,2930,1119,45238,8,4770,51971,7.21038 +321b859ee1b2e3f9874968cf86a89765dcc94f1daa4f4081f8ad231b50911dd7,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3472.19,3520,2381,1139,40984,8,3904,96041,7.69355 +327885af89164e64baa11fb9e8e4b9af8e7ab53c427d41ad23c216f3877453d6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,694.654,1325,878,447,14805,8,212,4812,0.646128 +3368a03f50cb20d1ca1ec5365507aa3096295da9a97f1688543cb9200aa911c0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,14676,8588,6402,2186,94632,8,6765,85872,24.7064 +33aca880c9efd51e3b11c9b865a5d96e10f7bf573ab2b76d1f56c929dad9b12b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,13176.2,5079,3597,1482,57732,8,6925,81417,15.7646 +34d64d3b3f09c059453e8a7eb2aa2d691cce300d5dbc94e1dd4bae24698c6a06,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,715.521,1762,1167,595,19934,8,256,10329,0.84016 +36c5b829921f254af40d517a9f1455da914471d2a17d7e9286d869735e1f7646,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3964.82,5073,3597,1476,57478,8,2828,58028,8.34405 +3707720aa7119866f34152927dba916a89b9b4980bc74daf4a4ce04c7c83a74e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,23136.9,7843,5854,1989,88415,8,10910,107959,29.2903 +379dc9168e43d8e9d0b62890fa2e276bb4107c588e6360ee40d3c7cdfb1de6f1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6175.92,5114,3610,1504,58789,8,1634,41974,7.64516 +37f84b16de74e01a63c4bdcf999c82d18ebf6d0e03406bd9ee65adb21ae54b71,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2603.74,2734,1941,793,30392,8,675,16106,1.93163 +38b4c712ec417b25f8c1fd58752402dfff72d8f346edac6d35c10d02d216bcd4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3772.51,3104,2156,948,35226,8,751,29001,3.78127 +38e83ff258692eadb3c637027ef94f7c4f1ff1790c584444cba3c4f450281b9d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,52982,11127,8496,2631,135485,8,12391,168556,65.4763 +3921ba5449869b9fca2c8b7808e99e3816492dbfbe55f3956019cbea156ed88d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6438.96,5112,3610,1502,58744,8,4341,60532,8.15447 +395b9abdb72014e256f558ea722b7df71fedf88a40e4bf0f70b34ed75d3e7ce1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1394.5,2707,1924,783,30064,8,788,15786,2.14286 +39ad61e32e7f3ecfed4123371232df4c15298efd0c20d9d3dd956c69d772db27,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3002.76,3239,2301,938,35861,8,973,27904,5.14887 +3b522f278d5967449cb3fc8dcdcf198cc3ecd7bd5f5c03cec433b4bb332433c0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2334.59,3235,2300,935,36199,8,1276,30370,4.11721 +3cbde998e188f4dfded4b435da864ce8b877c66367a6160132a29208b67f19e3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8571.61,5109,3610,1499,58459,8,3506,52995,8.33814 +3cf489848cf21fe1a27e0cdecd87845ba3e67495375dc33d4fc861f33c6039f6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,273.786,1236,794,442,13896,8,109,4450,0.275355 +3d58d5670d05e6b5f03761602fe37e31f464f8e740d8b5d658f543f2e56ca9ce,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6812.85,4589,3316,1273,54218,8,2229,58285,7.44364 +3e2b36379a2919afebb3b79a5e34bd8fd946fbc28059edd6a832ad467f412fc3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,626.761,1856,1264,592,20575,8,326,9895,0.978811 +3e69d6f5b7856ea1d07e24a00ad14594b6ba146d0685bdb57e54a8483065b9d0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1384.5,2565,1783,782,28938,8,955,19368,2.31893 +3edc7a438782c8c667a53e1e8de98b1335e55876bdc718a1196ba66d8824b9f5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2204.94,2322,1531,791,26774,8,4661,23327,3.42086 +3f9aacb091d6566f953a6f7bc9c46073305546a98e45d4e798ad1c3b2bafdff4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7769.88,4806,3325,1481,56210,8,1852,61786,8.39234 +3f9eb14b64530ab041bc300966e5f7fa1a44b578d0360f41468139f57fb9b6d8,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,701.966,1316,869,447,15389,8,0,0,0.141513 +40d5a4d59eb7467a69cb957e595b84c37998dd03e00f071ad1063e219053a924,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3245.66,4040,2924,1116,44649,8,1366,41353,6.65273 +4248a692d844a140c65947d9334b1b07f2b57b9d6ca9bbefd3d11a7bc76d1ef4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2662.56,2322,1531,791,26774,8,362,13873,2.77881 +42af128a10b50d2ba614168ff1e913357115bba1cd35e3a7e93b5bebc8ba2456,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7075.59,4054,2930,1124,45469,8,3684,73635,7.57263 +435a351b429715b8e1cd5932672f1f7a52fd843f3599ce90c7127f27a5906ab1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3472.88,3488,2362,1126,40581,8,1390,41486,7.61202 +458c6b0e91f3e132ee1877e7eaa1d6e887bfe937db034ec7c79e5c13067e57a0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,71202.6,20862,16429,4433,236709,8,9602,121165,66.0884 +45b13b7a52849e075a4815eff8fce183160b2507b29f5f44276bd5b283cce180,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1761.78,2579,1793,786,28601,8,826,18196,2.0476 +46c0155e6d81759ee93e16aa244b6dbf0a7349c44a245165b83dfd4ef442ff9c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,32597.8,11114,8496,2618,124184,8,15039,170009,88.9703 +471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3311.66,1863,1266,597,21835,8,303,10282,2.27321 +47888d1b56a42fa20c5c9457a9527199b1d09ec2b5528820a793234e19f5328e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3264.07,4040,2924,1116,44637,8,1980,44483,6.892 +484ac3f3201370073c6a5c037e22b9f57078489440e357c56a6d9520b4c08955,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1380120.0,20844,16436,4408,233557,8,498838,1688437,4132.21 +496c320b8ef688332fdb62ad282e05824ed7c6897f1e1782de65658d4f17aa51,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3964.92,5104,3610,1494,58090,8,2044,54259,8.29502 +49e2fc3d508cdb66bff21c054a927b03b334d0a849540dffb80c05d3fb77e18b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4810.32,3526,2381,1145,41425,8,1252,42558,7.70899 +49f5aba306220ddb56df3e237238e80ae4455ad2d41ef43856f5175dd6de48fa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8176.08,4589,3316,1273,51858,8,2546,59699,8.33407 +4a009d145752df05b177c81952c03a84a9ddae56fd662e856e212d32cd60dcee,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,445.985,1317,869,448,14660,8,107,3158,0.297181 +4b5c183b293875eb4e0b73aabd4581cbd17ba882b436d0dd49299632ccbbc661,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4210.68,4107,2621,1486,49290,8,3008,60246,8.15272 +4bec91f2bc8464588c77cbc76d4f59803f890548d8fc74c8a3346ff20c5dab37,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2137.43,2571,1783,788,29248,8,904,18832,2.15797 +4c230e355482c3652bd60bf83cf4ad77382cdb0b9abe08c27007683545e8f071,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3021.82,3722,2581,1141,42727,8,2499,49481,6.34042 +4d103dff0316dc6ba3d27838f49dd311510147101f6f497b97412b98eec9dae5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5044,2326,1531,795,28202,8,795,16032,4.34903 +4d5a013b0cfc8432770fee3b3594f7a67323a3f280da178bb34e7332743f96a2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4414.12,2326,1531,795,26958,8,364,13035,3.54087 +4dc032eadf8f2c84d493c917a5356a18eb9633a34763a134563923b85548d7ba,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3844.03,2330,1531,799,27198,8,495,12992,2.81166 +4e4bc1ad622d2addafb58df0886f20fabf9c872e18cba83057410255a6e2f356,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3107.88,3220,2288,932,36135,8,1375,31948,5.32494 +4e6c3b06b6ba4421cdf19aa4425c5ba46ca6421a9b95557cbfcf7c23f95673c8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,14327.9,8580,6402,2178,94141,8,11425,111123,46.945 +4e8c7686d89bded81fddef4fc8ea2e0b4ad22cb6b922a354bdaa316218669561,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,305.266,1313,869,444,14481,8,111,3720,0.3288 +505e49204f509b76db5e8fa0ecb2bc04befc0982ac388ed84d9f004e9d08a70a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5857.53,5114,3610,1504,58789,8,1724,50485,8.03885 +5085cb22f613e8a3f67099037e603983140390c5fcb8244df5383b667978713f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3216.8,3920,2783,1137,44096,8,1153,40430,6.91569 +5128497ce49b3c14f96b33bfecec456cd44208259435d5457c8c779c7f382bcb,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1799.35,2726,1936,790,30292,8,699,15185,2.55558 +5216fc2158f95b1730c1fd3689b0b5a0d9415f13f1dee52b6266266215e21ed9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,9137.48,7837,5854,1983,85434,8,5533,109722,24.3041 +5228181749e5d9e41dbf585a386a3b46df5239edc6c9e853f3c4bfba86708de8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4951.53,5094,3604,1490,58639,8,2474,50561,8.24999 +5248aba6397b3d34d16c24463aa8e1a7a3591f670a094a30becd777ecf915a9a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2600.23,2729,1936,793,30473,8,686,15497,2.07778 +53f9fe6243b44776260bb4960e2aab00cd17c495f0d955d618c7a7ded01a48ab,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4129.05,5104,3610,1494,58096,8,1937,57423,8.30293 +548bd4883b8031f78a5996556af64afcade4691492c8df8ed78f8df468cc652f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3091.2,3098,2149,949,33949,8,1072,27025,2.68433 +55194630a32dfef1e51e9adc4cd97e19a58f98d99e9d0668428a70b57f0539e5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4354.72,4053,2932,1121,44877,8,4442,51709,7.15852 +55dbffbe3c3057d4a4cebcd6be12812ec51a731ee21cade11d5bdc09035f083b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5903.16,3158,2012,1146,39594,8,532,30142,6.48306 +561e359c0dfd474aef398b08edbcaa429b63f38953cd73fb76d42e34595d7066,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11165.7,5099,3604,1495,61857,8,3701,72784,13.4906 +5651d412c1f0510a7ff1b43da9e22935ec2c616c7ad6d2f15b7d8cb00930480c,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4481100.0,20867,16436,4431,246453,8,891686,3512483,7757.25 +568cb19ecec5f12612462493e54e7af582f6b43606359e934281a90751fda3c7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,969.978,1739,1149,590,19373,8,248,7114,0.652094 +57b21c3f6c8f3ceb646f5ce6950949114d86bc1a225d6db5437e6f654821a6c6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4231.64,5273,3652,1621,58497,8,2224,71568,7.95616 +57b759f315f04cd49b38987b95a953459c7c29e4f53de8dd7a16f65c5057c92b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3337.57,2711,1924,787,31874,8,801,17137,3.11203 +58d58a76371ef7762bf96386ce5d2677d1bfc7d7df745d40f4a02bd30006662d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12685.6,5076,3230,1846,64588,8,2679,103365,14.722 +597355ebc6ef3cf4559ca40bef67472ce681fc7773899b3e6d151cf06282e67c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1079.47,2323,1530,793,26307,8,1269,28923,1.56722 +5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,105.102,713,461,252,7361,8,557,4453,0.0895648 +5978abbd9b38469692be7af1f78c5fd15d0736c6192c38ba083c6ca4778659a5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3841.35,5094,3604,1490,58639,8,1311,55094,8.26568 +5b643f5787956a7cc645eb43384ea0fa8631a71c3d429bc8da67e2d26fd720a1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1158.32,1859,1264,595,20738,8,353,9988,0.909669 +5b7f113c4c253c96360a1b1a8d0556ac77d08c5de1df11b21ae6c21fd7a5b10e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10133.3,5064,3230,1834,61076,8,4204,96355,23.0398 +5b8c61f99d31fed9077ef475613e5be6648f55f2a47bcc92c0e5d0e3b41ecb94,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,19871.1,7843,5854,1989,85727,8,11530,108844,32.3834 +5c113822cd66b2d552f8963bfb44ea77942c0912a9d61f00e742058e73472939,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6474.67,4812,3325,1487,56624,8,1458,52985,8.2366 +5c6b8b896ddf71d458fd0af11ccecaa00e0217f98d21c0ac6fc53e49742d5003,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7406.27,4358,2861,1497,54463,8,3399,58637,7.80385 +5c7fbb637ba552db26f14b8bc9914ed528a7f81c6cf0d066f175b9c8709a9310,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,670.551,1670,1070,600,19247,8,264,9558,0.780983 +5d27c27284a1efabd0e614c927db18ff7ba60ac4c6d120867ae49804606d33f6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4029.71,2350,1548,802,27265,8,422,16484,2.60462 +5d3e7651fac0ff61e2af85a19b94e89acbc3264678e3d7ccf3b767afdd415a1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3438.95,3722,2581,1141,42739,8,1428,46037,7.29287 +5e03f49283bdafdd3a0f637bf19dde9b77735fd8f4a0e8cd4e8af9e3bb006e4a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6061.9,3158,2012,1146,37830,8,6561,61517,7.28667 +5e7e1a665985eea7a66215f3b479b1c945f9562025c4281a9ba00f455c187e46,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,32885.9,5076,3230,1846,61648,8,9305,123328,32.1626 +5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6436.58,1979,1377,602,21981,8,436,9745,1.9513 +5fd76f37541dc792dccaecc2dc74a06253b8fe3ab9cd70a8026fb24aa6fa4fef,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3508.55,3098,2149,949,35981,8,857,24069,3.39139 +6048b517bbdeda0a3da0f391f05dfddeeab6a848e1c620d1e152821537c8d307,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4085.09,3722,2581,1141,42739,8,5788,70467,7.30942 +612b13f52e47be29ba0bc3a2be5c4e042a513765e87fb0b8676d152f8a8409f5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3910.15,4053,2932,1121,44889,8,1334,41508,6.01053 +62835d0eb69fc765c6c7a3e9536c90861584976cf492d552c8207ee2c9893abc,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11005.1,5081,3592,1489,61665,8,2742,65991,14.2197 +62c6e2f566388ddc13097f1369c9ad4569c1b4f54f888066e8ab2bfe96d3a0b4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4069.74,5273,3652,1621,58497,8,2224,71568,7.95576 +638dcb77f7553a70d99b39378c3be82213f350c928231057239ca5680b6fd284,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,518.256,1316,869,447,14603,8,222,4388,0.312387 +6396211e6842d6969cce80194013fff987eb3e308c435eccd942b5007dec4639,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3194.54,2733,1938,795,31882,8,601,16127,2.75747 +63f1e1c6c7452e11af1db799f7a380dabc9c9b6edb7083f59d57c4952dfdb16d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2893.75,1863,1266,597,21835,8,382,10557,2.23047 +643f0a85b4d3d3c13ef9cfbbbc20b1dc8f9d837a181ad2ebb84c6823e91829b5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6785.44,3727,2581,1146,43015,8,1010,38777,6.0984 +64e85d066f667ad5185c60b610746320099aafce4e68f71ded08c02a10c15ec2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5707370.0,20867,16436,4431,246453,8,1489558,5574793,10405.8 +64f19a6620a3451dd37087293c226c20f9ba01beb928e880795025bc17642ade,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1575.51,1739,1149,590,20421,8,260,7247,1.11017 +664907ffb2bdbd0cb7b5fb4c3b2c5d9c700b96a8267691ee4330a5dd2f370ead,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,60117.3,10407,7751,2656,117505,8,29456,160280,136.939 +66754667bf567d1f18152078c6781cbd453cccac416c9d06db5087e6a10c51f0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3208.92,3100,2156,944,35023,8,839,29713,4.43972 +674b484eb726690bc9d4878cb217bc6b22f30eac4f8d512be6e2713a303545dd,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3104.21,3220,2288,932,36135,8,5755,41690,6.06734 +67bbbc9a42158b6dfe51a3f52021cdb80ae1535eeeca21150ce10283948d9710,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,112.046,713,461,252,7361,8,557,4453,0.0896132 +67c6242c56a30b0610b4b4df8c9e802609c922c84515ddb24daa8a265ed05798,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1796.49,2713,1924,789,30412,8,624,12812,1.73543 +67f5fac68782f2951e196e4ae8273a46728b821ae433617aaf6d3242d36d5657,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,737.151,1240,794,446,14600,8,0,0,0.216815 +683f174a879de4bb230fe99336ee2df4ccb16d43051717a616a6bd5bfbdabfa5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6172.22,5073,3597,1476,57490,8,2870,62515,14.731 +6875434ae774ccafedbb3e729b922aa0025634c80f76d4fb5549f4901c57e8a3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2019.3,1864,1266,598,20886,8,646,10998,1.23111 +68df20b101dafbea7130c31d0faaf60971ddd458e7cfcd8f30bec787b4203df7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5416.5,4091,2956,1135,45445,8,4557,49531,6.7395 +69355a5e8d4028c466ba29d38fe021eba053515808a48e2f4895a50a0941a160,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1065.54,1860,1264,596,20822,8,365,9195,0.956687 +6ab3700646ed23d7b144c448e625ed94598db5806f3daf3cfe7a2a242cece1db,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7410.33,4806,3325,1481,58766,8,1826,58107,7.78839 +6c64b95d90a6caf1573da836e471273a2845a145a3ce07ccc4124cf7654bc226,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1883.75,2736,1938,798,30419,8,782,13357,2.07542 +6c7a1a5ce29490757a4fd01319c3af2f090f1488a9bd158edeb09a45ef95d8e0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6126.11,3226,2288,938,36510,8,1106,29478,6.1997 +6e1f2b89cfa03bff27e2308fb05c8a9e68e05dad0397cf805d949b6cf09e3064,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,324.559,1313,869,444,14485,8,373,4195,0.359571 +6e2d9a811b1dbbdf1d92a811e3fb65e4ec1a840585342b877f45d7842ec8f114,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1411.02,2730,1938,792,30064,8,619,15735,2.30004 +6ecbb5bc3a035ad7b51348fac58112aa35296e0e6ae9405119a1296fb9384045,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5580.53,5075,3592,1483,58433,8,3493,67375,15.4415 +6f3adead4b151af69acd980b16f5bd8e2ed0f0ae28c615f1c210a8e853827df5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6042,4046,2924,1122,47161,8,1848,42404,6.50539 +6f71d8acff2f8d22f7622dd943331d53409bc982b8d281f34259c40b915c9730,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3936.7,2330,1531,799,27198,8,483,13168,3.35132 +70b8dcc665126c1a6341f0c4e577dbcaefdab47475e576a7d72888677072a109,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3846.75,3098,2149,949,35981,8,1124,35050,3.48767 +7134b69950a5e555fc3439bf93781e0a9a7d79f30137214b96cf3956fb2324e6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3668.23,3150,2012,1138,37492,8,2201,73048,6.85616 +71b18e5d4bd0b04f2c9fed4d07a1cb5c359ffd3fd7034036cedb8b182b135075,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1074.74,2323,1530,793,26295,8,341,14652,1.45079 +7228b93c395db27bb2e8fc8976b27e1025c87d6f7c6463ca60078bae327b213e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4758.69,2781,1832,949,33969,8,1937,30801,4.63674 +728ea222fda0214f17e45d17dd9e9974b21d6bde8c9e88a6ac25d25a46de74cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,12843.4,5354,3525,1829,63705,8,9041,126376,31.9076 +72f2ec3d785c0a14f4295abb781aa2a7f7b023e0c82639e8bda7e1b6fb1e5bbe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5357.91,5104,3610,1494,58108,8,1981,54661,8.33971 +733d7abf574a3c54ca8522ba92c9d0de317b2ca06e621e45dec6c2673a3bd083,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1401.03,1674,1070,604,19452,8,283,8914,1.00189 +734ea7701ae361fbdb717ea3a6e7793574906a8fc6ef1353f0754dc99f858cd3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2078.5,3100,2149,951,34071,8,567,13553,1.50231 +744c046e89479d1befb90b75f08b4a15b2b687a779750b598a1e3f7572ddf98e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3569.97,4087,2956,1131,45114,8,4115,60998,6.88747 +757c9e1a076dc69239f063376288a84a499a2aabb892ed69d39fd637a7f0bd5d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9298.88,5109,3610,1499,58438,8,1320,43497,8.21765 +75ef625090c4c170b36923586cc4ba39c91471211bc12b328b231f06239be71c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3310.99,3375,2442,933,37535,8,1284,21599,3.05343 +76cda9dfd7936f002acb90d01c9ad5dd792fd17e5ccd3ca0f49a2fcc9f510244,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3072.18,2193,1403,790,25682,8,299,14001,5.90394 +78ef90514e8df4b0ad1b8b639c5cba31dc1d71011570850c043b79da490b6f35,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5812.56,5087,3592,1495,59195,8,2816,52241,8.45404 +78efbf308c9f28e54d3cad8847352b9a8b3abd79a7017e40247b96b73692c5a3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12361.7,4820,3334,1486,56476,8,8813,120664,14.9513 +7935760e9618796c7f423ed3035ff53456b7cda07a348ee8060af5cd0aeb85f4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,14663.3,7843,5854,1989,85855,8,6845,87740,24.6145 +79b2a8894050b4767afa5d2ef42656d32485579057f443bc7e5d85c4597ef3d9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3276.46,3100,2156,944,35023,8,709,29850,5.10741 +7a78d4951c1a8b67cebf1dd62ec358ef792f9fd6000319b75cda30b8a24775e6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3195.98,2569,1783,786,30546,8,542,18144,3.25983 +7a95410b6970cc899b5df0fc6c9c83cdd9b96f7cf8d34d6dd8c6220dd5457be4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1216.52,2726,1936,790,30288,8,808,15391,2.11419 +7b4c2358a5ad5bc41a3c0dcd67791b76b50cf252d8ee23e3290bf043f327ffb2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3598.47,3488,2362,1126,40569,8,1142,39320,7.16565 +7b4e94bce09b292527422552f4ddada7ccd62e28804f75b9bc88c4d64fad00db,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2388.08,2729,1936,793,30477,8,623,15347,2.11807 +7b9053fb13e462eae04cb54e9e64f7929247a3ece739347a45fdc8deacd46646,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2916.72,1859,1264,595,21771,8,399,10761,2.09439 +7bd5e487a816e310cf226295707e1f41f4b697bc67bddaaa8c309ea93e77a727,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,154.832,715,461,254,7431,8,31,985,0.0753838 +7c9525e45522c75edc617d4accd3fa5b23a03cff1bc3a39523f13637cacf91d8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,506.153,1737,1149,588,19236,8,243,5443,0.611709 +7d5ef007c15860b85ba57ea7a09ccb08ac2d31f296bc47936d625e7af0883661,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7474.2,5298,3673,1625,59077,8,9257,85199,24.3461 +7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1482010.0,20844,16436,4408,233557,8,548234,2019729,3843.75 +7e6b650d4435c812acac4ca343afb0734e8d22f3d1b64370345d98ece33807a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1183.34,1977,1377,600,21823,8,399,9558,0.909728 +7f37b05dbeb0f5013414a3c61b10aeb8dff6b9ab4ad520a8a9205964a26d102c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3097.71,3235,2300,935,36199,8,1276,30370,4.11745 +8073c7c85295abafcc3ca9dde8e50e775e59175274e1b7d3eec04570581b4469,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1649.08,2565,1783,782,28926,8,709,19482,2.86391 +80ac2372ce125d2b203a9d3c0111ef887a2ba0ab73248e5fd7d258f67025dfda,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6437.82,4592,3316,1276,52067,8,2738,52199,8.05118 +80f73e52da8a244ea0be2c39d85c197bc238b5af594944e77ba4baff43bee2ed,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6225.3,4117,2621,1496,49835,8,2111,75272,7.63856 +821f15ac4478cffdd0f615fe71e0eeb1c47b989f91a47f0e9e8760f5518f4868,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1855.33,2775,1832,943,32281,8,557,24512,3.10161 +82c981cebdc67d969363e30496d5e1278592f85aca9bffae6fb8135aade2369c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4100.31,4049,2930,1119,45237,8,2629,43878,6.32821 +82edf01d7c20302d494f05b2f335473d3535b086de4f8bb170f707cbdc06a7f5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9249.91,5277,3652,1625,58663,8,1749,67067,8.18564 +832bd4924ac7dec74dd3b8e9a14a0e380cac3ca9c041a6c9d765d6dba454895f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,104633,10404,7751,2653,117200,8,38734,248491,176.807 +834873ba45a488c7f8b1d6ad0d1d689ce41cb19ae9186807eabc801920b926a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3081.59,1850,1257,593,21650,8,794,13010,2.23403 +84327bfe5801100327e40bc478403a949d8947fd6a15ddcc2bcb0f06ce32eea6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5728.96,3224,2288,936,36358,8,927,30205,5.90036 +855c207bbe508dd6bc5c8e557648607de72dbe98e732548d00a14f1692fa30e9,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3036.42,2733,1938,795,31882,8,777,15687,3.01651 +859f76e784d7572cac7dbbf5a6b8358da4120fd0da76af247da8bdbe91f209ba,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,795.193,1739,1149,590,19405,8,255,5415,0.635959 +867ec278968f0337e74a78c449201e06acb674774dde6d598377e9a123d1d6cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6078.24,4825,3334,1491,56887,8,1766,61829,8.15386 +86c2f0304e765fa13c3e8d0c970082ec0f321665e56f4caeb7505d1a652b1958,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,24569.5,11114,8496,2618,124184,8,5451,107151,32.4269 +86c635d438e9048aeb8352c427f1b36966fa53086f50512d36c0f265d893155e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6348.77,3524,2381,1143,41227,8,6997,74326,7.5385 +87106998551ed8b22ec13067c8b3a05d47ccb12637e779a1040a9def4e9765b6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2555.9,3098,2149,949,33949,8,833,22856,2.43486 +87a7d382f1e5cd88801c156daf84fd0739c090390db57bd7622d86f7775dbb48,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6350.34,3727,2581,1146,43015,8,1791,41316,6.90988 +89264583d12fec785e020bbb3d0c29872c17db7a2aea98f0c936e42d568a8eb9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5541.49,3224,2288,936,36358,8,1466,31284,5.09327 +8a25ce239e8b745b34e8418960573b61c84d9f9af96681e7f5b54725b86b6bfe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5309.81,5075,3592,1483,58445,8,2382,66426,14.7412 +8a3a0575a6af4727c9e27c2e63a5da1f147282a3478b1278fd9bc8069e8110b1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6624.34,4054,2930,1124,45468,8,1748,41177,5.8758 +8b1db26428e486f04d080ee1a5589c05a5a1251c112b8d2af9dfb6d55ae0dcf4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4787.76,3224,2288,936,38028,8,2472,37155,4.98498 +8b264feb93eae5932741a7f9e9db294c22ea3cb66c4957c56c11a257c0e84e2b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3475.49,3220,2288,932,36135,8,1375,31948,5.32517 +8b439dc017e4e505dd41661ef4684654990664c10d073efcb6cbd7f77d2e1f75,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3044.89,2729,1936,793,32079,8,901,16035,3.18278 +8bf3c36029cc57273c55537c51f36e4d506c54f004db71af04c2f3833513a6ea,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3910.28,5094,3604,1490,58627,8,1354,54858,8.30722 +8d6e8152f2a7acab40571eb94f4ab02bf8dcd2f246844a3366ade55f64647001,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4449.32,3104,2156,948,36382,8,1352,35025,4.71866 +8e4e16238b7a5266c68e8a99bb41c18f3f2b3864ab9b439c946c2d99eeee0364,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7342.41,5064,3230,1834,61088,8,4363,91953,15.6115 +8edd162afaa39a55870723c221d056c251e1846b40ab5ac7042388639fbbca3a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11819.5,5079,3597,1482,59796,8,3578,76643,13.8424 +8f16d86d9b158570c045a8f7cf0c19b66d812d400677574b99f8f2270c9c7e3f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1669.89,3094,2149,945,33706,8,892,22010,2.7846 +8f41589af6050547301acab498a4f5b7894920b712156a7e2fac1664966dd04e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1599.16,2730,1938,792,30064,8,1148,16541,2.64948 +8f61a3cb2dd7e6d2b832e5a18ca464a3f6e93ae0072c5251e2d4f7582069d539,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3057.05,4053,2932,1121,44877,8,2050,42750,6.67817 +8f967cbd48541a625efc6f9e641373a0015889f904fe744dc6d5e7061a7a36fe,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1979.49,2325,1530,795,26442,8,581,16233,1.67653 +90e1185ded530d8fbb2b773076a2fdbdfbb9a725f2fc07b4d8b8280e93069b54,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1323.68,1847,1257,590,20644,8,3410,21289,1.66283 +90e16615aa9873fcba0b9ffeaec5584d26877834691f2b3011096e29281d26ec,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5772.7,4800,3325,1475,55906,8,5705,120717,14.8884 +915f8918d0b3d7d39a423af66871046decda0ae1e8230bbdeb43f12a08e79fc7,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1537.86,2579,1793,786,28589,8,876,16280,2.10021 +91dee20f1695cde5ba284269b5a1cddd4253c16a5e56639578ebbb8855c2c2fa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5312.01,4440,3147,1293,50667,8,1717,52956,7.43839 +94fbb6eb1148ca15aff1945c9b987c281b8ac300531a6c4ed88840629233828f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2360.01,2585,1793,792,28922,8,912,15157,1.93857 +9507e3b4ced4efa84827b4febcf8c4bf480ec914bc957af7ebe337c2a25fa887,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7026.39,4440,3147,1293,50587,8,1692,52454,7.8701 +95dffeb21af47c0da4d0cd31a1d8f2a02c049546ee31d53568c3c87ec796b9b7,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6499.23,3924,2783,1141,45864,8,1678,45114,6.78036 +96e1d05ec560b176485b6da3c8924718e9a3efa0e6605dda68c85a8354cfac91,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1443320.0,20844,16436,4408,233581,8,700829,2655203,4843.77 +97c67ce44962a7b1afacbdf7610259b634c74cf7953cf2b5766bfccf3e347b13,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3578.34,4434,3147,1287,50329,8,1997,53648,7.28891 +9801d683d790b79a89846abab1f9539026a1ca565629939ff874f65922709702,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4600.21,4050,2924,1126,45133,8,2345,44779,6.52967 +99406389a054df8283649e3669fd2a07fa770b3472003d54b6331d59527a15d1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,422.549,1240,794,446,14036,8,103,4068,0.26473 +99c5ac6d121d8b750f0bddfa010f32222a75f7d3c2235a87cc7883a876e3d654,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,798.026,1975,1377,598,21682,8,377,9553,1.18037 +99db0e67a00812cd88e5186525387f7f146332da1adcc35d7218f6d13b32b927,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6204.55,5104,3604,1500,59384,8,1650,51769,8.33136 +99dd837b2fb977cecca2cd812b39804c750269ea4317fa660ded1bfd10361217,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,28200.8,7843,5854,1989,88415,8,13095,128740,35.9572 +9b68c85c1a117037be6720b3f7a9f0c2b040b41c9b725525fb162df7f3a2910c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7909.24,4358,2861,1497,51975,8,969,45703,7.91396 +9be78e91ac11a52ea9632536e893507ce0081319840e6ec750614f774115ce96,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2300.62,3100,2149,951,34071,8,684,14103,1.8593 +9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5307350.0,20881,16450,4431,236975,8,949858,3779328,10022.4 +9c2be28afd9a6d476a647468dccac7801a653e7be6684ac128de72030efcb3f5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1161.53,1859,1264,595,20727,8,305,10055,0.839053 +9c3c55cd54e6074a7e2732ccfaac2e90698141e7e9fb30c946657a21fb186061,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4022.2,5104,3610,1494,58096,8,1777,58426,8.31302 +9c5486163986ecf6d1316c2ad9ab5a154f3847a1b7e01d23f2ad6251435b68dd,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1312210.0,20853,16429,4424,245972,8,261411,1407886,2214.53 +9c6904d7802ccf85e6e37fed810f6c741d114f7244b89d3d8801e90f0cf52cf4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3212.27,1977,1377,600,22871,8,822,11048,2.12357 +9c801bcf5dc6a4fa11be76710e268871e68ba55da83399f00fb9e0390f675962,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3098.94,2569,1783,786,29118,8,1617,21084,2.66028 +9d6512b86417f81a043a4abfa11ada9209f4e3e59b8b56f8f2e234e823c89eab,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4094.81,4049,2930,1119,45225,8,3832,89975,7.82779 +9e71a16c2592f15f94948241daaa4468f4f47ad791aa02a3751e03e97a2d377d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1034.56,1766,1167,599,20107,8,351,10858,0.75633 +9e867e2a560f391fe00509c9a263a7b25e7beaadcc151da54b616a068bff4d1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7404.83,5304,3831,1473,59864,8,1468,61490,14.4768 +9f1faefa3b6d0fde8da0886706df654164a7ba690fa4c6c337401c865e233cfe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3171.96,3520,2381,1139,40996,8,1193,38351,6.84478 +9f330e3d7528eea46bbfbd640e4dc0f7d732bf1fdc1dd9e39c323406b9cede01,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3351.44,4087,2956,1131,45102,8,1767,38543,6.44192 +9faddb505594f68b886e5540640f408e1d511fb47d3fde017a7466621b7d5d8d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11228.7,4358,2861,1497,54463,8,5127,73239,13.756 +9fef160ec7a392c9691777eb5c599e48956eed4b519eb0ef968c6da06a547db3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,640.309,1856,1264,592,20564,8,316,9914,0.898999 +a04dbde35f5eda88bf42a75eaf0d43d9b0665177cfa971469f3e5e9fb8225fd0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5754.89,4057,2930,1127,45699,8,1585,40818,6.47583 +a0bbbe5321ee24c3dbf69f223c4c0e8a0f62a2dfbf1e58c2f9414e2255a0122f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,850.031,1975,1377,598,21682,8,618,9748,1.17243 +a0cd1b349e85c374dac7fd4570d5dc90928422dea74e38afe67defa14d8a13a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6657.63,3224,2288,936,38028,8,7110,91485,6.30523 +a1a2584786c08975b608e6ba0d2d576dc3993ca614b721341a5bfd21f2c4bdac,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3956.08,4352,2861,1491,51635,8,670,40595,7.45242 +a2471fdf2d809899bf51327d180164d2523cb5bc14222327fda360f2f6677b7b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1979.36,2346,1548,798,27043,8,334,15496,2.57625 +a27f90201b577988626385f9254e8b786910326e6e8fb7b6cb1544a744743db8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3366.15,3150,2012,1138,37480,8,1494,43537,7.2959 +a2a435c469f2205b3c9cca2d78b3efb8ea3542308e986ea66d8774b36dcc3f99,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,316.487,1323,878,445,14094,8,166,4721,0.38152 +a2dfa72e3403f1488e1d679e3d8bf35a040fbfb8b52101a438626dfe9587aa58,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2377.49,2711,1924,787,30268,8,1266,30734,1.95116 +a325be32cc0287d707ddde2c5556e868387fda094272991cfe5cd1998091f5a9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5134.63,3158,2012,1146,37902,8,433,24742,6.26043 +a34a2f667fa7daad838285edacb2c038301c3bd073c6d83a9290a6cce2364723,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11295.1,5366,3525,1841,64385,8,2482,78855,16.0043 +a3865ae1eb5fb989347e0391f653d399c19e0050af1fcb79594dc0659bf374f0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6495.42,3924,2783,1141,44292,8,3579,74959,7.06788 +a49d307cfcf3ca9dff031a54cca31086a1882b7c0e65c1da60de651a8c9a79b8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10574.9,7837,5854,1983,85434,8,10534,98410,32.0729 +a4a7c4b150b49a6cf24302d9fd052b821060e59abd5de1589a34c32c368dfefb,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1840,1977,1377,600,21823,8,455,9769,1.15662 +a4ce40350fd94acc8dfa277dc1bcc0cc82a3a600ee96d7a5ca1342cc7a2cc18d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3986.44,5073,3597,1476,57490,8,2761,59642,8.21734 +a4dbf4239260ca8770d6c3f876a98ba92dd42102400396cf211e7f9696e4776c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1500.8,2707,1924,783,30068,8,695,16497,2.08018 +a87f850d84c25316081211e9935add586d4abd88a36e386cc59db72975479dc9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,180.457,715,461,254,7423,8,560,4544,0.0901002 +a8a5fdd87f0511775828ec72413b40065d20587aa2603638713d72f05bc543a2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3134.13,2711,1924,787,31870,8,740,15627,3.02381 +aa6fca326d5509b8593566dd692566d7aae1391f75b3ae9b3f5f2c09409f27c5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4676.26,2781,1832,949,32565,8,707,25709,3.30281 +ab06f38ddf16d391ca4564bd92864ba0d062b576927c24a606894d3b2ba5324f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3487.46,2571,1783,788,29262,8,6635,36704,2.86413 +ab0dc15b9cb918a6625a4f314188fadbf7851cbb0fb04d8237c821251021e07f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,42433.4,10391,7751,2640,116641,8,35646,377527,153.145 +ab4c547d9c6b55b6ba9d99b755dc01399f1a9eca8a3c1a53ec603955459bbb01,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6536.66,3727,2581,1146,45243,8,878,33840,6.79979 +ab65fced530e53ab8e8820b1566667bcc263f0a4616f8fcf0119dca9c2d6adfa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2168.04,1851,1257,594,20861,8,414,11107,1.5309 +abb5959746b98f2e3ca3e372ea9123d269d907763eeaa8974b931b9fe9066d9c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10397.7,5298,3673,1625,59077,8,17088,157373,39.3144 +abff5e6c75b21e295633c7d3920862fb20a7ed5a81d3b6c3d7adefe375a2331f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3515.33,4434,3147,1287,50329,8,1781,51076,7.59026 +aca45a10435494d79a4838efee7c3d968577b0426bebae02d39d196e4cf95d20,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3671.42,4815,3334,1481,56186,8,2414,53301,8.1817 +ad2944e678573fd35127a68e3818c4049d6c7f50988ef9031ad9f2c5669a9ef5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2371.29,1859,1264,595,21741,8,305,9961,1.70667 +ad5b37c3d671388ea80e099a9ee23706555adb1f1768f6393b9643e07c2a141c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5988.72,4364,2861,1503,52449,8,1772,55986,8.07535 +ad6577c2c75004d6adc5e7753d29ccebfb79bf175f4476e183c69c606b5091a6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,590.302,1325,878,447,14217,8,139,4506,0.34056 +aec821f70ed62b536c07791cb8cb7cc48661194d53fe27e8b65b139c13aec39a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1317.35,2726,1936,790,30276,8,759,14872,2.31358 +af7f7518658f6a121da81b663950506b43c7657af03a0b3e3e51e058163a472f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,748.329,1975,1377,598,21682,8,377,9553,1.18051 +afaba40d5fa8903139f80aa365c6ab850a2df54221f9be87383281c552513466,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1811.58,2732,1936,796,30680,8,612,14213,2.1616 +afb6bce329d577a5f52f98b748d2157c1bbc23473bc701cd560fb2b57d85a791,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,309.44,1323,878,445,14106,8,151,4649,0.378169 +afb7051a1f3226c7a23f4d22f224bd36ff41ca896c8e3be24c3d2d7c9da2ffcf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2133.87,2732,1936,796,30684,8,1314,16008,2.20437 +afe82892cb3a54c1449823bb460e1a9e59967e18729277426693d180e0125266,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,21205.3,7843,5854,1989,85727,8,11110,111181,31.961 +b040ac13f61b42c52e957df48581cfd54d36eedaad63da36839fdde9183dece6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3052.7,1766,1167,599,20923,8,228,10193,2.02794 +b13bb2432694ba5fe9768abc03525698986ba13ffe70a7112b90bd7fbad02aec,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8842.37,5079,3597,1482,57828,8,2133,60233,12.7949 +b14c571a562972071899d8eb08ed99fa4dfc944eedf4613632bbc2008ae90e23,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1873900.0,20844,16436,4408,233581,8,738946,2570390,6668.87 +b18203536680dcbad9276ec9d8db50f6ef463cbcbbea5aaeabff959652209e3c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6300.32,5104,3610,1494,58078,8,3809,110454,15.1082 +b1994f8885fd57c17b4817fa3515dcbf21b49b1bcf65f915336b3a9fe858e651,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5483.02,4061,2932,1129,45302,8,4083,46562,6.52935 +b1b2b8caa3156c8273d827504518306416d1e2909060667e4af05787fc173a38,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5984.27,4800,3325,1475,55918,8,7904,99296,16.3801 +b24ab4139208bdc598f3f70510ac9f58334d0af4bcce109a96f1dd48133ae1e3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,959.743,1739,1149,590,19373,8,308,7215,0.627408 +b290214fc63e3dcbf5bb5f263ebdec94aaa0ef84fcfa33180675b6346565b5b7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,28277.1,8588,6402,2186,94488,8,11791,108949,39.9897 +b43c4de7b33424859986111645c0f3535ce55c97d536fdfc7eda147261d92ad3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,9668,5304,3831,1473,59852,8,5829,81909,15.8384 +b4543584e1dea20783dd43c0d72808ddc8596c59707ffb1c7383a0b926b22a5e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6886.08,4058,2932,1126,45101,8,3426,74733,6.61894 +b5699531416576bc6f180919ba95643a25b1287458d36123f55eb76d8d5b47d2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1965.09,3094,2149,945,33706,8,4561,34650,2.96967 +b6027761fc923f4b03b977ca7530948fa7351cd7d5ffe1f0eff7e3b62577325e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3431.8,4049,2930,1119,45226,8,4101,48128,7.43628 +b72a77b55f9675d08f39dfdae6b58ce09d723623c71d67141407f794f2f5792a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1001.91,1860,1264,596,20803,8,744,12584,1.03077 +b7e392cb6562f741b79089dd527327fb8437eb6fb609701512465727cec3acd0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4531.41,2326,1531,795,28202,8,2030,22093,5.07546 +b91d4698bc045a1f72fa7423165efe4caac0ebee02edb3eed9f3846a1bc01bba,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7481.13,4054,2930,1124,47567,8,6808,70130,7.20177 +b9c06d6c62aab9e07e4ff671bd0cfb111de0618a95c82783a2facf40d681bcdd,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3654.93,4352,2861,1491,51635,8,1666,60664,8.20979 +ba0b9ea5e8f2328cb61bd31c0d61dafaec59744031371bbf7d594553e5b1c8c0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1315.97,1863,1266,597,20821,8,408,10424,1.05133 +ba3c3ec1af3ebbaee88b5d85beac349a3d26be07ff2aab68e6ae3790c8c47f3f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3264.35,2569,1783,786,29104,8,884,19554,2.58996 +ba86b8aa2d73b9780ab98d8557143022a9c326ee7dddeebc7e1572fe12abd770,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,66907.9,20876,16436,4440,237190,8,9936,121156,69.7478 +bac1fb9016d7061a81abe52a4479744cd8c8b9b890d0d2df020f2eed60a814e8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12741,5081,3592,1489,58781,8,8190,64546,15.7151 +baf98ab4178a2d5e0ed95a96d12df90ea8f4ddd6fd76e3cb74521afdc686a725,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4895.28,4582,3316,1266,51584,8,2446,59082,8.1702 +bb5648a91b469ef23f1155a766b8e6fd05b14bcf42940e511f7c88aefbc8ec47,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3262.15,2569,1783,786,30560,8,2070,30975,3.92594 +bb9eb5259a99d62dd29494b30eecaa36ff1f6fe5698d4793423bef4137a7b1a9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1017.39,1847,1257,590,20644,8,1010,13357,1.26922 +bbcf7fef3811556bc56b16655a1a8c18732092a7bd82e0c47313134504ed991f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,68275,20876,16436,4440,237190,8,11901,119272,73.1088 +bbcfb05efc5b22e60cea2c2aadf94a452df9e1d1a80e743926b755526773e3cb,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11690.6,5109,3610,1499,58438,8,6207,130018,13.9314 +bbdd48b171ac2a86a8e9c8dc148b5301278cde418f992a4d2efe2d41378c31aa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6444.66,3494,2362,1132,40817,8,1164,39616,6.93193 +bc676605802ed5fb85e5006a7df234667d32237dd086e56b2a8bd20a578155e4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,837165,17070,13335,3735,199450,8,213359,1171620,1353.18 +bcaed7c25140d0d5731f397b188a271512394f14fabe67b495076b23436e1d57,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,502.374,1737,1149,588,19236,8,243,5443,0.611565 +bd4be90c6f160c8932088ffb1f966190355a060720c71113a8784e00d8c2cd57,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4816.56,3924,2783,1141,44364,8,1919,42736,6.62824 +bea0526af2a0ef3f3fc16f4b0daa81f8c077307921ed0cbc02b78a60d008a2a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,10744.1,5076,3230,1846,61768,8,3721,100111,16.4477 +bef168aea12ee9d5a0b12880cb3c69ebc80ff968b9f2500a907c5f3f16f0d369,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7530.89,5298,3673,1625,59077,8,8859,83041,23.9536 +bf95b4aa258e822d108e65d7ce10e99104db1720fb2fe84d3b5f105ff9f2af39,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1132.06,1860,1266,594,20658,8,419,10594,1.17979 +c07c5abd63a1f29258c3989976e428c4e3195ed80d0c8e7c145fba613d394abe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,11003.3,8580,6402,2178,94141,8,9050,97946,32.2612 +c0a0dc04538c4ae54401b99c91b6b743811aab04bbc638360c16b4fc9b915728,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4920.17,2199,1403,796,25921,8,712,19587,4.70544 +c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,400652,17070,13335,3735,192562,8,122197,544974,817.73 +c198fac818f333764f4b5d0b08c72cae3b903978b4a6e19b138902d969dbb86b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1988.07,1859,1264,595,21752,8,372,10428,1.6284 +c1e1e1a8dc3fcef074f8642dc2d3db8001a120f65a6263335206fafe704fb56a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2431.35,2733,1938,795,30242,8,646,15490,2.17952 +c299a3f6d8e558df79357fcea15b8092424ca74e1cd3664e2dc128b62980c065,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,304.492,1313,869,444,14493,8,163,3749,0.333838 +c330aa34330c3723403b96c7b9cbc4ad2b656ef04275965f8e2996e1b57a46a2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2670.68,2733,1938,795,30242,8,590,15794,2.03907 +c407b925a91b430515ea23a95e35cb5075023a6b3f81624547928adfac80ea30,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7808.57,5099,3604,1495,61857,8,2283,62251,7.84759 +c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4839.59,2326,1531,795,26958,8,2116,24405,4.23301 +c50ae7a1597320837a67154b00c457247005405a0d61ae03b18207f42c3d2be5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,633.046,1856,1264,592,20594,8,302,9945,0.856833 +c636f4a25733401ac79f10e9031c1eb15798423352413076b31a5be0f9dd1ef8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,33804.5,8588,6402,2186,94488,8,18283,134769,56.5736 +c640cc60784b5f00e395f07384f65c540c7f59d3766fc098f1c34f8645b0e4ae,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4313.07,2199,1403,796,27097,8,307,13962,5.09543 +c6a6b2aab48ad0047d3e14c25d630fb1e22e3844608af935b5e481c1965a8010,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3883.39,5094,3604,1490,58627,8,2010,62433,8.22645 +c74e506b46863a2dc803d33e1af1b9c9f6e286bbe1e710d9cdfaa284b1c6f7db,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7692.09,5099,3604,1495,58973,8,1835,59762,8.13808 +c867ab59144791c88799ce95599bbd25090dec398595de1c1e35d01b8a7f06ec,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1564.42,2707,1924,783,30080,8,951,16490,2.14116 +c8c8858fad7755abe96b96b28bcbb93492fb84c5213e56797044f8ec51fbdf8a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,8928.37,7837,5854,1983,85434,8,6789,90678,24.054 +c8f846435e761ae39d9095f91dec1ff5daba0e88fbb838766d52cc6d9469aaec,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1152.52,1674,1070,604,19420,8,172,8896,0.839951 +ca043f0850c14f2d99c0a48b1a959387505b2facf3551fa053ddb9c68bfa58b5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3101.11,3313,2177,1136,38783,8,493,26836,3.2243 +caa6c275ceb224cc3efdf55f2c42a0d803b5ce6afe66cff65abaf8a50980b9cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7255.7,4358,2861,1497,51975,8,3095,59900,8.11072 +cb3ba9f21e52b233f5241e5003b1e388d1d1ffec427094d421a66c28df2f292e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,642.02,1762,1167,595,19934,8,256,10329,0.840026 +ccfac7f93fb9565b6c623253e6a722369a005a8479fe7e6d3112942ed39d274f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5546.81,4091,2956,1135,47437,8,2328,42380,6.34357 +cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,987.922,1316,869,447,15385,8,183,4481,0.725161 +cdc476dfd87c9b11d9a46dc7e8aeaa0f3efa9708cac0093587abb35f1228ffe0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1549180.0,20830,16429,4401,233076,8,443078,1855860,4027.99 +cea92f2d552c17f09f2927186ad61dba3f09e3405f0a58a1007d0769d2334c75,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6403.38,4091,2956,1135,45373,8,1734,40697,6.78591 +cf629228e69ab5f293c8c30459175cccac150e85ec84539bfd8d5dc27540cf1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2972.04,3369,2442,927,37121,8,1320,25163,4.09745 +cf93115a0bf38977ba3f8e8bc9ea9378389b50f513ebde8f4d5660d6158ee1ad,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2736.12,2711,1924,787,30264,8,931,17958,1.97305 +cfb91dee56a02f4826b9ee3cdda17de95706a33d8d5e844946a2316281641585,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,377.333,1313,869,444,14497,8,1116,25101,0.418748 +cfe62c4328920f71a44857714728227a3db0ab8e2b1535406ef09b4b6c841719,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,636.406,1856,1264,592,20564,8,316,9914,0.898867 +d000926320c493a1b1062c0f4f5e690e07761f43709e4362c20c25558d5cb175,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1605.55,2346,1548,798,27031,8,335,15513,2.20673 +d011c7fba6d3d11fa56d0962808f364fe43c519590a2c0b0c5d4b9d4000c971f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,15126.3,8588,6402,2186,94632,8,6826,86123,24.2877 +d08d3590e759b910bf4e5c31c39dd187ad6ca894a5da5c26f0e2343ffe9223c5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1772.15,2730,1938,792,30076,8,1612,16281,2.54555 +d0cb084bf73c6a8bf5cd88cfb595b6f89b05a25e54817adec872eb5b699f83c6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3731.05,4815,3334,1481,56174,8,1819,62821,8.34385 +d1b2c9992efda91256f54b5da850272632e2cd7a46cab2a4e81f74cedf156620,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5355.17,3313,2177,1136,40187,8,937,35168,4.77691 +d28acbdfd6c0f8dc93e215ce1a2fc55b369d63cd6b94efdbfaad6045b6f882ea,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,974.173,1847,1257,590,20644,8,1010,13357,1.26936 +d2bf66ca178e3511de17fd1d1baee9bdf66ca21ecd831f227837ce9271dd99b4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,241828,17058,13335,3723,190797,8,113751,616288,753.539 +d3d1ba1cf734596936c45e703ae6c602900ea669015657296757813ef56804c2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7758.26,4820,3334,1486,59032,8,2512,61449,7.84112 +d573733a5742e214054d9dcb2da9184aab7fbeb1af376b55fafb32a18244db01,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5402.52,3732,2581,1151,43342,8,2651,84649,7.43684 +d6ac13401de0afd9760e8f8853fc4fa523bc083e017c97628ace4cc6213b7630,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2636.93,2781,1832,949,32621,8,591,23984,2.88591 +d6dade98259be38e7313f9777ef831cea9573770df96f4f369515d2dfdf64892,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4844.67,3496,2362,1134,41015,8,897,33449,6.95599 +d75c887fb9536d1adc4dd996a2c83002aafd4a33c57da7f3f9ae4b1375acc563,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6416.26,5104,3604,1500,59384,8,1981,59854,8.47782 +d7df934f3b3ba0e8254473ce06758550361bdc3f1491b9f8d9053d63822f60cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1764.03,2731,1941,790,30189,8,4158,57953,2.6842 +d7e09c73e7c387ce90ed94c06031780e15353ec9e09ac3a8d2ce6cb1af0dc6f9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,688.182,1860,1266,594,20658,8,517,10517,1.00223 +d7f5127fbe51e0b6301e5092d44dacf3947138e0a6d52aa0820e05e552dcf8d6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3980.14,3372,2442,930,39222,8,1168,27661,4.24827 +d8130c88625b627297a53f53c8e762e3364a3a80df8f3c156308af5f39bfd89d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3748.33,1850,1257,593,21650,8,531,12139,2.30902 +da0d5cddd84af0e7503d69de955621895f5e4ed02463ce7b994926416ee15e90,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4490.47,4107,2621,1486,49278,8,1914,57190,8.23222 +da292d01044cc894931fcfb089158d65a1cd8e38a789c2f6c73be0fd81859ae3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11693.6,5310,3831,1479,63086,8,2652,73629,14.342 +daa215241452279f6bd5d569a96d188cea9dd668a82c7db3f1463c7b4122bf78,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1287.87,1851,1257,594,20861,8,325,10876,1.2279 +dad4ea991056ab6474c77d2983ea583b7475e7b62144be92e9414819af6c453b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3551.33,2734,1941,793,31998,8,803,16588,3.23431 +db7603182e6c01908b60a5198cc807e5d50446b92f36df74879d8cff59ededf9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1895.54,2565,1783,782,28952,8,3683,32229,2.78948 +e08d820df1d9743f539badc0bc1e61665bee76c052dd71840fb75c3567894d1b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4168.07,5104,3610,1494,58108,8,1594,55705,8.38265 +e0dee47cd630ceac71b5c13970dc2c1445ba7a9a780d4c8dc47ea48a94a76d13,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3910.49,3372,2442,930,37320,8,1120,26394,3.00747 +e0f3c7a20537704888438728a07497f4807978fb801fb82031069eca0220188b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3140.42,3309,2177,1132,38566,8,5606,75969,5.024 +e272c0e4899bd231855a0205e36e42663a9208b44d081e1149dc37bdafe3a33d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11336.5,5109,3610,1499,61671,8,3325,65832,13.2456 +e2b2952cbec18758e829524bf6dbc3429f6a22fa6d02d67a9a769b04e568acda,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2894.64,1739,1149,590,20421,8,229,7101,1.82151 +e2bbfb9a6518269bb6c17eb4174bdb2c455f86b24df806b2a70d3efc3cc2559b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16580.1,5310,3831,1479,60202,8,8747,86424,22.3038 +e2cb022ee2177db3e3a7d0013600ebdd0e836478a0de3e772bf2e43758f48b4f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6297.97,3494,2362,1132,42717,8,797,35952,6.38374 +e3f05dc6a31d4730a876f0feb1b4b2e907503a838b62458292ca91a970a07b98,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2054.76,2322,1531,791,26786,8,500,14190,2.89887 +e5061a18e1dccc8890efc254baf8645d165384227e9b8a2a184fa92c8a56b751,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12804.9,5312,3831,1481,60364,8,5457,68944,14.0536 +e5089b40043a2bd13c56d857868c30d39802c527a9f88960040cf46ad486d533,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5253.12,3240,2300,940,36452,8,2043,31440,4.28569 +e56d749a21bc8a639822cf600ca2b22078c1a5352377f0b84164691f615d3915,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4887.83,4057,2930,1127,45640,8,2631,43623,7.24441 +e5c9e667fbee60ca8a589904e1a7afc30c81394efd58e388a9f63470fa4ea406,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6608.39,4054,2930,1124,47566,8,1763,39458,6.43063 +e66de9b63db44a62c14bf4e342fb5e7c7c704ed8af07cafdc0b0bae1575561d2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1117.04,1859,1264,595,20757,8,286,9923,0.817957 +e70a9ef2b4f8aaa2b009cfae1b6ce00546089bbb5aa32842f75da342b97b5c62,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7970.52,5079,3597,1482,59796,8,2467,65020,7.82318 +e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1251.32,2726,1936,790,30280,8,733,15802,2.15822 +e8dab90c05bca526c4ca4dacc22f9973f240d5a80273896fcca6fa45ea919bb8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,916.404,1739,1149,590,19405,8,271,5748,0.718591 +ea6187c13380778e85ba5d23d7e056bb2abb811dea32e9675213085586a23a06,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4689630.0,20881,16450,4431,236975,8,1180208,4605661,9645.78 +eae6008a7290df6b48bd01fe9114fe7c8405b73d714b28dd55a48088f412303e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2575.79,2193,1403,790,25694,8,255,13536,4.3558 +ec107dc4aa7dbb1386df6da2db1f5b4dd192245085e5beee12265c82c9381edc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3548.75,3369,2442,927,37121,8,1320,25163,4.09768 +ec4b042c8f9a198d1b01cca74aa2bcd5acc11f565478ec964f0ba812e5f5f5c5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4618.5,3732,2581,1151,43342,8,2166,52761,6.90447 +ed81feb815fdb34cb44257fc30a6d9f24de2ed8fffcec1c868977a4d70d4395c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,26603,10391,7751,2640,116641,8,16842,255182,88.5344 +ed822dccbbfc8c02c2e04bd547202571cb79271a808d730d8742d31920e58213,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1608.48,3094,2149,945,33706,8,892,22010,2.78436 +ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3318.12,3722,2581,1141,42727,8,4463,93544,7.18218 +ed8b6e9582c28720d48b5839465e62d1837c0324ea9725c4d8af71b5718e8e81,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,32383.5,8588,6402,2186,97536,8,15627,122298,43.2025 +ed9224086383052b0c3c6e5d47f8be3b109682bde7c4f0606548d115a458c204,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4562.37,4061,2932,1129,45302,8,1402,41147,5.86141 +ee7841a529956537bd53bd0d06c598113f2c32871c93e105a3967b75e9f64199,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7673.95,5109,3610,1499,61718,8,2756,52996,7.88379 +eef410430ef1cdcadce5480be841daef9bd294a8d1a7a1912d559e62eee37fb3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1380.36,1850,1257,593,20766,8,497,11760,1.03943 +ef01a35c4b42507472b9855a2a470b8020f570bb1518eb15334873fe917bf877,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1974.88,2775,1832,943,32281,8,557,24512,3.10185 +ef97c0560bd7e6e8d095f4f4c12bd07e9f93e382ba24f038963f8e1d82c9e5c5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1667.51,2322,1531,791,26786,8,494,13933,2.20254 +f2899679b09262dca05f75c6fe70f8eca2a6daeb2bd59727c37c1b6658e24802,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,14595.1,8580,6402,2178,94129,8,13022,121753,48.0783 +f37d6966a7d23940fbb6746e26270b8f66f95534c8caf0eda6d1a692b7f03694,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6340.31,3524,2381,1143,43127,8,876,36477,6.44549 +f38c4b16a01010ad99c30db049af26efc7fa7366d244e2d8b09c59fb44bc51f6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6043.44,4364,2861,1503,52449,8,1677,58474,8.10386 +f44f61b4bcf0f5938c1b2e6436886c89427480ea10dc0835eb7889990b9aa4ea,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1400.94,1863,1266,597,20821,8,365,10412,0.933528 +f5931bc8ee19786a4ade31e56bdc19d415c4dbfa42da07785765944fe6748326,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5490.98,3727,2581,1146,45243,8,1721,44355,6.24261 +f7041abae337b502aa03618b1c0f40cc693acb9c0bd48efe21b41c42a7849bef,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5229.07,3240,2300,940,38286,8,1162,27920,5.07577 +f77cdd0764d8aa490133369275f4d62d71f47133f6102d90caa9a799c69ddc63,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6273.65,4058,2932,1126,47233,8,2166,42479,6.69212 +f78fe695ba9e97a63ab5ce95b254e44fa166fac58e2775b3ddf94f49ea7903cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4646.35,3241,2300,941,36541,8,1577,27157,4.01477 +f7bef5557bfa7900b2878f94c5cb50ef62b55ae98590c3348883692be9e0fe3e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11713.1,5277,3652,1625,60763,8,2608,83027,11.1826 +f990df9b39aa8360f64fe986746d28d6ce1c06c5498b465aa979d3ad19016ee0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1523.17,2707,1924,783,30076,8,677,16212,2.42093 +f9e4e9cdb24462a34649e1e803328a7ce8332623aa743a6197d0c69af13b02d2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,668.915,1737,1149,588,19236,8,296,6114,0.713344 +fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1364.16,1847,1257,590,20644,8,3410,21289,1.66269 +fbec9f8410da7935d158884bbe39a6d2ec9ea9a9cc81656e0df797d8a91227b0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,576.826,1737,1149,588,19236,8,296,6114,0.713486 +fc3b0f50b62cc1d9992a8e57b9ec42ca803833d372c275e62594a21cd3424008,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,522.676,1317,869,448,14656,8,146,3462,0.308244 +fdd2abfa77414b11517b8142ddf00539bd7f46bcf54d45a1c4c63054c4ad6702,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,436.964,1240,794,446,14012,8,87,4299,0.24253 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Subject: [PATCH 24/42] cpsat: per-problem param tuning + outlier-only stage3 Add three evolution surfaces per phase (GLOBAL_OVERRIDES, SIZE_BUCKETS, STAGE3_OVERRIDES) with constraint-count buckets and outlier-only stage3 overrides. Stage3 sample now picks from Statistics/outliers_top.csv (top-5 residual, capped at MAX_BASELINE_MS). Evaluator resolves params per problem with backward-compat shim for legacy get_params(). extract_best / prepare_phase chain bucket + stage3 dicts across phases. config.yaml system_message embeds full CpSolverParameters reference (275 fields) so the LLM has authoritative knob names in every call. --- input/_lib/extract_best.py | 57 ++- input/_lib/prepare_phase.py | 112 +++++- input/cpsat-bench/evolve/README.md | 46 ++- input/cpsat-bench/evolve/benchmark_final.py | 58 ++- input/cpsat-bench/evolve/build_samples.py | 144 ++++++- input/cpsat-bench/evolve/config.yaml | 212 +++++++++++ input/cpsat-bench/evolve/final_verify.py | 56 ++- .../evolve/phase1_search/initial_program.py | 76 +++- .../evolve/phase2_presolve/initial_program.py | 86 ++++- .../evolve/phase3_lp_cuts/initial_program.py | 84 ++++- .../evolve/phase4_unified/initial_program.py | 46 ++- .../evolve/prepare_phase_unified.py | 10 +- .../evolve/shared/cpsat_params_reference.md | 353 ++++++++++++++++++ input/cpsat-bench/evolve/shared/evaluator.py | 83 +++- input/cpsat-bench/evolve/shared/outliers.json | 191 ++++++++++ .../evolve/shared/stage1_sample.json | 30 +- .../evolve/shared/stage2_sample.json | 30 +- .../evolve/shared/stage3_sample.json | 32 +- .../evolve/shared/stage4_sample.json | 112 +++--- input/cpsat-bench/problems.jsonl | 344 +++++++++++++++++ 20 files changed, 1974 insertions(+), 188 deletions(-) create mode 100644 input/cpsat-bench/evolve/shared/cpsat_params_reference.md create mode 100644 input/cpsat-bench/evolve/shared/outliers.json diff --git a/input/_lib/extract_best.py b/input/_lib/extract_best.py index 7a79536434..5b324723b1 100644 --- a/input/_lib/extract_best.py +++ b/input/_lib/extract_best.py @@ -104,6 +104,59 @@ def main(root, shared, phase_dirs, argv=None): f"expected dict", file=sys.stderr) sys.exit(1) - out = pathlib.Path(shared) / f"phase{n}_best.json" + 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(pathlib.Path(root))} ({len(overrides)} keys)") + 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)") diff --git a/input/_lib/prepare_phase.py b/input/_lib/prepare_phase.py index 39a08219bf..98a8a23496 100644 --- a/input/_lib/prepare_phase.py +++ b/input/_lib/prepare_phase.py @@ -1,12 +1,21 @@ """ -Materialize a unified phase's initial_program.py with a literal dict -containing the union of prior-phase winners (from shared/phaseN_best.json). +Materialize a unified phase's initial_program.py with the union of prior-phase +winners (from shared/phaseN_best.json + optional phaseN_buckets.json + +phaseN_stage3.json). Per-bench `prepare_phase_unified.py` wrappers call `main(root, shared, prior_phases, unified_file)` after all prior phases have completed (and extract_best.py ran for each). Rewrites only the EVOLVE-BLOCK section of the unified phase's initial_program.py. + +Output format inside EVOLVE-BLOCK (cpsat-bench layout): + GLOBAL_OVERRIDES = {...} # union of phaseN_best.json + SIZE_BUCKETS = [(upper, {...})] # merged per-bucket overrides + STAGE3_OVERRIDES = {...} # union of phaseN_stage3.json + +If a bench doesn't ship the bucket/stage3 files, only GLOBAL_OVERRIDES is +written (legacy single-dict layout) — controlled by `dict_name` parameter. """ import json import pathlib @@ -28,30 +37,114 @@ def _load(shared, n): 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(root, shared, prior_phases, unified_file, dict_name="UNIFIED_OVERRIDES"): """ root — bench evolve dir (pathlib.Path) shared — bench evolve/shared dir (pathlib.Path) prior_phases — list of phase ints to merge (e.g. [1, 2, 3]) unified_file — path to the unified phase's initial_program.py - dict_name — variable name written in the EVOLVE-BLOCK (default UNIFIED_OVERRIDES) + dict_name — variable name for the merged global dict. Default + "UNIFIED_OVERRIDES" (legacy). cpsat-bench unified phase + uses "GLOBAL_OVERRIDES"; pass dict_name="GLOBAL_OVERRIDES" + from the wrapper. """ 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() - 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" - ) + + 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, @@ -61,5 +154,6 @@ def main(root, shared, prior_phases, unified_file, dict_name="UNIFIED_OVERRIDES" 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)") + f"({len(merged)} keys in EVOLVE-BLOCK){suffix}") diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index 593b24c89a..2b1d2d61bd 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -45,19 +45,40 @@ input/cpsat-bench/ LLM-mutated initial_program.py ↓ evaluator.py: - 1. get_params() → dict + 1. get_params(problem=None, stage=...) → global dict (workers/locked) 2. LOCKED-violation check (random_seed, num_search_workers, timeout_sec) - 3. stage1 (5 problems, runtime Q1+Q2): - per-problem solver run, invalid_param triggers early 0 + 3. stage1 (5 problems, runtime Q1+Q2 cluster centers): + get_params(problem=p, stage="stage1") per problem + invalid_param triggers early 0 score → gate (cascade_thresholds[0]) → stage2 - 4. stage2 (5 problems, runtime Q3+Q4): - same, gate → stage3 - 5. stage3 (5 problems, runtime Q5): - same, gate (cascade_thresholds[2]) → stage4 (chained inside stage3) - 6. stage4 (20 broad-runtime problems): - final metrics + per-problem artifacts (top 20 entries surfaced) + 4. stage2 (5 problems, runtime Q3+Q4 cluster centers): same + 5. stage3 (5 OUTLIER problems from Statistics/outliers_top.csv, + capped @ MAX_BASELINE_MS): + get_params(problem=p, stage="stage3") — STAGE3_OVERRIDES applied + on top of GLOBAL_OVERRIDES + SIZE_BUCKETS match + score → gate (cascade_thresholds[2]) → stage4 (chained inside stage3) + 6. stage4 (20 broad-runtime problems): final metrics + artifacts ``` +### Per-problem param resolution + +Each phase's `initial_program.py` exposes three evolution surfaces inside its +EVOLVE-BLOCK: + +| Surface | Applied when | Purpose | +|---|---|---| +| `GLOBAL_OVERRIDES` | every problem | baseline params for the phase | +| `SIZE_BUCKETS` | `num_constraints` matches the bucket's upper bound | trade off cuts / probing / subsolver mix by problem size (small <50k / mid 50–150k / large ≥150k) | +| `STAGE3_OVERRIDES` | `stage == "stage3"` AND `problem.is_outlier` | tune outlier-specific knobs without regressing normal problems | + +Apply order inside `get_params()`: +`BASELINE → prior_phase_globals → GLOBAL_OVERRIDES → SIZE_BUCKETS match → +STAGE3_OVERRIDES (gated) → PHASE_LOCKED`. + +`is_outlier` is set from `shared/outliers.json`, generated by `build_samples.py` +from `Statistics/outliers_top.csv`. Reference for valid CP-SAT param names: +`shared/cpsat_params_reference.md` (275 fields, grouped by category). + ## Quick start ```bash @@ -134,7 +155,10 @@ combined_score = geomean( (b_obj/v_obj)^COST_W * (b_ms/v_ms) ) ## Locked params -`random_seed=0`, `num_search_workers=8`, `timeout_sec=-1`. +`random_seed=0`, `num_search_workers` (per-phase: 1 in phase1/2, 8 in phase3/4). Modifying any locked key in `get_params()` returns `combined_score=0` plus a -`locked_violated` artifact identifying the offending key. +`locked_violated` artifact identifying the offending key. Per-problem +`get_params(problem, stage)` calls have locked keys and `num_search_workers` +re-pinned defensively by the evaluator after the call returns, so SIZE_BUCKETS +/ STAGE3_OVERRIDES cannot override them even by accident. diff --git a/input/cpsat-bench/evolve/benchmark_final.py b/input/cpsat-bench/evolve/benchmark_final.py index ea2ab53a56..c1e1ee7538 100644 --- a/input/cpsat-bench/evolve/benchmark_final.py +++ b/input/cpsat-bench/evolve/benchmark_final.py @@ -26,6 +26,7 @@ import argparse import csv import importlib.util +import inspect import json import pathlib import statistics @@ -44,12 +45,43 @@ _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" +_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" _DEFAULT_ITERS = 20 _DEFAULT_TIMEOUT_S = 300 _DECISIVE = ("OPTIMAL", "FEASIBLE") +def _load_outlier_shas(): + if not _OUTLIERS_JSON.exists(): + return set() + try: + d = json.loads(_OUTLIERS_JSON.read_text()) + except (json.JSONDecodeError, OSError): + return set() + return set(d.get("outliers") or {}) + + +def _supports_kwargs(fn, *kwargs): + 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(name in params_ for name in kwargs) + + +def _resolve_params(fn, problem, stage_name): + kwargs = {} + if _supports_kwargs(fn, "problem"): + kwargs["problem"] = problem + if _supports_kwargs(fn, "stage"): + kwargs["stage"] = stage_name + return fn(**kwargs) if kwargs else fn() + + def _load_program(program_path): spec = importlib.util.spec_from_file_location("program", program_path) module = importlib.util.module_from_spec(spec) @@ -61,15 +93,23 @@ def _load_program(program_path): def _load_problem_index(): + outliers = _load_outlier_shas() idx = {} with open(_PROBLEMS_JSONL) as f: for line in f: d = json.loads(line) - idx[d["problem_sha256"]] = { - "sha": d["problem_sha256"], + sha = d["problem_sha256"] + features = d.get("features") or {} + idx[sha] = { + "sha": sha, "problem_filename": d["problem_filename"], "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), "raw_result": (d.get("cpsat_status") or {}).get("result"), + "num_variables": int(features.get("num_variables") or 0), + "num_constraints": int(features.get("num_constraints") or 0), + "num_bool": int(features.get("num_bool") or 0), + "num_int": int(features.get("num_int") or 0), + "is_outlier": sha in outliers, } return idx @@ -154,7 +194,10 @@ def main(): return 2 program = _load_program(program_path) - variant_params = program.get_params() + # No-arg path resolves the "global" params used for worker/block sizing, + # locked-key validation, and summary diff stats. Per-problem variants + # (SIZE_BUCKETS + STAGE3_OVERRIDES) are resolved inside _worker below. + variant_params = _resolve_params(program.get_params, None, "final") _phase_locked = getattr(program, "PHASE_LOCKED", None) _lock = _phase_locked if isinstance(_phase_locked, dict) else LOCKED violations = {k: variant_params.get(k) for k in _lock @@ -195,9 +238,16 @@ def _worker(idx_meta): slot = i % n_parallel v_core = blocks[slot] b_core = baseline_cores[slot] + # Per-problem param resolution. Locked + worker count re-pinned to + # match `variant_params` so core blocks stay consistent. + per_params = _resolve_params(program.get_params, meta, "final") + for k, v in _lock.items(): + per_params[k] = v + if "num_search_workers" in variant_params: + per_params["num_search_workers"] = variant_params["num_search_workers"] t0 = time.monotonic() baseline_runs = _run_repeat(pb, BASELINE, n_iters, timeout_s, b_core, "baseline") - variant_runs = _run_repeat(pb, variant_params, n_iters, timeout_s, v_core, "variant") + variant_runs = _run_repeat(pb, per_params, n_iters, timeout_s, v_core, "variant") dt = time.monotonic() - t0 return i, meta, baseline_runs, variant_runs, v_core, dt diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index b788df8651..f1f8301562 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -13,15 +13,23 @@ Stages (decisive = OPTIMAL or FEASIBLE; this dataset is all OPTIMAL): Runtime is clustered into N_BUCKETS via 1D k-means (Lloyd's). Clusters - are ordered by ascending centroid (c1=fastest, c5=slowest), then merged: + are ordered by ascending centroid (c1=fastest, c5=slowest), then merged. - stage1 (5) center pick from clusters c1+c2 (fast group) - stage2 (5) center pick from clusters c3+c4 (mid group) - stage3 (5) center pick from cluster c5 (slow group) + stage1 (5) center pick from clusters c1+c2 (fast group) — default-param sanity + stage2 (5) center pick from clusters c3+c4 (mid group) — default-param sanity + stage3 (5) outliers from Statistics/outliers_top.csv — per-problem tune target + (top residual, capped at MAX_BASELINE_MS so a single tune + iteration stays in time budget) stage4 (20) quintile-spread broad sample, dedup vs stage1-3 + +Stage3 sample also writes shared/outliers.json (sha -> {residual, baseline_ms, +n_cons, n_vars}) so the evaluator can mark `is_outlier` on problem records and +phase initial_program.py can branch STAGE3_OVERRIDES on it. """ +import csv import json import pathlib +import sys _HERE = pathlib.Path(__file__).resolve().parent _BENCH = _HERE.parent @@ -31,6 +39,13 @@ _STAGE2 = _HERE / "shared" / "stage2_sample.json" _STAGE3 = _HERE / "shared" / "stage3_sample.json" _STAGE4 = _HERE / "shared" / "stage4_sample.json" +_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" + +# outliers_top.csv lives under cpsat-bench/Statistics/ (or its rename "1/"). +_OUTLIERS_CSV_CANDIDATES = [ + _BENCH / "Statistics" / "outliers_top.csv", + _BENCH / "1" / "outliers_top.csv", +] STAGE1_N = 5 STAGE2_N = 5 @@ -42,7 +57,7 @@ STAGE1_STRATEGY = "center" STAGE2_STRATEGY = "center" -STAGE3_STRATEGY = "center" +STAGE3_STRATEGY = "outliers" # was "center" — now picks outliers_top.csv STAGE4_STRATEGY = "spread" _DECISIVE_RESULTS = {"OPTIMAL", "FEASIBLE"} @@ -98,9 +113,105 @@ def _pick(strategy, sorted_rows, n_pick): return _center_pick(sorted_rows, n_pick) if strategy == "spread": return _quintile_spread(sorted_rows, n_pick, N_BUCKETS) + if strategy == "outliers": + # stage3 uses _pick_outliers() directly; this branch should not fire. + raise ValueError("'outliers' strategy requires _pick_outliers(), not _pick()") raise ValueError(f"unknown sample strategy: {strategy!r}") +def _find_outliers_csv(): + for p in _OUTLIERS_CSV_CANDIDATES: + if p.exists(): + return p + return None + + +def _load_outliers_top(csv_path): + """Read outliers_top.csv -> ordered list of {sha, residual, elapsed_ms, + n_vars, n_cons}, sorted by descending residual (already the file's order).""" + rows = [] + with open(csv_path) as f: + reader = csv.DictReader(f) + for r in reader: + try: + rows.append({ + "sha": r["_sha"], + "residual": float(r.get("_residual") or 0.0), + "elapsed_ms": float(r.get("_elapsed_ms") or 0.0), + "n_vars": int(float(r.get("_num_variables") or 0)), + "n_cons": int(float(r.get("_num_constraints") or 0)), + }) + except (KeyError, ValueError) as e: + print(f"warning: skipping malformed outlier row: {e}", + file=sys.stderr) + continue + rows.sort(key=lambda d: -d["residual"]) + return rows + + +def _pick_outliers(rows_by_sha, csv_path, n_pick, max_baseline_ms): + """Pick top-N residual outliers from outliers_top.csv whose baseline_ms is + ≤ max_baseline_ms (so a single tune iteration fits time budget). + + Falls back to slowest-cluster center pick (legacy stage3 logic) if csv is + missing or yields < n_pick eligible entries.""" + if csv_path is None: + print("warning: outliers_top.csv not found — falling back to " + "slow-cluster center pick for stage3", file=sys.stderr) + return None, [] + + csv_rows = _load_outliers_top(csv_path) + picks = [] + used = set() + diag = [] + for c in csv_rows: + if len(picks) >= n_pick: + break + if c["elapsed_ms"] > max_baseline_ms: + diag.append((c["sha"][:12], c["elapsed_ms"], + "skip: > MAX_BASELINE_MS")) + continue + d = rows_by_sha.get(c["sha"]) + if d is None: + diag.append((c["sha"][:12], c["elapsed_ms"], + "skip: not in problems.jsonl")) + continue + if c["sha"] in used: + continue + picks.append(d) + used.add(c["sha"]) + diag.append((c["sha"][:12], c["elapsed_ms"], + f"pick (residual={c['residual']:.3f}, " + f"n_cons={c['n_cons']})")) + + print(f"outliers stage3: from {csv_path.relative_to(_BENCH.parent)}, " + f"picked {len(picks)}/{n_pick} under MAX_BASELINE_MS={max_baseline_ms}ms") + for sha12, ms, note in diag: + print(f" {sha12} {int(ms):>10}ms {note}") + + return picks, csv_rows + + +def _write_outliers_json(picks, csv_all): + """Write shared/outliers.json: {sha: {residual, elapsed_ms, n_vars, n_cons}} + for every entry in outliers_top.csv (not just the stage3 picks). The + evaluator uses this map to set `is_outlier` on problem records.""" + by_sha = {c["sha"]: { + "residual": c["residual"], + "elapsed_ms": c["elapsed_ms"], + "n_vars": c["n_vars"], + "n_cons": c["n_cons"], + } for c in (csv_all or [])} + stage3_shas = [_id_key(d) for d in picks] + payload = { + "stage3_sample": stage3_shas, + "outliers": by_sha, + } + _OUTLIERS_JSON.write_text(json.dumps(payload, indent=2) + "\n") + print(f"wrote {_OUTLIERS_JSON.relative_to(_BENCH.parent)} " + f"({len(by_sha)} outliers, stage3_sample={len(stage3_shas)})") + + def _center_pick(sorted_rows, n_pick): total = len(sorted_rows) if total == 0 or n_pick <= 0: @@ -255,7 +366,26 @@ def _bucket_range(b): s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) - s3 = _pick(STAGE3_STRATEGY, pool_c5, STAGE3_N) + + # Stage3: outliers from Statistics/outliers_top.csv (top residual, capped + # at MAX_BASELINE_MS). Fallback to slow-cluster center pick if csv missing + # or yields nothing usable. + rows_by_sha = {_id_key(d): d for d in rows} + outliers_csv = _find_outliers_csv() + s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv, + STAGE3_N, MAX_BASELINE_MS) \ + if outliers_csv else (None, []) + if s3_outliers: + s3 = s3_outliers + stage3_criteria = (f"top-{STAGE3_N} residual outliers from " + f"{outliers_csv.name}, capped @ " + f"{MAX_BASELINE_MS}ms baseline") + else: + s3 = _center_pick(pool_c5, STAGE3_N) + stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " + "(outliers_top.csv unavailable or empty)") + + _write_outliers_json(s3 if s3_outliers else [], csv_all) # Stage4: broad spread across full decisive pool, dedup vs stage1-3. used = {_id_key(d) for d in (s1 + s2 + s3)} @@ -267,7 +397,7 @@ def _bucket_range(b): _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") - _write_sample(_STAGE3, s3, "stage3", "decisive runtime cluster c5 (slow group)") + _write_sample(_STAGE3, s3, "stage3", stage3_criteria) _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") for label, picks in (("stage1", s1), ("stage2", s2), diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index 327060f2b4..a902c0f17f 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -124,6 +124,218 @@ prompt: Watch num_conflicts and num_branches — a 2x speedup with same conflict count is hardware noise; a 2x with 10x fewer conflicts is a real find. + 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 (5) fast cluster centers — must not regress + stage2 (5) mid cluster centers — must not regress + stage3 (5) OUTLIER problems only — STAGE3_OVERRIDES active here + 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 diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py index 4c6d93d03b..055b557b33 100644 --- a/input/cpsat-bench/evolve/final_verify.py +++ b/input/cpsat-bench/evolve/final_verify.py @@ -28,6 +28,7 @@ pinned via OPENEVOLVE_CORE_RANGE or 1..N). """ import importlib.util +import inspect import json import pathlib import sys @@ -46,11 +47,42 @@ _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" _FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" +_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" TIMEOUT_S = 300 _DECISIVE = ("OPTIMAL", "FEASIBLE") +def _load_outlier_shas(): + if not _OUTLIERS_JSON.exists(): + return set() + try: + d = json.loads(_OUTLIERS_JSON.read_text()) + except (json.JSONDecodeError, OSError): + return set() + return set(d.get("outliers") or {}) + + +def _supports_kwargs(fn, *kwargs): + 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(name in params_ for name in kwargs) + + +def _resolve_params(fn, problem, stage_name): + kwargs = {} + if _supports_kwargs(fn, "problem"): + kwargs["problem"] = problem + if _supports_kwargs(fn, "stage"): + kwargs["stage"] = stage_name + return fn(**kwargs) if kwargs else fn() + + def _load_program(program_path): spec = importlib.util.spec_from_file_location("program", program_path) module = importlib.util.module_from_spec(spec) @@ -62,15 +94,23 @@ def _load_program(program_path): def _load_problem_index(): + outliers = _load_outlier_shas() idx = {} with open(_PROBLEMS_JSONL) as f: for line in f: d = json.loads(line) - idx[d["problem_sha256"]] = { - "sha": d["problem_sha256"], + sha = d["problem_sha256"] + features = d.get("features") or {} + idx[sha] = { + "sha": sha, "problem_filename": d["problem_filename"], "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), "raw_result": (d.get("cpsat_status") or {}).get("result"), + "num_variables": int(features.get("num_variables") or 0), + "num_constraints": int(features.get("num_constraints") or 0), + "num_bool": int(features.get("num_bool") or 0), + "num_int": int(features.get("num_int") or 0), + "is_outlier": sha in outliers, } return idx @@ -107,7 +147,9 @@ def main(): return 2 program = _load_program(program_path) - variant_params = program.get_params() + # final_verify treats stage as "final"; per-problem resolution still + # applies SIZE_BUCKETS but skips STAGE3_OVERRIDES (which is stage3-gated). + variant_params = _resolve_params(program.get_params, None, "final") # Use the program's PHASE_LOCKED if it exposes one (later phases typically # lock num_search_workers higher than baseline); fall back to global LOCKED. _phase_locked = getattr(program, "PHASE_LOCKED", None) @@ -148,8 +190,14 @@ def _measure(idx_meta): slot = i % n_parallel if n_parallel > 0 else 0 v_core = blocks[slot] if n_parallel > 0 else None b_core = baseline_cores[slot] if n_parallel > 0 else None + # Per-problem param resolution. PHASE_LOCKED keys re-pinned defensively. + per_params = _resolve_params(program.get_params, meta, "final") + for k, v in _lock.items(): + per_params[k] = v + if "num_search_workers" in variant_params: + per_params["num_search_workers"] = variant_params["num_search_workers"] b = run_cpsat(pb, BASELINE, TIMEOUT_S, cpu_core=b_core) - v = run_cpsat(pb, variant_params, TIMEOUT_S, cpu_core=v_core) + v = run_cpsat(pb, per_params, TIMEOUT_S, cpu_core=v_core) return i, meta, b, v tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] diff --git a/input/cpsat-bench/evolve/phase1_search/initial_program.py b/input/cpsat-bench/evolve/phase1_search/initial_program.py index 5d2d578566..b529beef5e 100644 --- a/input/cpsat-bench/evolve/phase1_search/initial_program.py +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -1,17 +1,31 @@ """ Phase 1: tune CP-SAT search / subsolver knobs. -Targeted namespace (LLM may add/remove/modify keys in the EVOLVE-BLOCK): +Reference: shared/cpsat_params_reference.md + (full proto field listing — consult before adding keys; invalid names + surface as `invalid_param` and zero the score.) + +Three evolution surfaces live in this file's EVOLVE-BLOCK: + GLOBAL_OVERRIDES — applied to every problem + SIZE_BUCKETS — applied conditionally on `num_constraints` + (small / medium / large; first match wins). + The dataset spans ~7k–246k constraints; thresholds + below split it ~evenly. Adjust freely. + STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND the problem + is in the outlier set (Statistics/outliers_top.csv). + Tune knobs that help long-tail / hard outliers + without regressing fast/mid problems. + +Targeted namespace for phase 1 (search/subsolvers): extra_subsolvers, ignore_subsolvers, interleave_search, - use_feasibility_jump, use_feasibility_pump + use_feasibility_jump, use_feasibility_pump, search_branching, + preferred_variable_order, repair_hint, diversify_lns_params. -Other params stay at BASELINE. -This phase pins num_search_workers=1 (single-worker search) so other knobs -are evaluated without multi-thread / multi-subsolver noise. Phase 3 raises -the worker count to explore subsolver-mix effects. +Other params stay at BASELINE. This phase pins num_search_workers=1 so other +knobs are evaluated without multi-thread / multi-subsolver noise. Phase 3 +raises the worker count to explore subsolver-mix effects. -Do NOT modify locked keys (see PHASE_LOCKED below + baseline_params.LOCKED): - random_seed, num_search_workers +Do NOT modify locked keys (random_seed, num_search_workers). Invalid solver keys cause evaluator to return 0 and surface the offending key. """ import pathlib @@ -30,23 +44,59 @@ # EVOLVE-BLOCK-START -SEARCH_OVERRIDES = { +GLOBAL_OVERRIDES = { "extra_subsolvers": ["default_lp", "no_lp"], "ignore_subsolvers": ["max_lp"], "interleave_search": True, "use_feasibility_jump": False, "use_feasibility_pump": False, } + +# (max_num_constraints_exclusive, override_dict). First match wins; the final +# entry must use float("inf") as the sentinel for "no upper bound". +SIZE_BUCKETS = [ + (50_000, {}), # small problems — keep GLOBAL_OVERRIDES intact + (150_000, {}), # medium + (float("inf"), {}), # large +] + +# Applied ONLY when stage == "stage3" AND problem["is_outlier"] is True. +# Outliers are pre-identified in Statistics/outliers_top.csv (residual log10 +# slowdown vs the runtime ~ vars^a * cons^b regression). Their search budget +# is dominated by conflict learning + LP iter; use this dict to add aggressive +# settings that would hurt simple problems but pay off on hard ones. +STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def get_params(): +def _bucket_override(num_constraints): + for upper, override in SIZE_BUCKETS: + if num_constraints < upper: + return override + return {} + + +def get_params(problem=None, stage=None): p = dict(BASELINE) - p.update(SEARCH_OVERRIDES) + 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) p.update(PHASE_LOCKED) # re-enforce phase lock last return p def get_phase_overrides(): - """Used by extract_best.py — returns ONLY this phase's evolved dict.""" - return dict(SEARCH_OVERRIDES) + """Used by extract_best.py — returns ONLY this phase's evolved GLOBAL dict. + SIZE_BUCKETS / STAGE3_OVERRIDES are extracted via the helpers below.""" + return dict(GLOBAL_OVERRIDES) + + +def get_phase_size_buckets(): + """Returns list[(upper_exclusive, override_dict)] for chaining to next phase.""" + 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 index bd63601882..db91b4e2ef 100644 --- a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -1,14 +1,29 @@ """ Phase 2: tune CP-SAT presolve / probing knobs. +Reference: shared/cpsat_params_reference.md (presolve section, lines ~132-162; +LP/cuts section may also be relevant for probing-LP interactions.) + +Three evolution surfaces in this file's EVOLVE-BLOCK: + GLOBAL_OVERRIDES — applied to every problem + SIZE_BUCKETS — applied conditionally on `num_constraints` + (large problems often want LIGHTER presolve to avoid + spending budget on substitution work that doesn't pay + off; small problems can afford full probing.) + STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND outlier. + Outliers have heavy presolve-time / search-time + imbalance — extra knobs here let you trade presolve + depth vs search depth without hurting normal cases. + Targeted namespace: cp_model_probing_level, cp_model_presolve, symmetry_level, - presolve_use_bva, presolve_bve_threshold -(LLM may explore other presolve_* / probing_* keys.) + presolve_use_bva, presolve_bve_threshold, presolve_substitution_level, + max_presolve_iterations, presolve_probing_deterministic_time_limit, + find_big_linear_overlap, infer_all_diffs, mip_presolve_level. -Inherits phase1 winners (loaded from shared/phase1_best.json if present). -Like phase1, num_search_workers stays at 1 — presolve effects must be measured -without multi-worker search masking them. +Inherits phase1 winners from shared/phase1_*.json. Like phase1, workers stays +at 1 — presolve effects must be measured without multi-worker search masking +them. Phase 3 raises workers. Do NOT modify locked keys (random_seed, num_search_workers). """ @@ -28,30 +43,79 @@ } -def _load_prev(name): +def _load_prev_dict(name): p = _SHARED / name if p.exists(): return json.loads(p.read_text()) return {} -_PHASE1 = _load_prev("phase1_best.json") +def _load_prev_buckets(name): + """Returns list[(upper, override_dict)] or None if file missing. + + File schema: [[upper_or_null, override_dict], ...] (JSON cannot encode inf, + so the writer stores null for the float('inf') sentinel).""" + p = _SHARED / name + if not p.exists(): + return None + raw = json.loads(p.read_text()) + out = [] + for upper, override in raw: + out.append((float("inf") if upper is None else upper, override)) + return out + + +_PHASE1 = _load_prev_dict("phase1_best.json") +_PHASE1_BUCKETS = _load_prev_buckets("phase1_buckets.json") +_PHASE1_STAGE3 = _load_prev_dict("phase1_stage3.json") # EVOLVE-BLOCK-START -PRESOLVE_OVERRIDES = { +GLOBAL_OVERRIDES = { "cp_model_probing_level": 1, } + +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] + +STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def get_params(): +def _merge_bucket(num_constraints): + """Merge phase1 bucket + phase2 bucket. Phase2 wins on conflicts.""" + out = {} + for buckets in (_PHASE1_BUCKETS or [], SIZE_BUCKETS): + for upper, override in buckets: + if num_constraints < upper: + out.update(override) + break + return out + + +def get_params(problem=None, stage=None): p = dict(BASELINE) p.update(_PHASE1) - p.update(PRESOLVE_OVERRIDES) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_merge_bucket(int(problem.get("num_constraints") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(_PHASE1_STAGE3) + p.update(STAGE3_OVERRIDES) p.update(PHASE_LOCKED) # phase1 may have stored workers; re-pin to 1 return p def get_phase_overrides(): - return dict(PRESOLVE_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 index b83c2c2bb5..b14ed1f539 100644 --- a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py +++ b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py @@ -1,21 +1,37 @@ """ Phase 3: tune CP-SAT LP / cuts / MIP-bridge AND subsolver-mix knobs. +Reference: shared/cpsat_params_reference.md (sections "LP/cuts" lines +~164-198, "MIP bridge" ~200-214, "parallel/workers" ~10-32.) + Worker count is RAISED to PHASE3_WORKERS (default 8) for this phase: many subsolvers (max_lp, no_lp, core, quick_restart, reduced_costs, lb_tree_search, probing_search, …) only activate when num_search_workers is large enough. Phase 3's job is to find the best subsolver combination + LP/cuts tuning that works at parallel-search scale. +Three evolution surfaces (see EVOLVE-BLOCK): + GLOBAL_OVERRIDES — applied to every problem + SIZE_BUCKETS — applied conditionally on `num_constraints`. + LARGE problems (≥150k constraints) often suffer from + LP iteration explosion; smaller max_num_cuts, lower + cut_level, dropping `max_lp` from subsolvers can pay + off here. SMALL problems can afford richer LP work. + STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND outlier. + Outliers spend ~30x more LP iterations than baseline + median (see Statistics/outliers_report.txt). Use this + to ship outlier-only LP/cut tuning. + Targeted namespace: max_num_cuts, cut_level, linearization_level, mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance, - extra_subsolvers, ignore_subsolvers, diversify_lns_params, repair_hint -(LLM may explore other *_cuts toggles and subsolver list contents.) + extra_subsolvers, ignore_subsolvers, diversify_lns_params, repair_hint, + add_*_cuts toggles, root_lp_iterations, exploit_*_lp_solution. -Inherits phase1+phase2 winners, but num_search_workers is re-pinned to -PHASE3_WORKERS at the end of get_params() — phases 1/2 ran at workers=1 so -their wins still need to be re-validated at workers=PHASE3_WORKERS here. +Inherits phase1+phase2 winners (+ their size_buckets / stage3_overrides if +present). num_search_workers is re-pinned to PHASE3_WORKERS at the end of +get_params() — phases 1/2 ran at workers=1 so their wins still need to be +re-validated at workers=PHASE3_WORKERS here. Do NOT modify locked keys (random_seed, num_search_workers). """ @@ -37,19 +53,31 @@ } -def _load_prev(name): +def _load_prev_dict(name): p = _SHARED / name if p.exists(): return json.loads(p.read_text()) return {} -_PHASE1 = _load_prev("phase1_best.json") -_PHASE2 = _load_prev("phase2_best.json") +def _load_prev_buckets(name): + p = _SHARED / 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] + + +_PHASE1 = _load_prev_dict("phase1_best.json") +_PHASE2 = _load_prev_dict("phase2_best.json") +_PHASE1_BUCKETS = _load_prev_buckets("phase1_buckets.json") +_PHASE2_BUCKETS = _load_prev_buckets("phase2_buckets.json") +_PHASE1_STAGE3 = _load_prev_dict("phase1_stage3.json") +_PHASE2_STAGE3 = _load_prev_dict("phase2_stage3.json") # EVOLVE-BLOCK-START -LP_CUTS_OVERRIDES = { +GLOBAL_OVERRIDES = { "max_num_cuts": 3000, "cut_level": 1, "mip_max_bound": 1e+07, @@ -57,17 +85,49 @@ def _load_prev(name): "mip_check_precision": 1e-06, "mip_drop_tolerance": 1e-07, } + +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] + +STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def get_params(): +def _merge_bucket(num_constraints): + out = {} + for buckets in (_PHASE1_BUCKETS or [], _PHASE2_BUCKETS or [], SIZE_BUCKETS): + for upper, override in buckets: + if num_constraints < upper: + out.update(override) + break + return out + + +def get_params(problem=None, stage=None): p = dict(BASELINE) p.update(_PHASE1) p.update(_PHASE2) - p.update(LP_CUTS_OVERRIDES) + p.update(GLOBAL_OVERRIDES) + if problem is not None: + p.update(_merge_bucket(int(problem.get("num_constraints") or 0))) + if stage == "stage3" and problem.get("is_outlier"): + p.update(_PHASE1_STAGE3) + p.update(_PHASE2_STAGE3) + p.update(STAGE3_OVERRIDES) p.update(PHASE_LOCKED) # pin workers=PHASE3_WORKERS for this phase return p def get_phase_overrides(): - return dict(LP_CUTS_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/phase4_unified/initial_program.py b/input/cpsat-bench/evolve/phase4_unified/initial_program.py index 2323f85179..34bfd99c65 100644 --- a/input/cpsat-bench/evolve/phase4_unified/initial_program.py +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -1,9 +1,16 @@ """ Phase 4: unified refinement. -EVOLVE-BLOCK below is auto-materialized by prepare_phase_unified.py from -the union of phase{1,2,3}_best.json winners. LLM may then tune all keys -jointly. +Reference: shared/cpsat_params_reference.md (any section may be tuned here.) + +EVOLVE-BLOCK below is auto-materialized by prepare_phase_unified.py from the +union of phase{1,2,3}_best.json winners (GLOBAL_OVERRIDES), plus the merged +SIZE_BUCKETS and STAGE3_OVERRIDES from those phases. LLM may then tune all +three surfaces jointly: + + GLOBAL_OVERRIDES — every-problem params + SIZE_BUCKETS — per-constraint-count overrides + STAGE3_OVERRIDES — outlier-only overrides (stage3 cascade) num_search_workers stays at PHASE4_WORKERS (= phase3 setting) so unified tuning happens at the same parallel-search scale as phase3. @@ -28,16 +35,41 @@ # EVOLVE-BLOCK-START -UNIFIED_OVERRIDES = {} +GLOBAL_OVERRIDES = {} +SIZE_BUCKETS = [ + (50_000, {}), + (150_000, {}), + (float("inf"), {}), +] +STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def get_params(): +def _bucket_override(num_constraints): + for upper, override in SIZE_BUCKETS: + if num_constraints < upper: + return override + return {} + + +def get_params(problem=None, stage=None): p = dict(BASELINE) - p.update(UNIFIED_OVERRIDES) + 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) p.update(PHASE_LOCKED) return p def get_phase_overrides(): - return dict(UNIFIED_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/prepare_phase_unified.py b/input/cpsat-bench/evolve/prepare_phase_unified.py index 9a09e32b0a..a6ae80f94f 100644 --- a/input/cpsat-bench/evolve/prepare_phase_unified.py +++ b/input/cpsat-bench/evolve/prepare_phase_unified.py @@ -1,6 +1,7 @@ """ Thin wrapper: calls _lib.prepare_phase.main with cpsat-bench phase config. -Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json. +Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json +plus phase{1,2,3}_buckets.json / phase{1,2,3}_stage3.json when present. """ import pathlib import sys @@ -18,4 +19,9 @@ PRIOR_PHASES = [1, 2, 3] if __name__ == "__main__": - main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE) + # cpsat-bench unified phase exposes GLOBAL_OVERRIDES (not the legacy + # UNIFIED_OVERRIDES) so prepare_phase writes that name. SIZE_BUCKETS and + # STAGE3_OVERRIDES are appended when prior phases produced bucket/stage3 + # extracts. + main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE, + dict_name="GLOBAL_OVERRIDES") diff --git a/input/cpsat-bench/evolve/shared/cpsat_params_reference.md b/input/cpsat-bench/evolve/shared/cpsat_params_reference.md new file mode 100644 index 0000000000..66b8373964 --- /dev/null +++ b/input/cpsat-bench/evolve/shared/cpsat_params_reference.md @@ -0,0 +1,353 @@ +# CpSolverParameters Reference (ortools 9.14.6206) + +Total fields: **275**. Auto-categorized by name pattern; some fields +appear under a best-fit bucket. Always verify with the upstream proto: + +`ortools/sat/sat_parameters.proto`. Setting any field on `cp_model.CpSolver().parameters` +via `setattr(s.parameters, name, value)` works for all of these. + + +## parallel/workers (19) + +| name | type | default | enum / notes | +|---|---|---|---| +| `diversify_lns_params` | bool | False | | +| `extra_subsolvers` | list | | | +| `filter_subsolvers` | list | | | +| `ignore_subsolvers` | list | | | +| `interleave_search` | bool | False | | +| `lb_relax_num_workers_threshold` | int32 | 16 | | +| `log_subsolver_statistics` | bool | False | | +| `num_full_subsolvers` | int32 | 0 | | +| `num_search_workers` | int32 | 0 | | +| `num_workers` | int32 | 0 | | +| `share_binary_clauses` | bool | True | | +| `share_glue_clauses` | bool | False | | +| `share_glue_clauses_dtime` | double | 1.0 | | +| `share_level_zero_bounds` | bool | True | | +| `share_objective_bounds` | bool | True | | +| `shared_tree_num_workers` | int32 | 0 | | +| `subsolver_params` | list | | | +| `subsolvers` | list | | | +| `use_objective_lb_search` | bool | False | | + +## seed/reproducibility (18) + +| name | type | default | enum / notes | +|---|---|---|---| +| `ignore_names` | bool | True | | +| `instantiate_all_variables` | bool | True | | +| `lns_initial_deterministic_limit` | double | 0.1 | | +| `log_prefix` | string | '' | | +| `log_search_progress` | bool | False | | +| `log_to_response` | bool | False | | +| `log_to_stdout` | bool | True | | +| `max_deterministic_time` | double | inf | | +| `max_num_deterministic_batches` | int32 | 0 | | +| `name` | string | '' | | +| `permute_presolve_constraint_order` | bool | False | | +| `permute_variable_randomly` | bool | False | | +| `presolve_probing_deterministic_time_limit` | double | 30.0 | | +| `probing_deterministic_time_limit` | double | 1.0 | | +| `random_seed` | int32 | 1 | | +| `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 (7) + +| name | type | default | enum / notes | +|---|---|---|---| +| `max_memory_in_mb` | int64 | 10000 | | +| `max_number_of_conflicts` | int64 | 9223372036854775807 | | +| `max_presolve_iterations` | int32 | 3 | | +| `max_time_in_seconds` | double | inf | | +| `stop_after_first_solution` | bool | False | | +| `stop_after_presolve` | bool | False | | +| `stop_after_root_propagation` | bool | False | | + +## search/branching (22) + +| name | type | default | enum / notes | +|---|---|---|---| +| `boolean_encoding_level` | int32 | 1 | | +| `fix_variables_to_their_hinted_value` | bool | False | | +| `hint_conflict_limit` | int32 | 10 | | +| `initial_polarity` | enum | 1 | {POLARITY_TRUE, POLARITY_FALSE, POLARITY_RANDOM} | +| `initial_variables_activity` | double | 0.0 | | +| `polarity_exploit_ls_hints` | bool | False | | +| `polarity_rephase_increment` | int32 | 1000 | | +| `preferred_variable_order` | enum | 0 | {IN_ORDER, IN_REVERSE_ORDER, IN_RANDOM_ORDER} | +| `random_polarity_ratio` | double | 0.0 | | +| `repair_hint` | bool | False | | +| `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 | False | | +| `use_disjunctive_constraint_in_cumulative` | bool | True | | +| `use_dual_scheduling_heuristics` | bool | True | | +| `use_erwa_heuristic` | bool | False | | +| `use_extended_probing` | bool | True | | +| `use_implied_bounds` | bool | True | | +| `use_lns` | bool | True | | +| `use_lns_only` | bool | False | | +| `use_objective_shaving_search` | bool | False | | +| `use_phase_saving` | bool | True | | +| `use_strong_propagation_in_disjunctive` | bool | False | | + +## conflict/restart (18) + +| name | type | default | enum / notes | +|---|---|---|---| +| `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` | int32 | 5000 | | +| `default_restart_algorithms` | string | 'LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART' | | +| `feasibility_jump_restart_factor` | int32 | 1 | | +| `glucose_decay_increment` | double | 0.01 | | +| `glucose_decay_increment_period` | int32 | 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` | int32 | 50 | | +| `restart_running_window_size` | int32 | 50 | | +| `subsumption_during_conflict_analysis` | bool | True | | +| `use_blocking_restart` | bool | False | | +| `variable_activity_decay` | double | 0.8 | | + +## clause db (8) + +| name | type | default | enum / notes | +|---|---|---|---| +| `clause_cleanup_lbd_bound` | int32 | 5 | | +| `clause_cleanup_ordering` | enum | 0 | {CLAUSE_ACTIVITY, CLAUSE_LBD} | +| `clause_cleanup_period` | int32 | 10000 | | +| `clause_cleanup_protection` | enum | 0 | {PROTECTION_NONE, PROTECTION_ALWAYS, PROTECTION_LBD} | +| `clause_cleanup_ratio` | double | 0.5 | | +| `clause_cleanup_target` | int32 | 0 | | +| `pb_cleanup_increment` | int32 | 200 | | +| `pb_cleanup_ratio` | double | 0.5 | | + +## presolve (27) + +| name | type | default | enum / notes | +|---|---|---|---| +| `convert_intervals` | bool | True | | +| `cp_model_presolve` | bool | True | | +| `cp_model_probing_level` | int32 | 2 | | +| `debug_crash_if_presolve_breaks_hint` | bool | False | | +| `debug_max_num_presolve_operations` | int32 | 0 | | +| `disable_constraint_expansion` | bool | False | | +| `encode_complex_linear_constraint_with_integer` | bool | False | | +| `expand_alldiff_constraints` | bool | False | | +| `expand_reservoir_constraints` | bool | True | | +| `expand_reservoir_using_circuit` | bool | False | | +| `find_big_linear_overlap` | bool | True | | +| `infer_all_diffs` | bool | True | | +| `max_pairs_pairwise_reasoning_in_no_overlap_2d` | int32 | 1250 | | +| `max_size_to_create_precedence_literals_in_disjunctive` | int32 | 60 | | +| `merge_at_most_one_work_limit` | double | 100000000.0 | | +| `merge_no_overlap_work_limit` | double | 1000000000000.0 | | +| `mip_presolve_level` | int32 | 2 | | +| `presolve_blocked_clause` | bool | True | | +| `presolve_bva_threshold` | int32 | 1 | | +| `presolve_bve_clause_weight` | int32 | 3 | | +| `presolve_bve_threshold` | int32 | 500 | | +| `presolve_extract_integer_enforcement` | bool | False | | +| `presolve_inclusion_work_limit` | int64 | 100000000 | | +| `presolve_substitution_level` | int32 | 1 | | +| `presolve_use_bva` | bool | True | | +| `remove_fixed_variables_early` | bool | True | | +| `symmetry_level` | int32 | 2 | | + +## LP/cuts (31) + +| name | type | default | enum / notes | +|---|---|---|---| +| `add_cg_cuts` | bool | True | | +| `add_clique_cuts` | bool | True | | +| `add_lin_max_cuts` | bool | True | | +| `add_lp_constraints_lazily` | bool | True | | +| `add_mir_cuts` | bool | True | | +| `add_objective_cut` | bool | False | | +| `add_rlt_cuts` | bool | True | | +| `add_zero_half_cuts` | bool | True | | +| `cut_active_count_decay` | double | 0.8 | | +| `cut_cleanup_target` | int32 | 1000 | | +| `cut_level` | int32 | 1 | | +| `cut_max_active_count_value` | double | 10000000000.0 | | +| `exploit_all_lp_solution` | bool | True | | +| `exploit_all_precedences` | bool | False | | +| `exploit_integer_lp_solution` | bool | True | | +| `feasibility_jump_linearization_level` | int32 | 2 | | +| `linearization_level` | int32 | 1 | | +| `lp_dual_tolerance` | double | 1e-07 | | +| `lp_primal_tolerance` | double | 1e-07 | | +| `max_all_diff_cut_size` | int32 | 64 | | +| `max_consecutive_inactive_count` | int32 | 100 | | +| `max_cut_rounds_at_level_zero` | int32 | 1 | | +| `max_integer_rounding_scaling` | int32 | 600 | | +| `max_num_cuts` | int32 | 10000 | | +| `new_constraints_batch_size` | int32 | 50 | | +| `only_add_cuts_at_level_zero` | bool | False | | +| `use_energetic_reasoning_in_no_overlap_2d` | bool | False | | +| `use_optimization_hints` | bool | True | | +| `use_overload_checker_in_cumulative` | bool | False | | +| `use_pb_resolution` | bool | False | | +| `use_timetabling_in_no_overlap_2d` | bool | False | | + +## MIP bridge (11) + +| name | type | default | enum / notes | +|---|---|---|---| +| `mip_automatically_scale_variables` | bool | True | | +| `mip_check_precision` | double | 0.0001 | | +| `mip_compute_true_objective_bound` | bool | True | | +| `mip_drop_tolerance` | double | 1e-16 | | +| `mip_max_activity_exponent` | int32 | 53 | | +| `mip_max_bound` | double | 10000000.0 | | +| `mip_max_valid_magnitude` | double | 1e+20 | | +| `mip_scale_large_domain` | bool | False | | +| `mip_treat_high_magnitude_bounds_as_infinity` | bool | False | | +| `mip_var_scaling` | double | 1.0 | | +| `mip_wanted_precision` | double | 1e-06 | | + +## LNS (1) + +| name | type | default | enum / notes | +|---|---|---|---| +| `lns_initial_difficulty` | double | 0.5 | | + +## feasibility jump/pump (10) + +| name | type | default | enum / notes | +|---|---|---|---| +| `feasibility_jump_batch_dtime` | double | 0.1 | | +| `feasibility_jump_decay` | double | 0.95 | | +| `feasibility_jump_enable_restarts` | bool | True | | +| `feasibility_jump_max_expanded_constraint_size` | int32 | 500 | | +| `feasibility_jump_var_perburbation_range_ratio` | double | 0.2 | | +| `feasibility_jump_var_randomization_probability` | double | 0.05 | | +| `use_feasibility_jump` | bool | True | | +| `use_feasibility_pump` | bool | True | | +| `violation_ls_compound_move_probability` | double | 0.5 | | +| `violation_ls_perturbation_period` | int32 | 100 | | + +## optimization (10) + +| name | type | default | enum / notes | +|---|---|---|---| +| `auto_detect_greater_than_at_least_one_of` | bool | True | | +| `binary_search_num_conflicts` | int32 | -1 | | +| `exploit_best_solution` | bool | False | | +| `exploit_relaxation_solution` | bool | False | | +| `minimize_reduction_during_pb_resolution` | bool | False | | +| `optimize_with_core` | bool | False | | +| `optimize_with_lb_tree_search` | bool | False | | +| `optimize_with_max_hs` | bool | False | | +| `use_absl_random` | bool | False | | +| `use_precedences_in_disjunctive_constraint` | bool | True | | + +## debug/output (3) + +| name | type | default | enum / notes | +|---|---|---|---| +| `debug_crash_on_bad_hint` | bool | False | | +| `debug_postsolve_with_full_solver` | bool | False | | +| `fp_rounding` | enum | 2 | {NEAREST_INTEGER, LOCK_BASED, ACTIVE_LOCK_BASED, PROPAGATION_ASSISTED} | + +## other/uncategorized (90) + +| name | type | default | enum / notes | +|---|---|---|---| +| `absolute_gap_limit` | double | 0.0001 | | +| `also_bump_variables_in_conflict_reasons` | bool | False | | +| `at_most_one_max_expansion_size` | int32 | 3 | | +| `catch_sigint_signal` | bool | True | | +| `clause_activity_decay` | double | 0.999 | | +| `core_minimization_level` | int32 | 2 | | +| `count_assumption_levels_in_lbd` | bool | True | | +| `cover_optimization` | bool | True | | +| `cp_model_use_sat_presolve` | bool | True | | +| `detect_linearized_product` | bool | False | | +| `detect_table_with_cost` | bool | False | | +| `encode_cumulative_as_reservoir` | bool | False | | +| `enumerate_all_solutions` | bool | False | | +| `exploit_objective` | bool | True | | +| `fill_additional_solutions_in_response` | bool | False | | +| `fill_tightened_domains_in_response` | bool | False | | +| `filter_sat_postsolve_clauses` | bool | False | | +| `find_multiple_cores` | bool | True | | +| `inprocessing_dtime_ratio` | double | 0.2 | | +| `inprocessing_minimization_dtime` | double | 1.0 | | +| `inprocessing_minimization_use_all_orderings` | bool | False | | +| `inprocessing_minimization_use_conflict_analysis` | bool | True | | +| `inprocessing_probing_dtime` | double | 1.0 | | +| `interleave_batch_size` | int32 | 0 | | +| `keep_all_feasible_solutions_in_presolve` | bool | False | | +| `keep_symmetry_in_presolve` | bool | False | | +| `linear_split_size` | int32 | 100 | | +| `max_alldiff_domain_size` | int32 | 256 | | +| `max_clause_activity_value` | double | 1e+20 | | +| `max_domain_size_when_encoding_eq_neq_constraints` | int32 | 16 | | +| `max_lin_max_size_for_expansion` | int32 | 0 | | +| `max_num_intervals_for_timetable_edge_finding` | int32 | 100 | | +| `max_sat_assumption_order` | enum | 0 | {DEFAULT_ASSUMPTION_ORDER, ORDER_ASSUMPTION_BY_DEPTH, ORDER_ASSUMPTION_BY_WEIGHT} | +| `max_sat_reverse_assumption_order` | bool | False | | +| `max_sat_stratification` | enum | 1 | {STRATIFICATION_NONE, STRATIFICATION_DESCENT, STRATIFICATION_ASCENT} | +| `maximum_regions_to_split_in_disconnected_no_overlap_2d` | int32 | 0 | | +| `min_orthogonality_for_lp_constraints` | double | 0.05 | | +| `minimize_shared_clauses` | bool | True | | +| `new_linear_propagation` | bool | True | | +| `no_overlap_2d_boolean_relations_limit` | int32 | 10 | | +| `num_conflicts_before_strategy_changes` | int32 | 0 | | +| `num_violation_ls` | int32 | 0 | | +| `only_solve_ip` | bool | False | | +| `polish_lp_solution` | bool | False | | +| `probing_num_combinations_limit` | int32 | 20000 | | +| `propagation_loop_detection_factor` | double | 10.0 | | +| `pseudo_cost_reliability_threshold` | int64 | 100 | | +| `push_all_tasks_toward_start` | bool | False | | +| `random_branches_ratio` | double | 0.0 | | +| `randomize_search` | bool | False | | +| `relative_gap_limit` | double | 0.0 | | +| `root_lp_iterations` | int32 | 2000 | | +| `routing_cut_dp_effort` | double | 10000000.0 | | +| `routing_cut_max_infeasible_path_length` | int32 | 6 | | +| `routing_cut_subset_size_for_binary_relation_bound` | int32 | 0 | | +| `routing_cut_subset_size_for_exact_binary_relation_bound` | int32 | 8 | | +| `routing_cut_subset_size_for_shortest_paths_bound` | int32 | 8 | | +| `routing_cut_subset_size_for_tight_binary_relation_bound` | int32 | 0 | | +| `save_lp_basis_in_lb_tree_search` | bool | False | | +| `search_random_variable_pool_size` | int64 | 0 | | +| `shared_tree_balance_tolerance` | int32 | 1 | | +| `shared_tree_max_nodes_per_worker` | int32 | 10000 | | +| `shared_tree_open_leaves_per_worker` | double | 2.0 | | +| `shared_tree_split_strategy` | enum | 0 | {SPLIT_STRATEGY_AUTO, SPLIT_STRATEGY_DISCREPANCY, SPLIT_STRATEGY_OBJECTIVE_LB, SPLIT_STRATEGY_BALANCED_TREE, SPLIT_STRATEGY_FIRST_PROPOSAL} | +| `shared_tree_worker_enable_phase_sharing` | bool | True | | +| `shared_tree_worker_enable_trail_sharing` | bool | True | | +| `shared_tree_worker_min_restarts_per_subtree` | int32 | 1 | | +| `shaving_search_threshold` | int64 | 64 | | +| `solution_pool_size` | int32 | 3 | | +| `strategy_change_increase_ratio` | double | 0.0 | | +| `table_compression_level` | int32 | 2 | | +| `use_all_different_for_circuit` | bool | False | | +| `use_area_energetic_reasoning_in_no_overlap_2d` | bool | False | | +| `use_conservative_scale_overload_checker` | bool | False | | +| `use_dynamic_precedence_in_cumulative` | bool | False | | +| `use_dynamic_precedence_in_disjunctive` | bool | False | | +| `use_exact_lp_reason` | bool | True | | +| `use_hard_precedences_in_cumulative` | bool | False | | +| `use_lb_relax_lns` | bool | True | | +| `use_linear3_for_no_overlap_2d_precedences` | bool | True | | +| `use_ls_only` | bool | False | | +| `use_optional_variables` | bool | False | | +| `use_probing_search` | bool | False | | +| `use_rins_lns` | bool | True | | +| `use_sat_inprocessing` | bool | True | | +| `use_shared_tree_search` | bool | False | | +| `use_symmetry_in_lp` | bool | False | | +| `use_timetable_edge_finding_in_cumulative` | bool | False | | +| `use_try_edge_reasoning_in_no_overlap_2d` | bool | False | | +| `variables_shaving_level` | int32 | -1 | | diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index bb60554f46..86fa6d6908 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -6,12 +6,26 @@ Per-problem timeout = max(MIN_TIMEOUT_S, ceil(baseline_ms * TIMEOUT_FACTOR / 1000)). Locked params (see baseline_params.LOCKED) must not deviate — violation => combined_score=0. +Per-problem param resolution: + If the program defines `get_params(problem=None, stage=None)`, the evaluator + calls it once per problem with the problem dict (carries num_constraints, + num_variables, is_outlier) and the active stage name ("stage1"…"stage4"). + This lets phases ship SIZE_BUCKETS (constraint-count conditional overrides) + and STAGE3_OVERRIDES (outlier-only tuning) inside their EVOLVE-BLOCK. + Programs that still expose `get_params()` (no args) keep working — the + evaluator falls back to a single global call and applies it uniformly. + +Worker count / PHASE_LOCKED must stay identical across all problems within +a phase. The evaluator reads them once via the no-arg path; per-problem +get_params() calls must NOT change locked keys or num_search_workers. + Environment overrides: OPENEVOLVE_MAX_PROBLEMS cap stage problem count OPENEVOLVE_PARALLEL_SOLVERS concurrent solver subprocesses (default 1) OPENEVOLVE_PYTHON_BIN python for worker subprocess """ import importlib.util +import inspect import json import math import os @@ -41,8 +55,21 @@ _STAGE2_SAMPLE = _HERE / "stage2_sample.json" _STAGE3_SAMPLE = _HERE / "stage3_sample.json" _STAGE4_SAMPLE = _HERE / "stage4_sample.json" +_OUTLIERS_JSON = _HERE / "outliers.json" _LOCAL_BASELINE = _HERE / "local_baseline.json" + +def _load_outlier_shas(): + """Return set of SHAs flagged by outliers_top.csv (via build_samples.py + writing shared/outliers.json). Empty if file missing.""" + if not _OUTLIERS_JSON.exists(): + return set() + try: + d = json.loads(_OUTLIERS_JSON.read_text()) + except (json.JSONDecodeError, OSError): + return set() + return set(d.get("outliers") or {}) + _PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") _KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", "wall_time", "user_time") @@ -87,6 +114,7 @@ def _load_problems(workers=1): local = {} if _LOCAL_BASELINE.exists(): local = json.loads(_LOCAL_BASELINE.read_text()) + outlier_shas = _load_outlier_shas() rows = [] with open(_PROBLEMS_JSONL) as f: for line in f: @@ -96,6 +124,7 @@ def _load_problems(workers=1): baseline_result = (d.get("cpsat_status") or {}).get("result") baseline_stats = d.get("cpsat_response_stats") or {} baseline_objective = (d.get("cpsat_status") or {}).get("objective_value") + features = d.get("features") or {} lo = _pick_local_baseline(local.get(sha), workers) if lo and lo.get("matches_raw"): baseline_ms = lo["elapsed_ms"] @@ -109,6 +138,11 @@ def _load_problems(workers=1): "baseline_result": baseline_result, "baseline_stats": baseline_stats, "baseline_objective": baseline_objective, + "num_variables": int(features.get("num_variables") or 0), + "num_constraints": int(features.get("num_constraints") or 0), + "num_bool": int(features.get("num_bool") or 0), + "num_int": int(features.get("num_int") or 0), + "is_outlier": sha in outlier_shas, }) return rows @@ -154,6 +188,33 @@ def _err_result(metrics_extra, artifacts): return EvaluationResult(metrics=metrics, artifacts=artifacts) +def _supports_kwargs(fn, *kwargs): + """Return True if `fn` accepts any of the named kwargs (problem/stage).""" + 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(name in params_ for name in kwargs) + + +def _resolve_params(program_get_params, problem, stage_name): + """Call program.get_params(problem=..., stage=...) when supported, else + fall back to get_params() (legacy single-dict per phase). Returns dict.""" + kwargs = {} + if _supports_kwargs(program_get_params, "problem"): + kwargs["problem"] = problem + if _supports_kwargs(program_get_params, "stage"): + kwargs["stage"] = stage_name + p = program_get_params(**kwargs) if kwargs else program_get_params() + if not isinstance(p, dict): + raise TypeError( + f"get_params() returned {type(p).__name__}, expected dict") + return p + + def _evaluate(program_path, problems, stage_name): try: program = _load_program(program_path) @@ -171,10 +232,11 @@ def _evaluate(program_path, problems, stage_name): "stage": stage_name}, ) + # Resolve "global" params (no problem context) once — used for worker + # count, core-block allocation, and PHASE_LOCKED enforcement. Per-problem + # variation only affects non-locked knobs (see _solve below). try: - params = program.get_params() - if not isinstance(params, dict): - raise TypeError(f"get_params() returned {type(params).__name__}, expected dict") + params = _resolve_params(program.get_params, None, stage_name) except Exception as e: return _err_result( {"error": f"get_params() raised: {e}"}, @@ -233,9 +295,22 @@ def _solve(idx_p): idx, p = idx_p input_path = _RAW_DIR / p["input_file"] timeout_s = max(MIN_TIMEOUT_S, math.ceil(p["baseline_ms"] * TIMEOUT_FACTOR / 1000)) + # Per-problem param resolution. Locked keys + num_search_workers MUST + # match the global `params` resolved earlier — re-pin defensively. + 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 "num_search_workers" in params: + per_params["num_search_workers"] = params["num_search_workers"] core = _core_pool.get() try: - r = run_cpsat(input_path, params, timeout_s, python_bin=_PYTHON_BIN, cpu_core=core) + r = run_cpsat(input_path, per_params, timeout_s, + python_bin=_PYTHON_BIN, cpu_core=core) finally: _core_pool.put(core) return idx, p, r, core, timeout_s diff --git a/input/cpsat-bench/evolve/shared/outliers.json b/input/cpsat-bench/evolve/shared/outliers.json new file mode 100644 index 0000000000..18a77fc3cb --- /dev/null +++ b/input/cpsat-bench/evolve/shared/outliers.json @@ -0,0 +1,191 @@ +{ + "stage3_sample": [ + "5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746", + "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", + "d8130c88625b627297a53f53c8e762e3364a3a80df8f3c156308af5f39bfd89d", + "e2b2952cbec18758e829524bf6dbc3429f6a22fa6d02d67a9a769b04e568acda", + "471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce" + ], + "outliers": { + "9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e": { + "residual": 1.2970725496824338, + "elapsed_ms": 5307350.0, + "n_vars": 20881, + "n_cons": 236975 + }, + "ea6187c13380778e85ba5d23d7e056bb2abb811dea32e9675213085586a23a06": { + "residual": 1.2433334003277663, + "elapsed_ms": 4689630.0, + "n_vars": 20881, + "n_cons": 236975 + }, + "64e85d066f667ad5185c60b610746320099aafce4e68f71ded08c02a10c15ec2": { + "residual": 1.2381431698374108, + "elapsed_ms": 5707370.0, + "n_vars": 20867, + "n_cons": 246453 + }, + 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"n_vars": 2326, + "n_cons": 26958 + }, + "9c6904d7802ccf85e6e37fed810f6c741d114f7244b89d3d8801e90f0cf52cf4": { + "residual": 0.403130605161544, + "elapsed_ms": 3212.27, + "n_vars": 1977, + "n_cons": 22871 + }, + "7b9053fb13e462eae04cb54e9e64f7929247a3ece739347a45fdc8deacd46646": { + "residual": 0.39503502501284604, + "elapsed_ms": 2916.72, + "n_vars": 1859, + "n_cons": 21771 + }, + "c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c": { + "residual": 0.39114990000400063, + "elapsed_ms": 400652.0, + "n_vars": 17070, + "n_cons": 192562 + }, + "63f1e1c6c7452e11af1db799f7a380dabc9c9b6edb7083f59d57c4952dfdb16d": { + "residual": 0.38764579685835177, + "elapsed_ms": 2893.75, + "n_vars": 1863, + "n_cons": 21835 + } + } +} diff --git a/input/cpsat-bench/evolve/shared/stage1_sample.json b/input/cpsat-bench/evolve/shared/stage1_sample.json index 1ae95ce24a..5a4185a1f9 100644 --- a/input/cpsat-bench/evolve/shared/stage1_sample.json +++ b/input/cpsat-bench/evolve/shared/stage1_sample.json @@ -2,37 +2,37 @@ "selection": "5 decisive runtime clusters c1+c2 (fast group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ - "bb9eb5259a99d62dd29494b30eecaa36ff1f6fe5698d4793423bef4137a7b1a9", - "71b18e5d4bd0b04f2c9fed4d07a1cb5c359ffd3fd7034036cedb8b182b135075", - "e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15", - "aec821f70ed62b536c07791cb8cb7cc48661194d53fe27e8b65b139c13aec39a", - "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac" + "90e1185ded530d8fbb2b773076a2fdbdfbb9a725f2fc07b4d8b8280e93069b54", + "2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137", + "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac", + "27a639ae4f3c7b232b758f7a4b7a8193bbdf2375bc6ad9f0ed832df3259ed18c", + "eef410430ef1cdcadce5480be841daef9bd294a8d1a7a1912d559e62eee37fb3" ], "summary": [ { - "sha": "bb9eb5259a99", + "sha": "90e1185ded53", "baseline_result": "OPTIMAL", - "baseline_ms": 1017.39 + "baseline_ms": 1323.68 }, { - "sha": "71b18e5d4bd0", + "sha": "2c283b1f086e", "baseline_result": "OPTIMAL", - "baseline_ms": 1074.74 + "baseline_ms": 1361.71 }, { - "sha": "e848603289cc", + "sha": "fa619c63ddf9", "baseline_result": "OPTIMAL", - "baseline_ms": 1251.32 + "baseline_ms": 1364.16 }, { - "sha": "aec821f70ed6", + "sha": "27a639ae4f3c", "baseline_result": "OPTIMAL", - "baseline_ms": 1317.35 + "baseline_ms": 1373.64 }, { - "sha": "fa619c63ddf9", + "sha": "eef410430ef1", "baseline_result": "OPTIMAL", - "baseline_ms": 1364.16 + "baseline_ms": 1380.36 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage2_sample.json b/input/cpsat-bench/evolve/shared/stage2_sample.json index 80ea8e73c8..8630eff9fd 100644 --- a/input/cpsat-bench/evolve/shared/stage2_sample.json +++ b/input/cpsat-bench/evolve/shared/stage2_sample.json @@ -2,37 +2,37 @@ "selection": "5 decisive runtime clusters c3+c4 (mid group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ - "b6027761fc923f4b03b977ca7530948fa7351cd7d5ffe1f0eff7e3b62577325e", - "321b859ee1b2e3f9874968cf86a89765dcc94f1daa4f4081f8ad231b50911dd7", - "97c67ce44962a7b1afacbdf7610259b634c74cf7953cf2b5766bfccf3e347b13", - "7b4c2358a5ad5bc41a3c0dcd67791b76b50cf252d8ee23e3290bf043f327ffb2", - "d0cb084bf73c6a8bf5cd88cfb595b6f89b05a25e54817adec872eb5b699f83c6" + "aa6fca326d5509b8593566dd692566d7aae1391f75b3ae9b3f5f2c09409f27c5", + "7228b93c395db27bb2e8fc8976b27e1025c87d6f7c6463ca60078bae327b213e", + "8b1db26428e486f04d080ee1a5589c05a5a1251c112b8d2af9dfb6d55ae0dcf4", + "49e2fc3d508cdb66bff21c054a927b03b334d0a849540dffb80c05d3fb77e18b", + "bd4be90c6f160c8932088ffb1f966190355a060720c71113a8784e00d8c2cd57" ], "summary": [ { - "sha": "b6027761fc92", + "sha": "aa6fca326d55", "baseline_result": "OPTIMAL", - "baseline_ms": 3431.8 + "baseline_ms": 4676.26 }, { - "sha": "321b859ee1b2", + "sha": "7228b93c395d", "baseline_result": "OPTIMAL", - "baseline_ms": 3472.19 + "baseline_ms": 4758.69 }, { - "sha": 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"sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "no_lp", "problem_sha256": "ffb713809ac8c43a2c5b9cf1de5f841511eab9db95cd9bd4ac5653992f9b2b14", "applied_params_hash": "fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c", "problem_filename": "ffb713809ac8c43a2c5b9cf1de5f841511eab9db95cd9bd4ac5653992f9b2b14.cpsat.pb"} From 1c8b7cf338524d843509d82b88eac0decb41d53a Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 26 May 2026 14:43:44 +0900 Subject: [PATCH 25/42] update default params --- input/cpsat-bench/evolve/build_samples.py | 10 +++++----- input/cpsat-bench/evolve/config.yaml | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index f1f8301562..d3d4e84eb5 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -47,16 +47,16 @@ _BENCH / "1" / "outliers_top.csv", ] -STAGE1_N = 5 -STAGE2_N = 5 +STAGE1_N = 10 +STAGE2_N = 10 STAGE3_N = 5 STAGE4_N = 20 N_BUCKETS = 5 MAX_BASELINE_MS = 120_000 # cap — exclude > 2 min monsters from sample pool -OUTLIER_IQR_K = 3.0 +OUTLIER_IQR_K = float('inf') -STAGE1_STRATEGY = "center" -STAGE2_STRATEGY = "center" +STAGE1_STRATEGY = "spread" +STAGE2_STRATEGY = "spread" STAGE3_STRATEGY = "outliers" # was "center" — now picks outliers_top.csv STAGE4_STRATEGY = "spread" diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index a902c0f17f..05864093df 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -8,11 +8,11 @@ bench: phases: - dir: phase1_search - iters: 60 + iters: 40 - dir: phase2_presolve - iters: 100 + iters: 40 - dir: phase3_lp_cuts - iters: 80 + iters: 40 - dir: phase4_unified iters: 40 unified_prepare_script: prepare_phase_unified.py @@ -34,7 +34,7 @@ bench: # - Env OPENEVOLVE_PARALLEL_SOLVERS overrides this value. parallel_solvers: 8 -max_iterations: 60 +max_iterations: 40 checkpoint_interval: 10 log_level: "INFO" random_seed: 42 From e51f4abd6b44e78b2c7f1973c64ccff4bc3909da Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 26 May 2026 15:11:55 +0900 Subject: [PATCH 26/42] tune stage3 sampling --- input/cpsat-bench/evolve/README.md | 17 +- input/cpsat-bench/evolve/build_samples.py | 160 +++++++++++++----- input/cpsat-bench/evolve/config.yaml | 12 +- input/cpsat-bench/evolve/rebaseline_local.py | 73 +++++--- input/cpsat-bench/evolve/shared/evaluator.py | 16 ++ input/cpsat-bench/evolve/shared/outliers.json | 6 +- .../evolve/shared/stage1_sample.json | 34 +++- .../evolve/shared/stage2_sample.json | 34 +++- .../evolve/shared/stage3_sample.json | 20 +-- .../evolve/shared/stage4_sample.json | 106 ++++++------ 10 files changed, 336 insertions(+), 142 deletions(-) diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index 2b1d2d61bd..a073368229 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -47,19 +47,28 @@ LLM-mutated initial_program.py evaluator.py: 1. get_params(problem=None, stage=...) → global dict (workers/locked) 2. LOCKED-violation check (random_seed, num_search_workers, timeout_sec) - 3. stage1 (5 problems, runtime Q1+Q2 cluster centers): + 3. stage1 (10 problems, runtime Q1+Q2 cluster centers): get_params(problem=p, stage="stage1") per problem invalid_param triggers early 0 score → gate (cascade_thresholds[0]) → stage2 - 4. stage2 (5 problems, runtime Q3+Q4 cluster centers): same - 5. stage3 (5 OUTLIER problems from Statistics/outliers_top.csv, - capped @ MAX_BASELINE_MS): + 4. stage2 (10 problems, runtime Q3+Q4 cluster centers): same + 5. stage3 (5 OUTLIER problems, stratified small/mid/large @ cap=1.5M ms, + phase3/4 only — phase1/2 skips stage3 and goes direct to stage4): get_params(problem=p, stage="stage3") — STAGE3_OVERRIDES applied on top of GLOBAL_OVERRIDES + SIZE_BUCKETS match score → gate (cascade_thresholds[2]) → stage4 (chained inside stage3) 6. stage4 (20 broad-runtime problems): final metrics + artifacts ``` +### Why phase1/2 skip stage3 + +Stage3 holds outlier problems whose recorded baseline came from W=8 runs +(median ~25min). Running them at W=1 (phase1/2 PHASE_LOCKED) takes ~10× +longer with no useful signal — phase1/2 tune knobs (presolve, single-worker +search) that the outliers don't really exercise. `evaluate_stage3` checks +`_peek_workers(program_path)` and, when W=1, returns stage4 metrics +directly with a `stage3_policy=skipped` artifact note. + ### Per-problem param resolution Each phase's `initial_program.py` exposes three evolution surfaces inside its diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index d3d4e84eb5..bb57786c6e 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -18,8 +18,9 @@ stage1 (5) center pick from clusters c1+c2 (fast group) — default-param sanity stage2 (5) center pick from clusters c3+c4 (mid group) — default-param sanity stage3 (5) outliers from Statistics/outliers_top.csv — per-problem tune target - (top residual, capped at MAX_BASELINE_MS so a single tune - iteration stays in time budget) + (top residual, capped at STAGE3_MAX_BASELINE_MS — higher than + MAX_BASELINE_MS so genuinely slow outliers enter the tune set; + still bounded so a single evolve iteration finishes) stage4 (20) quintile-spread broad sample, dedup vs stage1-3 Stage3 sample also writes shared/outliers.json (sha -> {residual, baseline_ms, @@ -52,11 +53,30 @@ STAGE3_N = 5 STAGE4_N = 20 N_BUCKETS = 5 -MAX_BASELINE_MS = 120_000 # cap — exclude > 2 min monsters from sample pool -OUTLIER_IQR_K = float('inf') - -STAGE1_STRATEGY = "spread" -STAGE2_STRATEGY = "spread" +# Global cap for stage1/2/4 sample pool. Anything slower than 2 min skews +# the quintile clustering. +MAX_BASELINE_MS = 120_000 +# Stage3 (outlier-only) gets a higher cap so genuinely slow outliers can +# enter the tune set. 25 min ≈ what one stage3 problem can chew through +# under W=8 (raw-data times are W=8-equivalent). Variant timeout = +# baseline_ms * 1.3 in evaluator. +STAGE3_MAX_BASELINE_MS = 1_500_000 +# Stratified stage3 pick — outliers cluster in two size regimes (small +# SAT-like and large LP-heavy). Picking only by residual order biases to +# small ones (they pass the cap easier). Bands: +# small : elapsed_ms < 10_000 (≤10s, vars≈1700-2000) +# mid : 10_000 ≤ ms < 500_000 (10s-500s, vars≈17000) +# large : 500_000 ≤ ms ≤ STAGE3_MAX (500s-1500s, vars≈20000) +# Within each band pick top-N by residual. +STAGE3_SMALL_MS = 10_000 +STAGE3_MID_MS = 500_000 +STAGE3_PICK_SMALL = 2 +STAGE3_PICK_MID = 2 +STAGE3_PICK_LARGE = 1 +OUTLIER_IQR_K = 3.0 + +STAGE1_STRATEGY = "center" +STAGE2_STRATEGY = "center" STAGE3_STRATEGY = "outliers" # was "center" — now picks outliers_top.csv STAGE4_STRATEGY = "spread" @@ -149,45 +169,96 @@ def _load_outliers_top(csv_path): return rows -def _pick_outliers(rows_by_sha, csv_path, n_pick, max_baseline_ms): - """Pick top-N residual outliers from outliers_top.csv whose baseline_ms is - ≤ max_baseline_ms (so a single tune iteration fits time budget). +def _pick_outliers(rows_by_sha, csv_path): + """Stratified outlier pick: bucket csv rows by elapsed_ms into + small/mid/large, then within each bucket pick top-N by residual. + + Returns (picks, csv_rows). picks is in [small..., mid..., large...] order. + csv_rows is the full csv (for outliers.json metadata). - Falls back to slowest-cluster center pick (legacy stage3 logic) if csv is - missing or yields < n_pick eligible entries.""" + Falls back to (None, []) if csv missing — caller uses legacy slow-cluster + center pick then.""" if csv_path is None: print("warning: outliers_top.csv not found — falling back to " "slow-cluster center pick for stage3", file=sys.stderr) return None, [] csv_rows = _load_outliers_top(csv_path) - picks = [] - used = set() - diag = [] + + def _band(ms): + if ms <= 0 or ms > STAGE3_MAX_BASELINE_MS: + return None + if ms < STAGE3_SMALL_MS: + return "small" + if ms < STAGE3_MID_MS: + return "mid" + return "large" + + by_band = {"small": [], "mid": [], "large": []} for c in csv_rows: - if len(picks) >= n_pick: - break - if c["elapsed_ms"] > max_baseline_ms: - diag.append((c["sha"][:12], c["elapsed_ms"], - "skip: > MAX_BASELINE_MS")) + if c["sha"] not in rows_by_sha: continue - d = rows_by_sha.get(c["sha"]) - if d is None: - diag.append((c["sha"][:12], c["elapsed_ms"], - "skip: not in problems.jsonl")) + band = _band(c["elapsed_ms"]) + if band is None: continue - if c["sha"] in used: - continue - picks.append(d) - used.add(c["sha"]) - diag.append((c["sha"][:12], c["elapsed_ms"], - f"pick (residual={c['residual']:.3f}, " - f"n_cons={c['n_cons']})")) + by_band[band].append(c) + # csv_rows already sorted by descending residual → by_band lists inherit. + + band_targets = { + "small": STAGE3_PICK_SMALL, + "mid": STAGE3_PICK_MID, + "large": STAGE3_PICK_LARGE, + } + picks = [] + used = set() + diag = [] + for band in ("small", "mid", "large"): + target = band_targets[band] + taken = 0 + for c in by_band[band]: + if taken >= target: + break + if c["sha"] in used: + continue + d = rows_by_sha[c["sha"]] + picks.append(d) + used.add(c["sha"]) + taken += 1 + diag.append((band, c["sha"][:12], c["elapsed_ms"], + f"pick (residual={c['residual']:.3f}, " + f"n_cons={c['n_cons']})")) + # Backfill: if a band can't fill its quota, try next band's pool. + for c in by_band[band][taken:]: + diag.append((band, c["sha"][:12], c["elapsed_ms"], + f"available (residual={c['residual']:.3f})")) + + # If any band underfilled, top off from remaining bands by residual. + n_target_total = sum(band_targets.values()) + if len(picks) < n_target_total: + leftover = [c for band in ("large", "mid", "small") + for c in by_band[band] if c["sha"] not in used] + leftover.sort(key=lambda c: -c["residual"]) + for c in leftover: + if len(picks) >= n_target_total: + break + d = rows_by_sha[c["sha"]] + picks.append(d) + used.add(c["sha"]) + diag.append(("backfill", c["sha"][:12], c["elapsed_ms"], + f"backfill (residual={c['residual']:.3f})")) + + n_small = sum(1 for b, *_ in diag if b == "small" and "pick" in _[-1]) + n_mid = sum(1 for b, *_ in diag if b == "mid" and "pick" in _[-1]) + n_large = sum(1 for b, *_ in diag if b == "large" and "pick" in _[-1]) print(f"outliers stage3: from {csv_path.relative_to(_BENCH.parent)}, " - f"picked {len(picks)}/{n_pick} under MAX_BASELINE_MS={max_baseline_ms}ms") - for sha12, ms, note in diag: - print(f" {sha12} {int(ms):>10}ms {note}") + f"stratified pick (cap={STAGE3_MAX_BASELINE_MS}ms) " + f"→ {len(picks)} total " + f"[small={n_small}/{STAGE3_PICK_SMALL} " + f"mid={n_mid}/{STAGE3_PICK_MID} " + f"large={n_large}/{STAGE3_PICK_LARGE}]") + for band, sha12, ms, note in diag: + print(f" [{band:<8}] {sha12} {int(ms):>10}ms {note}") return picks, csv_rows @@ -367,19 +438,24 @@ def _bucket_range(b): s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) - # Stage3: outliers from Statistics/outliers_top.csv (top residual, capped - # at MAX_BASELINE_MS). Fallback to slow-cluster center pick if csv missing - # or yields nothing usable. + # Stage3: stratified outlier pick from Statistics/outliers_top.csv + # (small/mid/large by elapsed_ms, top-residual within each band). cap = + # STAGE3_MAX_BASELINE_MS (higher than stage1/2/4's MAX_BASELINE_MS so + # genuinely slow outliers enter the tune set). Fallback to slow-cluster + # center pick if csv missing. rows_by_sha = {_id_key(d): d for d in rows} outliers_csv = _find_outliers_csv() - s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv, - STAGE3_N, MAX_BASELINE_MS) \ + s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv) \ if outliers_csv else (None, []) if s3_outliers: s3 = s3_outliers - stage3_criteria = (f"top-{STAGE3_N} residual outliers from " - f"{outliers_csv.name}, capped @ " - f"{MAX_BASELINE_MS}ms baseline") + stage3_criteria = (f"stratified outliers from {outliers_csv.name} " + f"(small≤{STAGE3_SMALL_MS}ms / " + f"mid<{STAGE3_MID_MS}ms / " + f"large≤{STAGE3_MAX_BASELINE_MS}ms; " + f"top-residual within band; " + f"target {STAGE3_PICK_SMALL}+{STAGE3_PICK_MID}+" + f"{STAGE3_PICK_LARGE})") else: s3 = _center_pick(pool_c5, STAGE3_N) stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index 05864093df..c5bfd7651a 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -137,9 +137,15 @@ prompt: candidate before stage3 ever runs). Stage layout in cascade: - stage1 (5) fast cluster centers — must not regress - stage2 (5) mid cluster centers — must not regress - stage3 (5) OUTLIER problems only — STAGE3_OVERRIDES active here + 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 diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py index 68d0542458..bb486c1826 100644 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -15,6 +15,13 @@ Auto-discovery: walks sibling phase*_*/initial_program.py and unions every PHASE_LOCKED["num_search_workers"]. Override with --workers 1,8 if needed. +STAGE3 OUTLIER POLICY (2026-05 revision): + Stage3 holds outlier problems with W=8-equivalent baselines from raw-data, + some > 1500s. Measuring those at W=1 would either timeout or take ~10× + longer with no value (evaluator skips stage3 for W=1 phases anyway — see + evaluate_stage3). To save rebaseline wall-clock, this script EXCLUDES stage3 + shas from the W=1 task list and measures stage3 only at W>=2. + Output schema (shared/local_baseline.json): { "": { @@ -83,19 +90,23 @@ def _load_problem_index(): return idx -def _load_target_shas(): +def _load_target_shas(include_stage3=True): + """Union of stage sample SHAs (dedup, ordered by first appearance). + include_stage3=False → drop stage3 sample (used for W=1 task list).""" if not _STAGE1_SAMPLE.exists(): print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", file=sys.stderr) sys.exit(2) - ids = [] - seen = set() - for sample_path, label in ( + samples = [ (_STAGE1_SAMPLE, "stage1"), (_STAGE2_SAMPLE, "stage2"), - (_STAGE3_SAMPLE, "stage3"), (_STAGE4_SAMPLE, "stage4"), - ): + ] + if include_stage3: + samples.insert(2, (_STAGE3_SAMPLE, "stage3")) + ids = [] + seen = set() + for sample_path, label in samples: if not sample_path.exists(): print(f"WARN: {sample_path.name} missing — skipping {label}", file=sys.stderr) continue @@ -213,34 +224,46 @@ def main(): worker_counts = _discover_phase_workers() print(f"[rebaseline] worker counts (auto-discovered): {worker_counts}") - shas = _load_target_shas() + # Build BOTH task lists: full (W>=2) and stage3-excluded (W=1). idx = _load_problem_index() - 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 - path = _RAW_DIR / meta["problem_filename"] - if not path.exists(): - print(f"ERROR: input not found: {path}", file=sys.stderr) - return 2 - tasks.append((i, meta, path)) + def _build_tasks(shas): + out = [] + 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) + sys.exit(2) + path = _RAW_DIR / meta["problem_filename"] + if not path.exists(): + print(f"ERROR: input not found: {path}", file=sys.stderr) + sys.exit(2) + out.append((i, meta, path)) + return out + + shas_full = _load_target_shas(include_stage3=True) + shas_no_stage3 = _load_target_shas(include_stage3=False) + tasks_full = _build_tasks(shas_full) + tasks_no_stage3 = _build_tasks(shas_no_stage3) + n_stage3_only = len(shas_full) - len(shas_no_stage3) cores = core_range() if cores is None: cores = list(range(1, parallel_solvers(default=1) + 1)) - print(f"rebaselining union of stage{{1,2,3,4}}_sample.json: " - f"{len(tasks)} problems × {len(worker_counts)} worker counts") + print(f"rebaselining stage{{1,2,3,4}}_sample.json: " + f"{len(tasks_full)} problems total, {n_stage3_only} stage3-only") + print(f" W=1 skips stage3 ({len(tasks_no_stage3)} problems)") + print(f" W>=2 measures all ({len(tasks_full)} problems)") + print(f" worker counts: {worker_counts}") print(f" per-problem timeout = {REBASELINE_TIMEOUT_S}s (never cut short), " f"core pool = {cores}") print() # results[sha]["by_workers"][str(w)] = {...} results = {} - for meta in (m for _, m, _ in tasks): + for meta in (m for _, m, _ in tasks_full): results[meta["sha"]] = { "raw_result": meta["raw_result"], "raw_elapsed_ms": meta["raw_ms"], @@ -250,6 +273,9 @@ def main(): t_start = time.monotonic() mismatch_total = 0 for w in worker_counts: + tasks = tasks_no_stage3 if w == 1 else tasks_full + print(f"[W={w}] {len(tasks)} problems " + f"({'stage3 skipped' if w == 1 else 'all stages'})", flush=True) completed = _measure_at_workers(tasks, w, cores) for i, meta, res, block in completed: got_result = res.get("result", "Unknown") @@ -288,10 +314,11 @@ def main(): elapsed = time.monotonic() - t_start _OUT.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.relative_to(_BENCH_DIR.parent)} " - f"({len(results)} entries × {len(worker_counts)} W = " - f"{len(results) * len(worker_counts)} runs, {mismatch_total} mismatches)") + f"({len(results)} entries, {n_runs} (sha,W) runs, " + f"{mismatch_total} mismatches)") print(f"total time: {elapsed:.1f}s") if mismatch_total: print(f"WARNING: {mismatch_total} (problem, W) pairs had result mismatch — " diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 86fa6d6908..4e173a5de1 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -489,7 +489,23 @@ def evaluate_stage2(program_path): def evaluate_stage3(program_path): # openevolve cascade hardcodes 3 stages, so user-stage4 (broad runtime # sample) is chained inside stage3 via the runtime cascade_threshold gate. + # + # Phase1/2 (W=1) policy: SKIP stage3 entirely. Stage3 holds outlier + # problems whose baseline is W=8-equivalent (raw-data was recorded with + # workers=8); running them at W=1 takes ≥10× longer with no signal — + # phase1/2 tune knobs that are irrelevant at W=1 anyway (subsolver mix, + # cuts). For W=1 phases, skip stage3 and go straight to stage4 as the + # final cascade step. w = _peek_workers(program_path) + if w == 1: + problems4 = _filter_stage4(_load_problems(w)) + r4 = _evaluate(program_path, problems4, "stage4") + if isinstance(r4, EvaluationResult): + r4.artifacts["stage3_policy"] = ( + "skipped: phase has num_search_workers=1 — outlier stage3 " + "is W=8-only (see evaluator.evaluate_stage3 docstring)") + return r4 + problems3 = _filter_stage3(_load_problems(w)) r3 = _evaluate(program_path, problems3, "stage3") if not isinstance(r3, EvaluationResult): diff --git a/input/cpsat-bench/evolve/shared/outliers.json b/input/cpsat-bench/evolve/shared/outliers.json index 18a77fc3cb..045c251cd3 100644 --- a/input/cpsat-bench/evolve/shared/outliers.json +++ b/input/cpsat-bench/evolve/shared/outliers.json @@ -2,9 +2,9 @@ "stage3_sample": [ "5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746", "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", - "d8130c88625b627297a53f53c8e762e3364a3a80df8f3c156308af5f39bfd89d", - "e2b2952cbec18758e829524bf6dbc3429f6a22fa6d02d67a9a769b04e568acda", - "471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce" + "c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c", + "073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151", + "7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de" ], "outliers": { "9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e": { diff --git a/input/cpsat-bench/evolve/shared/stage1_sample.json b/input/cpsat-bench/evolve/shared/stage1_sample.json index 5a4185a1f9..7e5e126141 100644 --- a/input/cpsat-bench/evolve/shared/stage1_sample.json +++ b/input/cpsat-bench/evolve/shared/stage1_sample.json @@ -1,14 +1,34 @@ { - "selection": "5 decisive runtime clusters c1+c2 (fast group)", + "selection": "10 decisive runtime clusters c1+c2 (fast group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ + "daa215241452279f6bd5d569a96d188cea9dd668a82c7db3f1463c7b4122bf78", + "ba0b9ea5e8f2328cb61bd31c0d61dafaec59744031371bbf7d594553e5b1c8c0", + "aec821f70ed62b536c07791cb8cb7cc48661194d53fe27e8b65b139c13aec39a", "90e1185ded530d8fbb2b773076a2fdbdfbb9a725f2fc07b4d8b8280e93069b54", "2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137", "fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac", "27a639ae4f3c7b232b758f7a4b7a8193bbdf2375bc6ad9f0ed832df3259ed18c", - "eef410430ef1cdcadce5480be841daef9bd294a8d1a7a1912d559e62eee37fb3" + "eef410430ef1cdcadce5480be841daef9bd294a8d1a7a1912d559e62eee37fb3", + "3e69d6f5b7856ea1d07e24a00ad14594b6ba146d0685bdb57e54a8483065b9d0", + "395b9abdb72014e256f558ea722b7df71fedf88a40e4bf0f70b34ed75d3e7ce1" ], "summary": [ + { + "sha": "daa215241452", + "baseline_result": "OPTIMAL", + "baseline_ms": 1287.87 + }, + { + "sha": "ba0b9ea5e8f2", + "baseline_result": "OPTIMAL", + "baseline_ms": 1315.97 + }, + { + "sha": "aec821f70ed6", + "baseline_result": "OPTIMAL", + "baseline_ms": 1317.35 + }, { "sha": "90e1185ded53", "baseline_result": "OPTIMAL", @@ -33,6 +53,16 @@ "sha": "eef410430ef1", "baseline_result": "OPTIMAL", "baseline_ms": 1380.36 + }, + { + "sha": "3e69d6f5b785", + "baseline_result": "OPTIMAL", + "baseline_ms": 1384.5 + }, + { + "sha": "395b9abdb720", + "baseline_result": "OPTIMAL", + "baseline_ms": 1394.5 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage2_sample.json b/input/cpsat-bench/evolve/shared/stage2_sample.json index 8630eff9fd..d3b23029ff 100644 --- a/input/cpsat-bench/evolve/shared/stage2_sample.json +++ b/input/cpsat-bench/evolve/shared/stage2_sample.json @@ -1,14 +1,34 @@ { - "selection": "5 decisive runtime clusters c3+c4 (mid group)", + "selection": "10 decisive runtime clusters c3+c4 (mid group)", "source": "cpsat-bench/problems.jsonl", "sha256": [ + "9801d683d790b79a89846abab1f9539026a1ca565629939ff874f65922709702", + "ec4b042c8f9a198d1b01cca74aa2bcd5acc11f565478ec964f0ba812e5f5f5c5", + "f78fe695ba9e97a63ab5ce95b254e44fa166fac58e2775b3ddf94f49ea7903cf", "aa6fca326d5509b8593566dd692566d7aae1391f75b3ae9b3f5f2c09409f27c5", "7228b93c395db27bb2e8fc8976b27e1025c87d6f7c6463ca60078bae327b213e", "8b1db26428e486f04d080ee1a5589c05a5a1251c112b8d2af9dfb6d55ae0dcf4", "49e2fc3d508cdb66bff21c054a927b03b334d0a849540dffb80c05d3fb77e18b", - "bd4be90c6f160c8932088ffb1f966190355a060720c71113a8784e00d8c2cd57" + "bd4be90c6f160c8932088ffb1f966190355a060720c71113a8784e00d8c2cd57", + "2cf09323b6357707934b8bab13217c744b0633a6bf11f0d7b51b0961f33f9264", + "c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5" ], "summary": [ + { + "sha": "9801d683d790", + "baseline_result": "OPTIMAL", + "baseline_ms": 4600.21 + }, + { + "sha": "ec4b042c8f9a", + "baseline_result": "OPTIMAL", + "baseline_ms": 4618.5 + }, + { + "sha": "f78fe695ba9e", + "baseline_result": "OPTIMAL", + "baseline_ms": 4646.35 + }, { "sha": "aa6fca326d55", "baseline_result": "OPTIMAL", @@ -33,6 +53,16 @@ "sha": "bd4be90c6f16", "baseline_result": "OPTIMAL", "baseline_ms": 4816.56 + }, + { + "sha": "2cf09323b635", + "baseline_result": "OPTIMAL", + "baseline_ms": 4828 + }, + { + "sha": "c4fe2cc04ae7", + "baseline_result": "OPTIMAL", + "baseline_ms": 4839.59 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage3_sample.json b/input/cpsat-bench/evolve/shared/stage3_sample.json index 899e3a731f..0a59eb19ae 100644 --- a/input/cpsat-bench/evolve/shared/stage3_sample.json +++ b/input/cpsat-bench/evolve/shared/stage3_sample.json @@ -1,12 +1,12 @@ { - "selection": "5 top-5 residual outliers from outliers_top.csv, capped @ 120000ms baseline", + "selection": "5 stratified outliers from outliers_top.csv (small\u226410000ms / mid<500000ms / large\u22641500000ms; top-residual within band; target 2+2+1)", "source": "cpsat-bench/problems.jsonl", "sha256": [ "5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746", "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", - "d8130c88625b627297a53f53c8e762e3364a3a80df8f3c156308af5f39bfd89d", - "e2b2952cbec18758e829524bf6dbc3429f6a22fa6d02d67a9a769b04e568acda", - "471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce" + "c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c", + "073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151", + "7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de" ], "summary": [ { @@ -20,19 +20,19 @@ "baseline_ms": 3530.81 }, { - "sha": "d8130c88625b", + "sha": "c0bbae62f14d", "baseline_result": "OPTIMAL", - "baseline_ms": 3748.33 + "baseline_ms": 400652 }, { - "sha": "e2b2952cbec1", + "sha": "073ef8ba3455", "baseline_result": "OPTIMAL", - "baseline_ms": 2894.64 + "baseline_ms": 1496560.0 }, { - "sha": "471adab20a30", + "sha": "7e5aa4016ad6", "baseline_result": "OPTIMAL", - "baseline_ms": 3311.66 + "baseline_ms": 1482010.0 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage4_sample.json b/input/cpsat-bench/evolve/shared/stage4_sample.json index 9a7c1ecada..74c934e73c 100644 --- a/input/cpsat-bench/evolve/shared/stage4_sample.json +++ b/input/cpsat-bench/evolve/shared/stage4_sample.json @@ -3,24 +3,24 @@ "source": "cpsat-bench/problems.jsonl", "sha256": [ "5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5", - "3e2b36379a2919afebb3b79a5e34bd8fd946fbc28059edd6a832ad467f412fc3", - "d28acbdfd6c0f8dc93e215ce1a2fc55b369d63cd6b94efdbfaad6045b6f882ea", - "1a1c06a66af7443f6136b4ba851fd267ab7fff909953005391a5e81fc0a57af0", - "a4dbf4239260ca8770d6c3f876a98ba92dd42102400396cf211e7f9696e4776c", - "6c64b95d90a6caf1573da836e471273a2845a145a3ce07ccc4124cf7654bc226", - "87106998551ed8b22ec13067c8b3a05d47ccb12637e779a1040a9def4e9765b6", - "9c801bcf5dc6a4fa11be76710e268871e68ba55da83399f00fb9e0390f675962", + "1da421e660098ad22f7cf645e4955a7d621856372d3d89ace5722c48699b9b5d", + "568cb19ecec5f12612462493e54e7af582f6b43606359e934281a90751fda3c7", + "915f8918d0b3d7d39a423af66871046decda0ae1e8230bbdeb43f12a08e79fc7", + "c867ab59144791c88799ce95599bbd25090dec398595de1c1e35d01b8a7f06ec", + "2abe830de840839cc8700da827e94f67e8231e6dbc5fdba2042dc0a5cfc022ff", + "37f84b16de74e01a63c4bdcf999c82d18ebf6d0e03406bd9ee65adb21ae54b71", "ca043f0850c14f2d99c0a48b1a959387505b2facf3551fa053ddb9c68bfa58b5", - "9f330e3d7528eea46bbfbd640e4dc0f7d732bf1fdc1dd9e39c323406b9cede01", - "4dc032eadf8f2c84d493c917a5356a18eb9633a34763a134563923b85548d7ba", - "c640cc60784b5f00e395f07384f65c540c7f59d3766fc098f1c34f8645b0e4ae", - "55194630a32dfef1e51e9adc4cd97e19a58f98d99e9d0668428a70b57f0539e5", - "d1b2c9992efda91256f54b5da850272632e2cd7a46cab2a4e81f74cedf156620", - "f38c4b16a01010ad99c30db049af26efc7fa7366d244e2d8b09c59fb44bc51f6", - "e5c9e667fbee60ca8a589904e1a7afc30c81394efd58e388a9f63470fa4ea406", - "8a3a0575a6af4727c9e27c2e63a5da1f147282a3478b1278fd9bc8069e8110b1", - "9b68c85c1a117037be6720b3f7a9f0c2b040b41c9b725525fb162df7f3a2910c", - "e272c0e4899bd231855a0205e36e42663a9208b44d081e1149dc37bdafe3a33d", + "674b484eb726690bc9d4878cb217bc6b22f30eac4f8d512be6e2713a303545dd", + "57b759f315f04cd49b38987b95a953459c7c29e4f53de8dd7a16f65c5057c92b", + "5978abbd9b38469692be7af1f78c5fd15d0736c6192c38ba083c6ca4778659a5", + "4b5c183b293875eb4e0b73aabd4581cbd17ba882b436d0dd49299632ccbbc661", + "57b21c3f6c8f3ceb646f5ce6950949114d86bc1a225d6db5437e6f654821a6c6", + "1cbb4fb20584a2df3ff85a980e992daccf8381fb6da66657c08a35a9f6f61fe3", + "6c7a1a5ce29490757a4fd01319c3af2f090f1488a9bd158edeb09a45ef95d8e0", + "a0cd1b349e85c374dac7fd4570d5dc90928422dea74e38afe67defa14d8a13a5", + "643f0a85b4d3d3c13ef9cfbbbc20b1dc8f9d837a181ad2ebb84c6823e91829b5", + "e70a9ef2b4f8aaa2b009cfae1b6ce00546089bbb5aa32842f75da342b97b5c62", + "0a65b64b80dcebb76c352dc449f577b4714d5ecc4a7ab1636d9e5af952bea067", "5b8c61f99d31fed9077ef475613e5be6648f55f2a47bcc92c0e5d0e3b41ecb94" ], "summary": [ @@ -30,94 +30,94 @@ "baseline_ms": 105.102 }, { - "sha": "3e2b36379a29", + "sha": "1da421e66009", "baseline_result": "OPTIMAL", - "baseline_ms": 626.761 + "baseline_ms": 623.536 }, { - "sha": "d28acbdfd6c0", + "sha": "568cb19ecec5", "baseline_result": "OPTIMAL", - "baseline_ms": 974.173 + "baseline_ms": 969.978 }, { - "sha": "1a1c06a66af7", + "sha": "915f8918d0b3", "baseline_result": "OPTIMAL", - "baseline_ms": 1500.26 + "baseline_ms": 1537.86 }, { - "sha": "a4dbf4239260", + "sha": "c867ab591447", "baseline_result": "OPTIMAL", - "baseline_ms": 1500.8 + "baseline_ms": 1564.42 }, { - "sha": "6c64b95d90a6", + "sha": "2abe830de840", "baseline_result": "OPTIMAL", - "baseline_ms": 1883.75 + "baseline_ms": 1961.23 }, { - "sha": "87106998551e", + "sha": "37f84b16de74", "baseline_result": "OPTIMAL", - "baseline_ms": 2555.9 + "baseline_ms": 2603.74 }, { - "sha": "9c801bcf5dc6", + "sha": "ca043f0850c1", "baseline_result": "OPTIMAL", - "baseline_ms": 3098.94 + "baseline_ms": 3101.11 }, { - "sha": "ca043f0850c1", + "sha": "674b484eb726", "baseline_result": "OPTIMAL", - "baseline_ms": 3101.11 + "baseline_ms": 3104.21 }, { - "sha": "9f330e3d7528", + "sha": "57b759f315f0", "baseline_result": "OPTIMAL", - "baseline_ms": 3351.44 + "baseline_ms": 3337.57 }, { - "sha": "4dc032eadf8f", + "sha": "5978abbd9b38", "baseline_result": "OPTIMAL", - "baseline_ms": 3844.03 + "baseline_ms": 3841.35 }, { - "sha": "c640cc60784b", + "sha": "4b5c183b2938", "baseline_result": "OPTIMAL", - "baseline_ms": 4313.07 + "baseline_ms": 4210.68 }, { - "sha": "55194630a32d", + "sha": "57b21c3f6c8f", "baseline_result": "OPTIMAL", - "baseline_ms": 4354.72 + "baseline_ms": 4231.64 }, { - "sha": "d1b2c9992efd", + "sha": "1cbb4fb20584", "baseline_result": "OPTIMAL", - "baseline_ms": 5355.17 + "baseline_ms": 5391.62 }, { - "sha": "f38c4b16a010", + "sha": "6c7a1a5ce294", "baseline_result": "OPTIMAL", - "baseline_ms": 6043.44 + "baseline_ms": 6126.11 }, { - "sha": "e5c9e667fbee", + "sha": "a0cd1b349e85", "baseline_result": "OPTIMAL", - "baseline_ms": 6608.39 + "baseline_ms": 6657.63 }, { - "sha": "8a3a0575a6af", + "sha": "643f0a85b4d3", "baseline_result": "OPTIMAL", - "baseline_ms": 6624.34 + "baseline_ms": 6785.44 }, { - "sha": "9b68c85c1a11", + "sha": "e70a9ef2b4f8", "baseline_result": "OPTIMAL", - "baseline_ms": 7909.24 + "baseline_ms": 7970.52 }, { - "sha": "e272c0e4899b", + "sha": "0a65b64b80dc", "baseline_result": "OPTIMAL", - "baseline_ms": 11336.5 + "baseline_ms": 11540.4 }, { "sha": "5b8c61f99d31", From 739fcc844eb641afbc24b25d8f73d6d2a69c899a Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 26 May 2026 17:12:45 +0900 Subject: [PATCH 27/42] tune score logic --- input/cpsat-bench/evolve/README.md | 16 +++- input/cpsat-bench/evolve/benchmark_final.py | 71 +++++++++++++---- input/cpsat-bench/evolve/config.yaml | 14 +++- input/cpsat-bench/evolve/final_verify.py | 22 +++++- .../evolve/shared/_cpsat_solve_worker.py | 12 +++ input/cpsat-bench/evolve/shared/evaluator.py | 8 +- input/cpsat-bench/evolve/shared/score.py | 79 ++++++++++++++++--- 7 files changed, 184 insertions(+), 38 deletions(-) diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index a073368229..478c5a5e4a 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -149,16 +149,28 @@ The runner already pins each worker subprocess with `taskset -c `. ## Score formula (cost mode) ``` -combined_score = geomean( (b_obj/v_obj)^COST_W * (b_ms/v_ms) ) +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 (fallback when dtime missing) ``` +- **Deterministic_time** (CP-SAT's hardware-independent work measure) is the + primary time metric. Wall-clock varies with CPU load / NUMA / thermal + state — using dtime removes that noise from the score so genuine knob + improvements are visible. `geomean_speedup` in the metrics dict is the + dtime ratio; `geomean_wall_speedup` is reported alongside as a + diagnostic. Override with `OPENEVOLVE_TIME_METRIC=wall` if needed. +- Fallback to wall when either side lacks `deterministic_time` in `stats` + (e.g. older local_baseline.json from before this change — rerun + `rebaseline_local.py` to refresh). - All 85 baselines are OPTIMAL, so if the variant also reaches OPTIMAL, the cost ratio collapses to 1.0 and score reduces to geomean(time speedup). - Variants that bail to FEASIBLE-but-worse-objective are penalized via the cost ratio; variants that bail to UNKNOWN/INFEASIBLE contribute 1e-6 to - the geomean (~60× penalty across stage1's 5 problems). + the geomean (~60× penalty across stage1's 10 problems). - Efficiency factor multiplies based on `num_conflicts` (weight 2.0) and `num_branches` (weight 1.5) ratios vs baseline. diff --git a/input/cpsat-bench/evolve/benchmark_final.py b/input/cpsat-bench/evolve/benchmark_final.py index c1e1ee7538..dfadb990f5 100644 --- a/input/cpsat-bench/evolve/benchmark_final.py +++ b/input/cpsat-bench/evolve/benchmark_final.py @@ -136,11 +136,14 @@ def _resolve_sample(idx): def _aggregate(runs): - """runs = list of dicts {elapsed_ms, result, objective?, timeout, ...}. + """runs = list of dicts {elapsed_ms, result, objective?, timeout, stats?}. Returns aggregate stats over only the decisive runs (others as 'failed'). - """ + Tracks both wall (elapsed_ms) and deterministic_time aggregates.""" decisive = [r for r in runs if r["result"] in _DECISIVE] ms_list = [r["elapsed_ms"] for r in decisive] + dt_list = [(r.get("stats") or {}).get("deterministic_time") + for r in decisive] + dt_list = [d for d in dt_list if d is not None and d > 0] obj_list = [r["objective"] for r in decisive if r.get("objective") is not None] agg = { @@ -156,6 +159,13 @@ def _aggregate(runs): agg["stddev_ms"] = statistics.stdev(ms_list) if len(ms_list) > 1 else 0.0 else: agg.update({"mean_ms": 0, "median_ms": 0, "min_ms": 0, "max_ms": 0, "stddev_ms": 0.0}) + if dt_list: + agg["mean_dt"] = statistics.mean(dt_list) + agg["median_dt"] = statistics.median(dt_list) + agg["stddev_dt"] = statistics.stdev(dt_list) if len(dt_list) > 1 else 0.0 + agg["n_dt"] = len(dt_list) + else: + agg.update({"mean_dt": 0, "median_dt": 0, "stddev_dt": 0.0, "n_dt": 0}) if obj_list: agg["mean_obj"] = statistics.mean(obj_list) agg["min_obj"] = min(obj_list) @@ -175,6 +185,7 @@ def _run_repeat(problem_path, params, n, timeout_s, core, tag): "timeout": bool(r.get("timeout")), "invalid_param": r.get("invalid_param"), "error": r.get("error"), + "stats": r.get("stats") or {}, }) return runs @@ -260,11 +271,13 @@ def _worker(idx_meta): i, meta, b_runs, v_runs, core, dt = completed[-1] b_agg = _aggregate(b_runs) v_agg = _aggregate(v_runs) - sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + sp_w = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + sp_d = (b_agg["mean_dt"] / max(v_agg["mean_dt"], 1)) if v_agg["mean_dt"] else 0 + sp_main = sp_d if sp_d else sp_w print(f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} core={core} " f"base_mean={b_agg['mean_ms']:>7.0f}ms (n_ok={b_agg['n_decisive']}/{n_iters}) " f"variant_mean={v_agg['mean_ms']:>7.0f}ms (n_ok={v_agg['n_decisive']}/{n_iters}) " - f"speedup={sp:.2f}x (took {dt:.1f}s)", + f"speedup={sp_main:.2f}x[{'dt' if sp_d else 'wall'}] wall={sp_w:.2f}x (took {dt:.1f}s)", flush=True) else: with ThreadPoolExecutor(max_workers=n_parallel) as ex: @@ -312,8 +325,10 @@ def _worker(idx_meta): for _, meta, b_runs, v_runs, _, _ in completed: b_agg = _aggregate(b_runs) v_agg = _aggregate(v_runs) - mean_sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 - med_sp = (b_agg["median_ms"] / max(v_agg["median_ms"], 1)) if v_agg["median_ms"] else 0 + mean_sp_w = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 + med_sp_w = (b_agg["median_ms"] / max(v_agg["median_ms"], 1)) if v_agg["median_ms"] else 0 + mean_sp_d = (b_agg["mean_dt"] / max(v_agg["mean_dt"], 1)) if v_agg["mean_dt"] else 0 + med_sp_d = (b_agg["median_dt"] / max(v_agg["median_dt"], 1)) if v_agg["median_dt"] else 0 summary_rows.append({ "sha": meta["sha"], "n_iters": n_iters, @@ -323,14 +338,22 @@ def _worker(idx_meta): "baseline_min_ms": round(b_agg["min_ms"], 1), "baseline_max_ms": round(b_agg["max_ms"], 1), "baseline_stddev_ms": round(b_agg["stddev_ms"], 1), + "baseline_mean_dt": round(b_agg["mean_dt"], 3), + "baseline_median_dt": round(b_agg["median_dt"], 3), + "baseline_n_dt": b_agg["n_dt"], "variant_n_decisive": v_agg["n_decisive"], "variant_mean_ms": round(v_agg["mean_ms"], 1), "variant_median_ms": round(v_agg["median_ms"], 1), "variant_min_ms": round(v_agg["min_ms"], 1), "variant_max_ms": round(v_agg["max_ms"], 1), "variant_stddev_ms": round(v_agg["stddev_ms"], 1), - "mean_speedup": round(mean_sp, 3), - "median_speedup": round(med_sp, 3), + "variant_mean_dt": round(v_agg["mean_dt"], 3), + "variant_median_dt": round(v_agg["median_dt"], 3), + "variant_n_dt": v_agg["n_dt"], + "mean_speedup": round(mean_sp_w, 3), # wall (legacy) + "median_speedup": round(med_sp_w, 3), # wall (legacy) + "mean_dt_speedup": round(mean_sp_d, 3), # deterministic (primary) + "median_dt_speedup": round(med_sp_d, 3), }) with open(summary_csv, "w", newline="") as f: @@ -339,17 +362,28 @@ def _worker(idx_meta): w.writerows(summary_rows) # ----- overall aggregate ----- - # speedup geomean over problems with both means valid + # speedup geomean over problems with both means valid. Compute both wall + # and deterministic-time geomeans; dt is preferred when populated for both + # baseline + variant on every (problem, iter) pair. import math valid = [r for r in summary_rows if r["baseline_mean_ms"] > 0 and r["variant_mean_ms"] > 0 and r["baseline_n_decisive"] > 0 and r["variant_n_decisive"] > 0] + valid_dt = [r for r in valid + if r["mean_dt_speedup"] > 0 and r["median_dt_speedup"] > 0] if valid: geo_mean = math.exp(sum(math.log(r["mean_speedup"]) for r in valid) / len(valid)) geo_med = math.exp(sum(math.log(r["median_speedup"]) for r in valid) / len(valid)) arith_mean = sum(r["mean_speedup"] for r in valid) / len(valid) else: geo_mean = geo_med = arith_mean = 0.0 + if valid_dt: + geo_mean_dt = math.exp(sum(math.log(r["mean_dt_speedup"]) + for r in valid_dt) / len(valid_dt)) + geo_med_dt = math.exp(sum(math.log(r["median_dt_speedup"]) + for r in valid_dt) / len(valid_dt)) + else: + geo_mean_dt = geo_med_dt = 0.0 n_baseline_failed = sum(r["baseline_n_decisive"] < n_iters for r in summary_rows) n_variant_failed = sum(r["variant_n_decisive"] < n_iters for r in summary_rows) @@ -366,10 +400,13 @@ def _worker(idx_meta): "wall_clock_s": round(wall, 1), "n_problems": len(metas), "summary": { - "geomean_speedup_mean": round(geo_mean, 3), - "geomean_speedup_median": round(geo_med, 3), - "arith_mean_speedup": round(arith_mean, 3), + "geomean_dt_speedup_mean": round(geo_mean_dt, 3), # primary + "geomean_dt_speedup_median": round(geo_med_dt, 3), + "geomean_wall_speedup_mean": round(geo_mean, 3), # diagnostic + "geomean_wall_speedup_median": round(geo_med, 3), + "arith_mean_wall_speedup": round(arith_mean, 3), "n_problems_with_valid_speedup": len(valid), + "n_problems_with_valid_dt_speedup": len(valid_dt), "n_baseline_partial_fail": n_baseline_failed, "n_baseline_total_fail": n_baseline_all_failed, "n_variant_partial_fail": n_variant_failed, @@ -380,10 +417,12 @@ def _worker(idx_meta): print() print("== overall (mean-of-means across problems) ==") - print(f" problems with valid speedup : {len(valid)}/{len(metas)}") - print(f" geomean mean-speedup : {geo_mean:.3f}x") - print(f" geomean median-speedup : {geo_med:.3f}x") - print(f" arith mean-speedup : {arith_mean:.3f}x") + print(f" problems with valid speedup : wall={len(valid)}/{len(metas)} dt={len(valid_dt)}/{len(metas)}") + print(f" geomean dt-speedup (mean) : {geo_mean_dt:.3f}x (primary, hardware-independent)") + print(f" geomean dt-speedup (median) : {geo_med_dt:.3f}x") + print(f" geomean wall-speedup (mean) : {geo_mean:.3f}x (diagnostic)") + print(f" geomean wall-speedup (med) : {geo_med:.3f}x") + print(f" arith mean wall-speedup : {arith_mean:.3f}x") print(f" baseline partial-fail cases : {n_baseline_failed} (≥1 of {n_iters} runs non-decisive)") print(f" baseline total-fail cases : {n_baseline_all_failed} (all {n_iters} runs non-decisive)") print(f" variant partial-fail cases : {n_variant_failed}") diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index c5bfd7651a..814ed8d99e 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -71,6 +71,11 @@ prompt: 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 @@ -119,10 +124,11 @@ prompt: new_constraints_batch_size: int mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance: numeric - Per-round artifacts show speedup, 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 — a 2x speedup with same conflict count - is hardware noise; a 2x with 10x fewer conflicts is a real find. + 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: diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py index 055b557b33..1c38b071f2 100644 --- a/input/cpsat-bench/evolve/final_verify.py +++ b/input/cpsat-bench/evolve/final_verify.py @@ -223,8 +223,17 @@ def _measure(idx_meta): var_result = v.get("result", "Unknown") var_obj = v.get("objective") var_invalid = v.get("invalid_param") - - speedup = base_ms_local / max(var_ms, 1) + b_dt = (b.get("stats") or {}).get("deterministic_time") + v_dt = (v.get("stats") or {}).get("deterministic_time") + + wall_speedup = base_ms_local / max(var_ms, 1) + if b_dt and v_dt and b_dt > 0 and v_dt > 0: + dtime_speedup = float(b_dt) / float(v_dt) + speedup = dtime_speedup + speedup_src = "dt" + else: + speedup = wall_speedup + speedup_src = "wall" if var_invalid: flag = f" INVALID_PARAM={var_invalid}" speedup = 0.0 @@ -257,7 +266,8 @@ def _measure(idx_meta): f"{('/obj=' + format(base_obj, '.3g')) if base_obj is not None else ''} " f"variant={var_result:<10}/{var_ms:>7}ms" f"{('/obj=' + format(var_obj, '.3g')) if var_obj is not None else ''} " - f"speedup={speedup:.2f}x cost_ratio={cost_ratio:.3f}{flag}", + f"speedup={speedup:.2f}x[{speedup_src}] " + f"wall={wall_speedup:.2f}x cost_ratio={cost_ratio:.3f}{flag}", flush=True, ) results.append({ @@ -286,7 +296,11 @@ def _measure(idx_meta): print(f" solved : {metrics['solved']}/{metrics['comparable']}") print(f" regressions : {metrics['regressions']} " f"(baseline OK, variant failed)") - print(f" geomean (cost×time) : {metrics['geomean_speedup']:.3f}") + print(f" geomean (cost×dtime): {metrics['geomean_speedup']:.3f} " + f"(dtime_used={metrics.get('dtime_used', 0)}/" + f"{metrics.get('dtime_used', 0) + metrics.get('dtime_fallback', 0)})") + print(f" geomean (cost×wall) : {metrics.get('geomean_wall_speedup', 0.0):.3f} " + f"(diagnostic)") print(f" solved_rate : {metrics['solved_rate']:.3f} (over comparable)") print(f" efficiency : {metrics.get('efficiency', 1.0):.3f}") print(f" combined_score : {metrics['combined_score']:.3f}") diff --git a/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py index 6e05b14780..3c33eacda2 100644 --- a/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py +++ b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py @@ -153,6 +153,18 @@ def main(): 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: diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 4e173a5de1..814018a1cb 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -72,7 +72,8 @@ def _load_outlier_shas(): _PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") -_KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", "wall_time", "user_time") +_KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", + "wall_time", "user_time", "deterministic_time") _DECISIVE = ("OPTIMAL", "FEASIBLE") @@ -444,7 +445,10 @@ def _fmt_core(c): "summary": ( f"solved={metrics['solved']}/{metrics['total']} " f"regressions={metrics['regressions']} " - f"geomean_speedup={metrics['geomean_speedup']:.3f} " + f"geomean_dtime={metrics['geomean_speedup']:.3f} " + f"geomean_wall={metrics.get('geomean_wall_speedup', 0.0):.3f} " + f"dtime_used={metrics.get('dtime_used', 0)}/" + f"{metrics.get('dtime_used', 0) + metrics.get('dtime_fallback', 0)} " f"efficiency={metrics.get('efficiency', 1.0):.3f} " f"score={metrics['combined_score']:.3f}" ), diff --git a/input/cpsat-bench/evolve/shared/score.py b/input/cpsat-bench/evolve/shared/score.py index 714020c930..2041cc0e39 100644 --- a/input/cpsat-bench/evolve/shared/score.py +++ b/input/cpsat-bench/evolve/shared/score.py @@ -4,9 +4,20 @@ Score mode: cost (minimize CP-SAT objective). combined = geomean(cost_ratio^COST_W * time_ratio) * solved_rate^2 * efficiency^STATS_WEIGHT + + time_ratio (preferred): baseline_dtime / variant_dtime + CP-SAT's deterministic_time is hardware-independent, + so per-iteration noise across machines / system load + stays out of the score. ~order-of-magnitude same as + wall_time but stable across runs. + time_ratio (fallback) : baseline_ms / variant_ms (wall) + used when either dtime is missing (legacy local + baselines without dtime in stats, or worker not + emitting it). Diagnostic geomean_wall_speedup is + always reported alongside. + - both baseline+variant decisive (OPTIMAL or FEASIBLE) with objective values: cost_ratio = (baseline_obj + EPS) / (variant_obj + EPS) [minimize] - time_ratio = baseline_ms / variant_ms - status mismatch (e.g. variant UNKNOWN where baseline OPTIMAL): 1e-6 - missing baseline_objective (rebaseline not yet run) falls back to time_ratio only @@ -22,6 +33,7 @@ Env overrides: OPENEVOLVE_STATS_WEIGHT exponent on efficiency, default 0.333, 0 disables OPENEVOLVE_COST_WEIGHT exponent on cost_ratio, default 1.0, 0 disables cost factor + OPENEVOLVE_TIME_METRIC "dtime" (default) | "wall" — force wall ratio """ import math import os @@ -68,6 +80,25 @@ def _efficiency(per_problem): return math.exp(log_sum / len(per_prob_effs)), len(per_prob_effs) +def _time_ratio(p, metric): + """Compute time_ratio. metric ∈ {"dtime", "wall"}. + Returns (ratio, source) where source describes which clock was used. + Falls back to wall when dtime missing on either side.""" + 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" + # fallback / forced wall + return p["baseline_ms"] / max(p["elapsed_ms"], 1), "wall" + + +def _wall_ratio(p): + return p["baseline_ms"] / max(p["elapsed_ms"], 1) + + def _score_cost(per_problem): """ Cost mode scoring rules: @@ -75,19 +106,28 @@ def _score_cost(per_problem): (Baseline never reached OPTIMAL/FEASIBLE within timeout → variant has no target to beat. Counting it as 1e-6 unfairly tanks the geomean.) - baseline decisive + variant decisive → ratio = cost_ratio^W * time_ratio - (or just time_ratio if either objective_value is unknown). + (time_ratio is deterministic_time-based when both sides have it, else wall) - baseline decisive + variant NOT decisive → 1e-6 + regression++ (real loss). - Returns: (geomean, solved_rate, solved, regressions, comparable) - comparable = problems where baseline was decisive (i.e. counted in geomean). - solved_rate is over `comparable` (not the total input list). + Returns: (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) + comparable = problems where baseline was decisive (counted in geomean). + dtime_used = comparable problems where dtime ratio applied. + dtime_fallback= comparable problems forced to wall ratio (dtime missing). """ cost_weight = float(os.environ.get("OPENEVOLVE_COST_WEIGHT", "1.0")) cost_weight = max(0.0, min(cost_weight, 2.0)) + time_metric = os.environ.get("OPENEVOLVE_TIME_METRIC", "dtime").lower() + if time_metric not in ("dtime", "wall"): + time_metric = "dtime" + ratios = [] + wall_ratios = [] solved = 0 regressions = 0 comparable = 0 + dtime_used = 0 + dtime_fallback = 0 for p in per_problem: b_ok = p["baseline_result"] in _DECISIVE if not b_ok: @@ -96,23 +136,35 @@ def _score_cost(per_problem): v_ok = p["result"] in _DECISIVE b_cost = p.get("baseline_objective") v_cost = p.get("objective") - time_r = p["baseline_ms"] / max(p["elapsed_ms"], 1) + time_r, src = _time_ratio(p, time_metric) + wall_r = _wall_ratio(p) + 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(1e-6) + wall_ratios.append(1e-6) regressions += 1 if not ratios: - return 1.0, 0.0, 0, 0, 0 + 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, solved_rate, solved, regressions, comparable + return (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) def score(per_problem): @@ -121,6 +173,7 @@ def score(per_problem): return { "combined_score": 0.0, "geomean_speedup": 0.0, + "geomean_wall_speedup": 0.0, "solved_rate": 0.0, "regressions": 0, "solved": 0, @@ -130,9 +183,12 @@ def score(per_problem): "efficiency": 1.0, "efficiency_pairs": 0, "stats_weight": 0.0, + "dtime_used": 0, + "dtime_fallback": 0, } - geomean, solved_rate, solved, regressions, comparable = _score_cost(per_problem) + (geomean, geomean_wall, solved_rate, solved, regressions, + comparable, dtime_used, dtime_fallback) = _score_cost(per_problem) efficiency, eff_pairs = _efficiency(per_problem) try: @@ -145,7 +201,8 @@ def score(per_problem): return { "combined_score": float(combined), - "geomean_speedup": float(geomean), + "geomean_speedup": float(geomean), # primary (dtime when available) + "geomean_wall_speedup": float(geomean_wall), # diagnostic (wall) "solved_rate": float(solved_rate), "regressions": int(regressions), "solved": int(solved), @@ -155,4 +212,6 @@ def score(per_problem): "efficiency": float(efficiency), "efficiency_pairs": int(eff_pairs), "stats_weight": float(stats_weight), + "dtime_used": int(dtime_used), + "dtime_fallback": int(dtime_fallback), } From 3a4ebddf26590396fc24c21598dc242afa347a00 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 26 May 2026 18:25:35 +0900 Subject: [PATCH 28/42] add optimization target prompts --- input/cpsat-bench/evolve/config.yaml | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index 814ed8d99e..3d61bb5291 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -81,6 +81,23 @@ prompt: 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.) From 8d77730ac7272ce1fd9bc02dab2d3ee5a0056262 Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 27 May 2026 10:37:28 +0900 Subject: [PATCH 29/42] avoid zero sample crash: --- input/cpsat-bench/evolve/rebaseline_local.py | 3 ++ input/cpsat-bench/evolve/shared/evaluator.py | 29 ++++++++++++++++++++ 2 files changed, 32 insertions(+) diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py index bb486c1826..1d0de81c6a 100644 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -163,6 +163,9 @@ def _parse_workers_arg(s): def _measure_at_workers(tasks, w, cores): """Run baseline for one worker count W across all tasks. Returns list of (i, meta, res, core_block) tuples in submission order.""" + if not tasks: + print(f" workers={w}: no tasks — skipping", flush=True) + return [] blocks = alloc_core_blocks(cores, w) if not blocks: blocks = [list(cores)] if cores else [None] diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 814018a1cb..6c00310849 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -261,6 +261,35 @@ def _evaluate(program_path, problems, stage_name): if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] + # Empty stage sample → pass-through. Score is set well above any sane + # cascade_thresholds entry so downstream stages keep running. Use this to + # debug a single stage in isolation by emptying the samples for the others. + if not problems: + pass_through = { + "combined_score": 100.0, + "geomean_speedup": 100.0, + "geomean_wall_speedup": 100.0, + "solved_rate": 1.0, + "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, + "stage": stage_name, + } + return EvaluationResult( + metrics=pass_through, + artifacts={ + "stage": stage_name, + "summary": "empty sample — stage skipped (cascade pass-through, score=100)", + }, + ) + for p in problems: input_path = _RAW_DIR / p["input_file"] if not input_path.exists(): From 6642f789bbbd0382bbf7cacc76859010ac7c3fcb Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 27 May 2026 10:46:09 +0900 Subject: [PATCH 30/42] chage claude max turn --- openevolve/llm/claude_code.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openevolve/llm/claude_code.py b/openevolve/llm/claude_code.py index 7050e68214..7a07912892 100644 --- a/openevolve/llm/claude_code.py +++ b/openevolve/llm/claude_code.py @@ -108,7 +108,7 @@ async def _query_once( ) opts_kwargs: Dict[str, Any] = { - "max_turns": 1, + "max_turns": 4, "allowed_tools": [], "disallowed_tools": [], "permission_mode": "bypassPermissions", From 44aba6115958b07adea1e306808018c5f365d08a Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 27 May 2026 11:09:03 +0900 Subject: [PATCH 31/42] add isolate large profile --- input/cpsat-bench/evolve/README.md | 56 +++++++++++-- input/cpsat-bench/evolve/build_samples.py | 47 +++++++++++ .../evolve/phase1_search/initial_program.py | 7 +- .../evolve/phase2_presolve/initial_program.py | 6 +- input/cpsat-bench/evolve/rebaseline_local.py | 37 +++++++-- input/cpsat-bench/evolve/shared/evaluator.py | 79 ++++++++++++++++++- input/run_phase.sh | 53 ++++++++++++- 7 files changed, 265 insertions(+), 20 deletions(-) diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index 478c5a5e4a..294eab78a9 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -33,7 +33,8 @@ input/cpsat-bench/ │ ├── evaluator.py # cascade stages │ ├── cpsat_runner.py # subprocess solver invoker │ ├── _cpsat_solve_worker.py - │ ├── stage{1..4}_sample.json # built by build_samples.py + │ ├── stage{1..4}_sample.json # small profile (cascade staging) + │ ├── stage1_large_sample.json # large profile (single hardest outlier) │ └── local_baseline.json # built by rebaseline_local.py └── phase{1..4}_/ └── initial_program.py # EVOLVE-BLOCK @@ -104,16 +105,58 @@ python input/cpsat-bench/evolve/shared/baseline_params.py # for cost-mode scoring — first run picks it up automatically too) python input/cpsat-bench/evolve/rebaseline_local.py -# 4) run phases sequentially -./input/cpsat-bench/evolve/run_phase.sh 1 -./input/cpsat-bench/evolve/run_phase.sh 2 -./input/cpsat-bench/evolve/run_phase.sh 3 -./input/cpsat-bench/evolve/run_phase.sh 4 +# 4) run phases sequentially (small profile = historical run) +./input/run_phase.sh cpsat-bench 1 +./input/run_phase.sh cpsat-bench 2 +./input/run_phase.sh cpsat-bench 3 +./input/run_phase.sh cpsat-bench 4 +# or all phases at once: +./input/run_phase.sh cpsat-bench ``` After each non-final phase, `run_phase.sh` calls `extract_best.py` to write `shared/phaseN_best.json`, which the next phase's `initial_program.py` loads. +## Profiles (`small` vs `large`) + +`run_phase.sh --profile ` switches between two independent +tuning tracks: + +| | small (default) | large | +|---|---|---| +| sample files | `stage{1..4}_sample.json` (cascade staging) | `stage1_large_sample.json` (single sample) | +| phase1/2 workers | 1 | 8 | +| phase3/4 workers | 8 | 8 | +| evaluation | cascade stage1 → 2 → 3 → 4 | single eval on all stages (cache-backed) | +| problem set | clustered fast/mid + outliers + broad spread | top-`STAGE1_LARGE_N` residual outlier(s) from `Statistics/outliers_top.csv` | +| output dir | `openevolve_output/` | `openevolve_output_large/` | + +Profile sets `OPENEVOLVE_PROFILE` env var that: + +- `evaluator.py` reads to pick sample file + dispatch every cascade stage + entry point (`evaluate_stage{1..4}`) to a single `_evaluate_large()` for + large profile (cache-backed so 3-stage cascade only runs 1 real eval). +- `rebaseline_local.py` reads to rebaseline only the large sample set. +- `phase{1,2}_*/initial_program.py` reads to override `PHASE_LOCKED` + `num_search_workers=8` (default 1). + +Adjust `STAGE1_LARGE_N` in `build_samples.py` to widen the outlier set. + +```bash +# large profile (all phases W=8, single hardest outlier) +./input/run_phase.sh cpsat-bench --profile large +./input/run_phase.sh cpsat-bench 1 --profile large # single phase +``` + +`local_baseline.json` is shared across profiles (keyed by worker count); +running both profiles reuses each other's measurements where worker count +matches. + +> Note: `extract_best.py` writes a single `phaseN_best.json` per phase +> regardless of profile. Running both profiles back-to-back overwrites the +> previous profile's best file. Rename/move it manually if you want to keep +> both. + ## Resume from checkpoint ```bash @@ -137,6 +180,7 @@ python /app/openevolve-run.py \ | `OPENEVOLVE_STATS_WEIGHT` | 0.333 | exponent on efficiency factor (0 disables) | | `OPENEVOLVE_COST_WEIGHT` | 1.0 | exponent on cost_ratio in cost-mode score (0 disables cost factor) | | `OPENEVOLVE_PYTHON_BIN` | `sys.executable` | python used by solver worker | +| `OPENEVOLVE_PROFILE` | `small` | tuning track: `small` (cascade staging) or `large` (single outlier eval). Set automatically by `run_phase.sh --profile`. Drives sample file selection in `evaluator.py` / `rebaseline_local.py` and worker count override in phase1/2 `initial_program.py`. | | `SKIP_REBASELINE` | 0 | skip per-host baseline remeasurement (reuse existing local_baseline.json) | ## CPU pinning (Linux) diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index bb57786c6e..4030f31d5f 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -42,6 +42,13 @@ _STAGE4 = _HERE / "shared" / "stage4_sample.json" _OUTLIERS_JSON = _HERE / "shared" / "outliers.json" +# Large-profile sample (selected via OPENEVOLVE_PROFILE=large). Single file +# only — large profile bypasses cascade staging entirely; evaluator dispatches +# every stage entry point to a single outlier-set evaluation. File name keeps +# the stage1_large prefix so legacy callers (and evaluator's path resolver) +# continue to find it. +_STAGE1_LARGE = _HERE / "shared" / "stage1_large_sample.json" + # outliers_top.csv lives under cpsat-bench/Statistics/ (or its rename "1/"). _OUTLIERS_CSV_CANDIDATES = [ _BENCH / "Statistics" / "outliers_top.csv", @@ -53,6 +60,10 @@ STAGE3_N = 5 STAGE4_N = 20 N_BUCKETS = 5 +# Large profile: single hardest outlier (top residual from outliers_top.csv). +# Tune higher if want to evaluate against more outliers at the cost of +# proportionally longer per-iteration eval time. +STAGE1_LARGE_N = 1 # Global cap for stage1/2/4 sample pool. Anything slower than 2 min skews # the quintile clustering. MAX_BASELINE_MS = 120_000 @@ -484,6 +495,42 @@ def _bucket_range(b): f"{str(_result_key(d)):<10} " f"{int(_runtime_key(d)):>7}ms") + # ---- large profile sample (OPENEVOLVE_PROFILE=large) ---- + # Single sample file with STAGE1_LARGE_N top-residual outliers (default 1). + # evaluator dispatches every cascade stage entry point to one outlier-set + # evaluation, so no stage2/3/4 split is needed. + if csv_all: + eligible = [c for c in csv_all if c["sha"] in rows_by_sha] + # csv_all already sorted by descending residual in _load_outliers_top. + large_picks = eligible[:STAGE1_LARGE_N] + large_rows = [rows_by_sha[c["sha"]] for c in large_picks] + else: + large_picks = [] + large_rows = [] + _write_sample( + _STAGE1_LARGE, large_rows, "stage1_large", + f"top-{STAGE1_LARGE_N} residual outlier(s) from outliers_top.csv " + f"(W=8 tuning set)", + ) + + # Clean up stale empty stage{2,3,4}_large files from older builds — they + # would be picked up by evaluator's path resolver and produce stale empty + # pass-throughs, masking real outlier scores. + for stale in ( + _HERE / "shared" / "stage2_large_sample.json", + _HERE / "shared" / "stage3_large_sample.json", + _HERE / "shared" / "stage4_large_sample.json", + ): + if stale.exists(): + stale.unlink() + print(f"removed stale {stale.relative_to(_BENCH.parent)}") + + print(f"\nstage1_large ({len(large_rows)} outliers):") + for d in large_rows: + print(f" {_id_key(d)[:12]} " + f"{str(_result_key(d)):<10} " + f"{int(_runtime_key(d)):>7}ms") + if __name__ == "__main__": main() diff --git a/input/cpsat-bench/evolve/phase1_search/initial_program.py b/input/cpsat-bench/evolve/phase1_search/initial_program.py index b529beef5e..d950e72dd0 100644 --- a/input/cpsat-bench/evolve/phase1_search/initial_program.py +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -28,6 +28,7 @@ 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 @@ -37,9 +38,13 @@ from baseline_params import BASELINE # noqa: E402 +# OPENEVOLVE_PROFILE=large → run this phase at W=8 (outlier tuning track). +# Default (small) keeps the historical W=1 search-only sweep. +_LARGE_PROFILE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() + == "large") PHASE_LOCKED = { "random_seed": 0, - "num_search_workers": 1, + "num_search_workers": 8 if _LARGE_PROFILE else 1, } diff --git a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py index db91b4e2ef..904978ccbd 100644 --- a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -28,6 +28,7 @@ Do NOT modify locked keys (random_seed, num_search_workers). """ import json +import os import pathlib import sys @@ -37,9 +38,12 @@ from baseline_params import BASELINE # noqa: E402 +# OPENEVOLVE_PROFILE=large → run this phase at W=8 (outlier tuning track). +_LARGE_PROFILE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() + == "large") PHASE_LOCKED = { "random_seed": 0, - "num_search_workers": 1, + "num_search_workers": 8 if _LARGE_PROFILE else 1, } diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py index 1d0de81c6a..4e3c23501c 100644 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -50,6 +50,7 @@ import argparse import importlib.util import json +import os import pathlib import queue as _queue import sys @@ -66,12 +67,27 @@ _BENCH_DIR = _HERE.parent _RAW_DIR = _BENCH_DIR / "raw-data" _PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" -_STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" -_STAGE3_SAMPLE = _HERE / "shared" / "stage3_sample.json" -_STAGE4_SAMPLE = _HERE / "shared" / "stage4_sample.json" _OUT = _HERE / "shared" / "local_baseline.json" + +def _profile(): + return (os.environ.get("OPENEVOLVE_PROFILE") or "small").strip().lower() + + +def _stage_sample(stage_num): + profile = _profile() + if profile != "small": + suffixed = _HERE / "shared" / f"stage{stage_num}_{profile}_sample.json" + if suffixed.exists(): + return suffixed + return _HERE / "shared" / f"stage{stage_num}_sample.json" + + +_STAGE1_SAMPLE = _stage_sample(1) +_STAGE2_SAMPLE = _stage_sample(2) +_STAGE3_SAMPLE = _stage_sample(3) +_STAGE4_SAMPLE = _stage_sample(4) + REBASELINE_TIMEOUT_S = 3600 @@ -92,7 +108,18 @@ def _load_problem_index(): def _load_target_shas(include_stage3=True): """Union of stage sample SHAs (dedup, ordered by first appearance). - include_stage3=False → drop stage3 sample (used for W=1 task list).""" + include_stage3=False → drop stage3 sample (used for W=1 task list). + + Large profile short-circuits to a single sample (stage1_large only) — + cascade staging is disabled in evaluator for that profile.""" + if _profile() == "large": + large = _HERE / "shared" / "stage1_large_sample.json" + if not large.exists(): + print(f"ERROR: {large} missing — run build_samples.py first", + file=sys.stderr) + sys.exit(2) + return list(dict.fromkeys(json.loads(large.read_text())["sha256"])) + if not _STAGE1_SAMPLE.exists(): print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", file=sys.stderr) diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 6c00310849..99fd5c815c 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -51,14 +51,32 @@ _BENCH_DIR = _HERE.parent.parent _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" _OUTLIERS_JSON = _HERE / "outliers.json" _LOCAL_BASELINE = _HERE / "local_baseline.json" +def _profile(): + """Active sample profile (OPENEVOLVE_PROFILE env). 'small' (default) reads + legacy stage{N}_sample.json; 'large' reads stage{N}_large_sample.json, + falling back to legacy if the large file is missing.""" + return (os.environ.get("OPENEVOLVE_PROFILE") or "small").strip().lower() + + +def _stage_sample(stage_num): + profile = _profile() + if profile != "small": + suffixed = _HERE / f"stage{stage_num}_{profile}_sample.json" + if suffixed.exists(): + return suffixed + return _HERE / f"stage{stage_num}_sample.json" + + +_STAGE1_SAMPLE = _stage_sample(1) +_STAGE2_SAMPLE = _stage_sample(2) +_STAGE3_SAMPLE = _stage_sample(3) +_STAGE4_SAMPLE = _stage_sample(4) + + def _load_outlier_shas(): """Return set of SHAs flagged by outliers_top.csv (via build_samples.py writing shared/outliers.json). Empty if file missing.""" @@ -509,17 +527,68 @@ def _peek_workers(program_path): return 1 +# Large profile: no cascade stages — every cascade call evaluates the same +# outlier set once and caches. Cache lifetime = current evaluator process +# (openevolve spawns a fresh worker per variant, so cache holds exactly one +# entry per iteration). Keyed by program_path so a re-used path string doesn't +# leak stale metrics across variants. +_LARGE_RESULT_CACHE: dict = {} + + +def _load_large_sample_shas(): + path = _HERE / "stage1_large_sample.json" + if not path.exists(): + return None + try: + return set(json.loads(path.read_text())["sha256"]) + except (json.JSONDecodeError, KeyError, OSError): + return None + + +def _evaluate_large(program_path): + """Single-stage evaluation for OPENEVOLVE_PROFILE=large. Runs all outliers + once, caches result, returns same EvaluationResult on subsequent cascade + calls (stage2/stage3 entry points) so the final variant score reflects the + real outlier score instead of a pass-through placeholder.""" + key = str(program_path) + cached = _LARGE_RESULT_CACHE.get(key) + if cached is not None: + return cached + + keep = _load_large_sample_shas() + if keep is None: + result = _err_result( + {"error": "stage1_large_sample.json missing/invalid"}, + {"suggestion": "Run build_samples.py to regenerate.", + "stage": "large"}, + ) + _LARGE_RESULT_CACHE[key] = result + return result + + w = _peek_workers(program_path) + problems = [p for p in _load_problems(w) if p["sha"] in keep] + result = _evaluate(program_path, problems, "large") + _LARGE_RESULT_CACHE[key] = result + return result + + def evaluate_stage1(program_path): + if _profile() == "large": + return _evaluate_large(program_path) w = _peek_workers(program_path) return _evaluate(program_path, _filter_stage1(_load_problems(w)), "stage1") def evaluate_stage2(program_path): + if _profile() == "large": + return _evaluate_large(program_path) w = _peek_workers(program_path) return _evaluate(program_path, _filter_stage2(_load_problems(w)), "stage2") def evaluate_stage3(program_path): + if _profile() == "large": + return _evaluate_large(program_path) # openevolve cascade hardcodes 3 stages, so user-stage4 (broad runtime # sample) is chained inside stage3 via the runtime cascade_threshold gate. # @@ -557,6 +626,8 @@ def evaluate_stage3(program_path): def evaluate_stage4(program_path): # Standalone entry for manual / final-verify use. Not invoked by cascade. + if _profile() == "large": + return _evaluate_large(program_path) w = _peek_workers(program_path) problems = _filter_stage4(_load_problems(w)) return _evaluate(program_path, problems, "stage4") diff --git a/input/run_phase.sh b/input/run_phase.sh index 1d0bd786d8..3ff51707eb 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -6,13 +6,25 @@ # # Usage: # ./input/run_phase.sh [] [--pin N-M] [--extract-only] -# [--iterations ] [extra flags] +# [--iterations ] [--profile small|large] +# [extra flags] # # omitted → run ALL phases sequentially (1..N). # numeric → run that single phase. # +# --profile small (default) historical run: phase1/2 W=1, phase3/4 W=8, +# uses stage{1..4}_sample.json. Output: openevolve_output/. +# --profile large outlier-only tuning track: phase1..4 all W=8, uses +# stage{1..4}_large_sample.json (stage1=outliers, rest +# empty → cascade pass-through). Output: +# openevolve_output_large/. Sets OPENEVOLVE_PROFILE=large +# so evaluator + phase initial_program.py pick the right +# sample files and worker count. +# # Examples: -# ./input/run_phase.sh cpsat-bench # run all phases (1..4) +# ./input/run_phase.sh cpsat-bench # all phases, small profile +# ./input/run_phase.sh cpsat-bench --profile large # all phases, large profile +# ./input/run_phase.sh cpsat-bench 1 --profile large # single phase, large # ./input/run_phase.sh cpsat-bench --pin 2-7 # all phases, pinned # ./input/run_phase.sh cpsat-bench 1 --pin 2-7 # ./input/run_phase.sh z3-bench 4 @@ -89,6 +101,7 @@ PHASE_RANGE_STR="1..$N_PHASES" # Parse remaining flags EXTRACT_ONLY=0 PIN_RANGE="" +PROFILE="small" PASSTHROUGH=() while [ $# -gt 0 ]; do case "$1" in @@ -104,6 +117,14 @@ while [ $# -gt 0 ]; do PIN_RANGE="${1#--pin=}" shift ;; + --profile) + PROFILE="$2" + shift 2 + ;; + --profile=*) + PROFILE="${1#--profile=}" + shift + ;; -h|--help) usage exit 0 @@ -116,6 +137,24 @@ while [ $# -gt 0 ]; do done set -- "${PASSTHROUGH[@]+"${PASSTHROUGH[@]}"}" +case "$PROFILE" in + small|large) ;; + *) + echo "--profile must be 'small' or 'large' (got: $PROFILE)" >&2 + exit 2 + ;; +esac + +export OPENEVOLVE_PROFILE="$PROFILE" +echo "[run_phase] OPENEVOLVE_PROFILE=$PROFILE" + +# Profile-suffixed output dir so small/large runs don't collide. +if [ "$PROFILE" = "small" ]; then + OUTPUT_DIR="openevolve_output" +else + OUTPUT_DIR="openevolve_output_${PROFILE}" +fi + # Decide phase list if [ -z "$PHASE" ]; then if [ "$EXTRACT_ONLY" = "1" ]; then @@ -156,7 +195,14 @@ if [ "$EXTRACT_ONLY" != "1" ]; then exit 1 fi + need_build=0 if [ ! -f "$ROOT/shared/stage1_sample.json" ] || [ ! -f "$ROOT/shared/stage2_sample.json" ]; then + need_build=1 + fi + if [ "$PROFILE" = "large" ] && [ ! -f "$ROOT/shared/stage1_large_sample.json" ]; then + need_build=1 + fi + if [ "$need_build" = "1" ]; then echo "[run_phase] sample json missing — running $BUILD_SAMPLES_SCRIPT first..." python "$ROOT/$BUILD_SAMPLES_SCRIPT" fi @@ -296,7 +342,7 @@ run_one_phase() { fi cd "$ROOT/$dir" - echo "[run_phase] === bench=$BENCH phase=$phase dir=$dir ${iter:+iter=$iter }cwd=$(pwd) ===" + echo "[run_phase] === bench=$BENCH phase=$phase dir=$dir profile=$PROFILE output=$OUTPUT_DIR ${iter:+iter=$iter }cwd=$(pwd) ===" local iter_flag=() if [ -n "$iter" ]; then @@ -307,6 +353,7 @@ run_one_phase() { initial_program.py \ "$ROOT/shared/evaluator.py" \ --config "$ROOT/config.yaml" \ + --output "$OUTPUT_DIR" \ "${iter_flag[@]+"${iter_flag[@]}"}" \ "$@" From c51b48924f33d3d2e950d0f39f44b471b169f559 Mon Sep 17 00:00:00 2001 From: hdson Date: Wed, 27 May 2026 11:24:30 +0900 Subject: [PATCH 32/42] stage 3 tune --- input/cpsat-bench/evolve/build_samples.py | 181 ++++++++---------- input/cpsat-bench/evolve/shared/outliers.json | 8 +- .../evolve/shared/stage1_large_sample.json | 14 ++ .../evolve/shared/stage3_sample.json | 26 +-- .../evolve/shared/stage4_sample.json | 82 ++++---- 5 files changed, 150 insertions(+), 161 deletions(-) create mode 100644 input/cpsat-bench/evolve/shared/stage1_large_sample.json diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index 4030f31d5f..db05354812 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -72,23 +72,19 @@ # under W=8 (raw-data times are W=8-equivalent). Variant timeout = # baseline_ms * 1.3 in evaluator. STAGE3_MAX_BASELINE_MS = 1_500_000 -# Stratified stage3 pick — outliers cluster in two size regimes (small -# SAT-like and large LP-heavy). Picking only by residual order biases to -# small ones (they pass the cap easier). Bands: -# small : elapsed_ms < 10_000 (≤10s, vars≈1700-2000) -# mid : 10_000 ≤ ms < 500_000 (10s-500s, vars≈17000) -# large : 500_000 ≤ ms ≤ STAGE3_MAX (500s-1500s, vars≈20000) -# Within each band pick top-N by residual. -STAGE3_SMALL_MS = 10_000 -STAGE3_MID_MS = 500_000 -STAGE3_PICK_SMALL = 2 -STAGE3_PICK_MID = 2 -STAGE3_PICK_LARGE = 1 +# Stage3 target-based pick — outliers in outliers_top.csv span 4 orders of +# magnitude (180ms .. 5.7M ms). Picking by residual alone produces a sample +# with wild runtime variance, which makes per-iteration scoring noisy. Instead +# pick STAGE3_N outliers whose baseline elapsed_ms is closest to +# STAGE3_TARGET_MS, dropping any whose ratio to target exceeds +# STAGE3_TARGET_TOL_RATIO (e.g. target=20000 tol=5.0 → accepts 4000..100000ms). +STAGE3_TARGET_MS = 20_000 +STAGE3_TARGET_TOL_RATIO = 5.0 OUTLIER_IQR_K = 3.0 STAGE1_STRATEGY = "center" STAGE2_STRATEGY = "center" -STAGE3_STRATEGY = "outliers" # was "center" — now picks outliers_top.csv +STAGE3_STRATEGY = "target_ms" # pick outliers nearest STAGE3_TARGET_MS STAGE4_STRATEGY = "spread" _DECISIVE_RESULTS = {"OPTIMAL", "FEASIBLE"} @@ -180,96 +176,76 @@ def _load_outliers_top(csv_path): return rows -def _pick_outliers(rows_by_sha, csv_path): - """Stratified outlier pick: bucket csv rows by elapsed_ms into - small/mid/large, then within each bucket pick top-N by residual. +def _pick_outliers(rows_by_sha, csv_path, all_rows=None, n_pick=None, + target_ms=None, tol_ratio=None): + """Target-ms pick for stage3: prefer outliers_top.csv entries whose + baseline elapsed_ms is closest to target_ms; if fewer than n_pick survive + the tol_ratio filter, backfill from the full problems pool (non-outlier + problems also near target_ms). - Returns (picks, csv_rows). picks is in [small..., mid..., large...] order. + Returns (picks, csv_rows). picks sorted by ascending |elapsed_ms-target_ms|. csv_rows is the full csv (for outliers.json metadata). - Falls back to (None, []) if csv missing — caller uses legacy slow-cluster - center pick then.""" + Returns (None, []) when csv missing — caller falls back to slow-cluster + center pick.""" if csv_path is None: print("warning: outliers_top.csv not found — falling back to " "slow-cluster center pick for stage3", file=sys.stderr) return None, [] + if n_pick is None: + n_pick = STAGE3_N + if target_ms is None: + target_ms = STAGE3_TARGET_MS + if tol_ratio is None: + tol_ratio = STAGE3_TARGET_TOL_RATIO + csv_rows = _load_outliers_top(csv_path) - def _band(ms): + def _within_tol(ms): if ms <= 0 or ms > STAGE3_MAX_BASELINE_MS: - return None - if ms < STAGE3_SMALL_MS: - return "small" - if ms < STAGE3_MID_MS: - return "mid" - return "large" - - by_band = {"small": [], "mid": [], "large": []} - for c in csv_rows: - if c["sha"] not in rows_by_sha: - continue - band = _band(c["elapsed_ms"]) - if band is None: - continue - by_band[band].append(c) - # csv_rows already sorted by descending residual → by_band lists inherit. - - band_targets = { - "small": STAGE3_PICK_SMALL, - "mid": STAGE3_PICK_MID, - "large": STAGE3_PICK_LARGE, - } + return False + return max(ms / target_ms, target_ms / ms) <= tol_ratio + + # Pass 1: outliers within tol. + outlier_eligible = [c for c in csv_rows + if c["sha"] in rows_by_sha and _within_tol(c["elapsed_ms"])] + outlier_eligible.sort(key=lambda c: abs(c["elapsed_ms"] - target_ms)) + outlier_pick_csv = outlier_eligible[:n_pick] + picks = [rows_by_sha[c["sha"]] for c in outlier_pick_csv] + used_shas = {c["sha"] for c in outlier_pick_csv} + + # Pass 2: backfill from full problems pool near target_ms (non-outliers). + backfill_picks = [] + if len(picks) < n_pick and all_rows is not None: + decisive = (d for d in all_rows + if _id_key(d) not in used_shas + and _within_tol(_runtime_key(d)) + and _result_key(d) in _DECISIVE_RESULTS) + backfill_pool = sorted(decisive, + key=lambda d: abs(_runtime_key(d) - target_ms)) + need = n_pick - len(picks) + backfill_picks = backfill_pool[:need] + picks.extend(backfill_picks) - picks = [] - used = set() - diag = [] - for band in ("small", "mid", "large"): - target = band_targets[band] - taken = 0 - for c in by_band[band]: - if taken >= target: - break - if c["sha"] in used: - continue - d = rows_by_sha[c["sha"]] - picks.append(d) - used.add(c["sha"]) - taken += 1 - diag.append((band, c["sha"][:12], c["elapsed_ms"], - f"pick (residual={c['residual']:.3f}, " - f"n_cons={c['n_cons']})")) - # Backfill: if a band can't fill its quota, try next band's pool. - for c in by_band[band][taken:]: - diag.append((band, c["sha"][:12], c["elapsed_ms"], - f"available (residual={c['residual']:.3f})")) - - # If any band underfilled, top off from remaining bands by residual. - n_target_total = sum(band_targets.values()) - if len(picks) < n_target_total: - leftover = [c for band in ("large", "mid", "small") - for c in by_band[band] if c["sha"] not in used] - leftover.sort(key=lambda c: -c["residual"]) - for c in leftover: - if len(picks) >= n_target_total: - break - d = rows_by_sha[c["sha"]] - picks.append(d) - used.add(c["sha"]) - diag.append(("backfill", c["sha"][:12], c["elapsed_ms"], - f"backfill (residual={c['residual']:.3f})")) - - n_small = sum(1 for b, *_ in diag if b == "small" and "pick" in _[-1]) - n_mid = sum(1 for b, *_ in diag if b == "mid" and "pick" in _[-1]) - n_large = sum(1 for b, *_ in diag if b == "large" and "pick" in _[-1]) print(f"outliers stage3: from {csv_path.relative_to(_BENCH.parent)}, " - f"stratified pick (cap={STAGE3_MAX_BASELINE_MS}ms) " - f"→ {len(picks)} total " - f"[small={n_small}/{STAGE3_PICK_SMALL} " - f"mid={n_mid}/{STAGE3_PICK_MID} " - f"large={n_large}/{STAGE3_PICK_LARGE}]") - for band, sha12, ms, note in diag: - print(f" [{band:<8}] {sha12} {int(ms):>10}ms {note}") + f"target={target_ms}ms tol={tol_ratio}x cap={STAGE3_MAX_BASELINE_MS}ms " + f"→ {len(picks)}/{n_pick} " + f"(outliers={len(outlier_pick_csv)}, backfill={len(backfill_picks)})") + for c in outlier_pick_csv: + ratio = max(c["elapsed_ms"] / target_ms, target_ms / c["elapsed_ms"]) + print(f" outlier {c['sha'][:12]} {int(c['elapsed_ms']):>10}ms " + f"Δ={int(c['elapsed_ms'] - target_ms):+d}ms " + f"ratio={ratio:.2f}x residual={c['residual']:.3f}") + for d in backfill_picks: + ms = _runtime_key(d) + ratio = max(ms / target_ms, target_ms / ms) + print(f" backfill {_id_key(d)[:12]} {int(ms):>10}ms " + f"Δ={int(ms - target_ms):+d}ms ratio={ratio:.2f}x") + if len(picks) < n_pick: + print(f" warning: only {len(picks)} candidates within tol={tol_ratio}x " + f"of {target_ms}ms — widen STAGE3_TARGET_TOL_RATIO or change " + f"STAGE3_TARGET_MS to fill stage3") return picks, csv_rows @@ -449,24 +425,23 @@ def _bucket_range(b): s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) - # Stage3: stratified outlier pick from Statistics/outliers_top.csv - # (small/mid/large by elapsed_ms, top-residual within each band). cap = - # STAGE3_MAX_BASELINE_MS (higher than stage1/2/4's MAX_BASELINE_MS so - # genuinely slow outliers enter the tune set). Fallback to slow-cluster - # center pick if csv missing. + # Stage3: target-ms outlier pick from Statistics/outliers_top.csv — + # outliers whose baseline_ms is closest to STAGE3_TARGET_MS (within + # STAGE3_TARGET_TOL_RATIO). Keeps stage3 runtime variance low so cascade + # score is dominated by signal, not noise from a 1500ms vs 1500000ms split. + # Fallback to slow-cluster center pick if csv missing. rows_by_sha = {_id_key(d): d for d in rows} outliers_csv = _find_outliers_csv() - s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv) \ + s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv, + all_rows=rows) \ if outliers_csv else (None, []) if s3_outliers: s3 = s3_outliers - stage3_criteria = (f"stratified outliers from {outliers_csv.name} " - f"(small≤{STAGE3_SMALL_MS}ms / " - f"mid<{STAGE3_MID_MS}ms / " - f"large≤{STAGE3_MAX_BASELINE_MS}ms; " - f"top-residual within band; " - f"target {STAGE3_PICK_SMALL}+{STAGE3_PICK_MID}+" - f"{STAGE3_PICK_LARGE})") + stage3_criteria = ( + f"outliers from {outliers_csv.name} nearest " + f"{STAGE3_TARGET_MS}ms (tol={STAGE3_TARGET_TOL_RATIO}x, " + f"cap={STAGE3_MAX_BASELINE_MS}ms; target {STAGE3_N})" + ) else: s3 = _center_pick(pool_c5, STAGE3_N) stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " diff --git a/input/cpsat-bench/evolve/shared/outliers.json b/input/cpsat-bench/evolve/shared/outliers.json index 045c251cd3..951f103a03 100644 --- a/input/cpsat-bench/evolve/shared/outliers.json +++ b/input/cpsat-bench/evolve/shared/outliers.json @@ -1,10 +1,10 @@ { "stage3_sample": [ "5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746", - "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", - "c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c", - "073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151", - "7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de" + "c0a0dc04538c4ae54401b99c91b6b743811aab04bbc638360c16b4fc9b915728", + "c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5", + "5b8c61f99d31fed9077ef475613e5be6648f55f2a47bcc92c0e5d0e3b41ecb94", + "0976039f182f3d2a82c23ebc237b080434e5f24b43329696d42000dc1865231e" ], "outliers": { "9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e": { diff --git a/input/cpsat-bench/evolve/shared/stage1_large_sample.json b/input/cpsat-bench/evolve/shared/stage1_large_sample.json new file mode 100644 index 0000000000..3a8e8e6a8f --- /dev/null +++ b/input/cpsat-bench/evolve/shared/stage1_large_sample.json @@ -0,0 +1,14 @@ +{ + "selection": "1 top-1 residual outlier(s) from outliers_top.csv (W=8 tuning set)", + "source": "cpsat-bench/problems.jsonl", + "sha256": [ + "9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e" + ], + "summary": [ + { + "sha": "9c28c175d2cd", + "baseline_result": "OPTIMAL", + "baseline_ms": 5307350.0 + } + ] +} diff --git a/input/cpsat-bench/evolve/shared/stage3_sample.json b/input/cpsat-bench/evolve/shared/stage3_sample.json index 0a59eb19ae..501c91cae3 100644 --- a/input/cpsat-bench/evolve/shared/stage3_sample.json +++ b/input/cpsat-bench/evolve/shared/stage3_sample.json @@ -1,12 +1,12 @@ { - "selection": "5 stratified outliers from outliers_top.csv (small\u226410000ms / mid<500000ms / large\u22641500000ms; top-residual within band; target 2+2+1)", + "selection": "5 outliers from outliers_top.csv nearest 20000ms (tol=5.0x, cap=1500000ms; target 5)", "source": "cpsat-bench/problems.jsonl", "sha256": [ "5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746", - "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", - "c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c", - "073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151", - "7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de" + "c0a0dc04538c4ae54401b99c91b6b743811aab04bbc638360c16b4fc9b915728", + "c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5", + "5b8c61f99d31fed9077ef475613e5be6648f55f2a47bcc92c0e5d0e3b41ecb94", + "0976039f182f3d2a82c23ebc237b080434e5f24b43329696d42000dc1865231e" ], "summary": [ { @@ -15,24 +15,24 @@ "baseline_ms": 6436.58 }, { - "sha": "303a6733c9ec", + "sha": "c0a0dc04538c", "baseline_result": "OPTIMAL", - "baseline_ms": 3530.81 + "baseline_ms": 4920.17 }, { - "sha": "c0bbae62f14d", + "sha": "c4fe2cc04ae7", "baseline_result": "OPTIMAL", - "baseline_ms": 400652 + "baseline_ms": 4839.59 }, { - "sha": "073ef8ba3455", + "sha": "5b8c61f99d31", "baseline_result": "OPTIMAL", - "baseline_ms": 1496560.0 + "baseline_ms": 19871.1 }, { - "sha": "7e5aa4016ad6", + "sha": "0976039f182f", "baseline_result": "OPTIMAL", - "baseline_ms": 1482010.0 + "baseline_ms": 19431.4 } ] } diff --git a/input/cpsat-bench/evolve/shared/stage4_sample.json b/input/cpsat-bench/evolve/shared/stage4_sample.json index 74c934e73c..4669357bf3 100644 --- a/input/cpsat-bench/evolve/shared/stage4_sample.json +++ b/input/cpsat-bench/evolve/shared/stage4_sample.json @@ -8,20 +8,20 @@ "915f8918d0b3d7d39a423af66871046decda0ae1e8230bbdeb43f12a08e79fc7", "c867ab59144791c88799ce95599bbd25090dec398595de1c1e35d01b8a7f06ec", "2abe830de840839cc8700da827e94f67e8231e6dbc5fdba2042dc0a5cfc022ff", - "37f84b16de74e01a63c4bdcf999c82d18ebf6d0e03406bd9ee65adb21ae54b71", + "5248aba6397b3d34d16c24463aa8e1a7a3591f670a094a30becd777ecf915a9a", + "9c801bcf5dc6a4fa11be76710e268871e68ba55da83399f00fb9e0390f675962", "ca043f0850c14f2d99c0a48b1a959387505b2facf3551fa053ddb9c68bfa58b5", - "674b484eb726690bc9d4878cb217bc6b22f30eac4f8d512be6e2713a303545dd", - "57b759f315f04cd49b38987b95a953459c7c29e4f53de8dd7a16f65c5057c92b", - "5978abbd9b38469692be7af1f78c5fd15d0736c6192c38ba083c6ca4778659a5", - "4b5c183b293875eb4e0b73aabd4581cbd17ba882b436d0dd49299632ccbbc661", - "57b21c3f6c8f3ceb646f5ce6950949114d86bc1a225d6db5437e6f654821a6c6", - "1cbb4fb20584a2df3ff85a980e992daccf8381fb6da66657c08a35a9f6f61fe3", - "6c7a1a5ce29490757a4fd01319c3af2f090f1488a9bd158edeb09a45ef95d8e0", - "a0cd1b349e85c374dac7fd4570d5dc90928422dea74e38afe67defa14d8a13a5", - "643f0a85b4d3d3c13ef9cfbbbc20b1dc8f9d837a181ad2ebb84c6823e91829b5", - 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00:00:00 2001 From: hdson Date: Thu, 28 May 2026 14:06:02 +0900 Subject: [PATCH 33/42] [docs] add openevolve study --- docs/openevolve-intro/assets/styles.css | 298 +++++ docs/openevolve-intro/index.html | 1125 +++++++++++++++++++ docs/openevolve-intro/sections/README.md | 24 + docs/openevolve-intro/slides.html | 1303 ++++++++++++++++++++++ 4 files changed, 2750 insertions(+) create mode 100644 docs/openevolve-intro/assets/styles.css create mode 100644 docs/openevolve-intro/index.html create mode 100644 docs/openevolve-intro/sections/README.md create mode 100644 docs/openevolve-intro/slides.html 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..104fdc8d7c --- /dev/null +++ b/docs/openevolve-intro/index.html @@ -0,0 +1,1125 @@ + + + + + + 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

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

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Artifact 사이드 채널 피드백

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

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2.1 MAP-Elites Quality-Diversity — 자세히

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+ 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. +

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+ 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. +

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3단계 처리 파이프라인

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

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  • Fitness(점수): "같은 셀에 들어온 두 프로그램 중 누가 1등이냐"만 결정. 셀 안 비교용.
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  • Feature 좌표: "어느 셀에 들어갈지"만 결정. 셀 배치용.
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+ 이 분리가 핵심이다. 점수가 좌표를 결정하면 다양성을 잃는다. 좌표가 독립이라 점수가 낮은 영역도 살아남고, 그 영역에서 나중에 더 좋은 변형이 나오면 셀이 갱신된다. +

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+ 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. +
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그림으로 보는 격자 비유

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+ 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. +

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + ★ + + + 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.
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  • "단순하면서 빠른" 셀과 "복잡하지만 더 빠른" 셀이 동시에 챔피언을 가진다.
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  • 한 셀의 챔피언이 다른 셀에서 영감 코드로 쓰일 수 있다 — 다른 셀에 사는 좋은 아이디어를 빌려 자기 셀의 챔피언을 갱신.
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  • 격자 전체가 채워지면 "이 코드 공간에서 어떤 트레이드오프가 가능한지"에 대한 지도(map)가 자동으로 그려진다.
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+ 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. +
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+ 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). +

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2.2 Island 기반 분산 집단 — 자세히

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+ 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. +

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+ 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. +

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Migration — 격리만 하면 안 되는 이유

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

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

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타겟 코드 선택 — Exploration vs Exploitation

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

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  • Exploration: 현재 island 인구에서 균일 무작위 추출. 점수가 낮은 후보에게도 기회.
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  • Exploitation: archive(island의 elite 모음)에서 fitness 비례 가중 추출. 좋은 타겟에 집중.
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  • Fallback: island가 비었을 때 global best를 시드로 주입 (best가 모든 island를 재시작시킬 수 있음).
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+ Best 추적 분리. OpenEvolve는 island별 best와 별도로 global best를 영구 보존한다. MAP-Elites 격자 갱신 과정에서 best가 셀에서 밀려 사라지는 사고를 막기 위한 안전장치. +

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그림으로 보는 Island 비유 — 갈라파고스

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+ 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. +

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+ + + + + + + + + + + + + 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.
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  • 섬 = island. 격리된 독립 진화 공간.
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  • 핀치새 부리 = 진화 중인 코드 변형. 같은 초기 프로그램에서 출발했지만 섬마다 다른 방향으로 변형.
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  • 새가 다른 섬으로 날아감 = migration. 우수 변형을 옆 island로 복사.
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  • 섬 멸종해도 다른 섬 살아남음 = 분산 안전망. 한 island가 나쁜 지역 최적해에 빠져도 다른 island가 다른 줄기를 계속 진화시킴.
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+ 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. +
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2.3 Artifact 사이드 채널 피드백 — 자세히

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+ 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. +

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

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

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저장 정책

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

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### stderr
+```
+(error "smt.case_split=5 out of range")
+```
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+### solver_stats
+```
+conflicts=1.3M decisions=2.1M propagations=8.9M
+```
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+ 각 artifact 키가 markdown 헤더로, 값이 코드 펜스로 들어간다. LLM이 자연스럽게 읽고 다음 변형에서 참고한다. +

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다른 피드백 채널과의 차이

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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—
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+ 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. +
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+ 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. +

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3. 큰 그림 아키텍처

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+ 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. +
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4. 한 iteration의 생애 — 피드백 루프

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+ 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.
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각 단계 세부

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  1. Sample — 현재 island에서 exploration(다양성) vs exploitation(엘리트) 비율로 타겟 코드와 영감 프로그램을 무작위 추출.
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  2. Build Prompt — 타겟 코드, 과거 시도 3개 점수, 다른 island의 elite 5개, 직전 평가 artifact를 하나의 prompt로 묶음.
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  3. LLM Generate — Ensemble이 가중치로 모델 1개를 뽑음. 그 모델이 diff 블록 또는 전체 재작성 코드를 출력.
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  4. Parse Code — diff 모드면 SEARCH/REPLACE 블록을 타겟 코드에 적용; rewrite 모드면 코드 블록 추출.
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  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 phase2 진화 로그 읽기

+

+ 여기까지가 이론. 이제 실제 로그를 따라가며 LLM이 어떻게 CP-SAT 파라미터를 튜닝하는지 본다. 로그 출처: input/cpsat-bench/evolve/phase2_presolve/openevolve_output/logs/openevolve_20260527_154008.log. Phase2는 phase1의 best 결과를 물려받아 presolve/probing 표면을 미세 조정하는 단계. +

+ +

7.1 실행 환경

+ + + + + + + + + + + +
항목값
LLM Ensembleclaude-sonnet-4-6 (weight 0.80) + claude-haiku-4-5 (weight 0.20)
Random seed42 (재현성 확보)
Island3개
총 iteration20
고정(LOCKED) 파라미터random_seed=0, num_search_workers=1 — LLM이 못 건드림
샘플 문제stage1=10개, stage2=10개, stage3=outlier (cascade 평가)
Phase 위치phase1 best 상속 → presolve 표면 튜닝
+ +

7.2 시작점 — 이미 높은 baseline (score 0.9976)

+

+ Phase2의 초기 initial_program.py는 phase1에서 검증된 minimum 설정만 들고 있다. Phase1이 다양한 search 옵션을 시도한 뒤 살아남은 핵심 파라미터: +

+ 
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,
+}
+
+SIZE_BUCKETS = [
+    (50_000,         {}),
+    (150_000,        {}),
+    (float("inf"),   {}),
+]
+
+STAGE3_OVERRIDES = {}
+ +

평가 결과:

+ 
combined_score = 0.9976
+geomean_speedup = 1.0409          ← deterministic_time 4.1% 단축
+geomean_wall_speedup = 1.2351     ← wall time 23.5% 빠름
+solved_rate = 1.0 (10/10 OPTIMAL)
+efficiency = 0.8802               ← 일부 문제에서 conflicts 약간 증가 (88점)
+ +
+ Phase1과의 큰 차이. Phase1은 score=0에서 출발했지만 Phase2는 시작부터 0.9976. 즉 phase1이 잘 작동해 미세 조정만 남은 상태. 큰 도약 대신 점진적 개선이 목표. +
+ +

7.3 Iteration 1 — SIZE_BUCKETS와 STAGE3_OVERRIDES 채움 (score 1.0)

+

+ Iteration 1에서 ensemble이 claude-haiku-4-5를 샘플링. LLM thinking 로그를 한국어로 요약하면: +

+ +
+ LLM 사고 요약 (번역): +
    +
  1. "현재 score 0.9976 — 이미 매우 높음. 큰 점프 대신 점진적 개선이 필요하다."
    2. +
  2. "Phase2는 presolve/probing 튜닝 단계. cp_model_probing_level=1이 글로벌 기본인데, 작은 문제(<50k)는 SAT-유사 특성이라 probing을 더 높게 가져도 안전하다."
    3. +
  3. "SIZE_BUCKETS의 첫 버킷에 cp_model_probing_level: 2 추가. 중/대 버킷은 그대로 두어 LP-heavy 인스턴스 회귀 방지."
    4. +
  4. "Outlier(stage3 전용)에는 더 공격적으로 — cp_model_probing_level: 3, symmetry_level: 3, probing_num_combinations_limit: 35000. STAGE3_OVERRIDES는 outlier에만 적용되니 일반 stage1/2 성능 손해 없음."
    5. +
+
+ +

+ LLM이 적용한 새 EVOLVE-BLOCK: +

+ + 
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,    # 글로벌은 보수적으로 유지
+}
+
+SIZE_BUCKETS = [
+    (50000,  {"cp_model_probing_level": 2}),   # 작은 문제 — probing 올림
+    (150000, {}),                              # 중간 — 그대로
+    (inf,    {}),                              # 큰 문제 — 그대로 (LP-heavy 보호)
+]
+
+STAGE3_OVERRIDES = {
+    "cp_model_probing_level": 3,               # outlier 전용 — 강력
+    "symmetry_level": 3,
+    "probing_num_combinations_limit": 35000,
+}
+ +

평가 결과:

+ 
iter 1: combined_score = 1.0000  (0.9976 → 1.0000, +0.0024)
+        solved_rate = 1.0  (10/10 OPTIMAL)
+        geomean_speedup = 1.0000
+        geomean_wall_speedup = 1.1226
+        efficiency = 1.0000           ← efficiency 페널티 사라짐
+        🌟 New best solution found at iteration 1
+ +
+ 주목할 점. wall_speedup은 1.2351 → 1.1226으로 살짝 하락. 하지만 combined_score는 증가. 이유는 efficiency 0.88 → 1.00. score 식이 wall만 보지 않고 efficiency까지 가중치를 두기 때문에, "솔버가 더 적은 일을 해서 같은 결과를 얻은 변형"이 더 높은 점수를 받는다. +
+ +

7.4 Iteration 5~6 — 욕심내다 efficiency 페널티

+

+ Iteration 5, 6에서 LLM이 더 공격적인 presolve 옵션을 시도. 결과는 의외: +

+ 
iter 5: combined_score=0.7969  geomean_speedup=0.8460  efficiency=0.8356
+iter 6: combined_score=0.6964  geomean_speedup=0.7689  efficiency=0.7427
+

+ OPTIMAL은 다 맞췄지만 (solved_rate=1.0) score는 큰 폭으로 하락. 원인은 두 가지: +

+
    +
  • geomean_speedup < 1.0 — variant가 baseline보다 deterministic_time을 더 많이 씀
    • +
  • efficiency 하락 — conflicts 5,221 → 7,817~8,670개로 증가. 솔버가 헤맴.
    • +
+ +
+ 학습 포인트. 이미 score 1.0인 상태에서 추가 개선은 매우 어렵다. 어설픈 욕심이 오히려 후퇴. score 식 자체가 "더 빠르거나 더 효율적이면 보상, 양쪽 다 나쁘면 페널티" 구조라 stable한 변형이 valuable. +
+ +

7.5 Iteration 7~13 — Regression 폭주와 같은 문제 반복

+

+ Iteration 7, 8, 10, 11, 12, 13 모두 같은 에러: +

+ 
combined_score = 0.0000
+error = "result regression on 3e69d6f5b7: baseline=OPTIMAL got=UNKNOWN"
+

+ 같은 문제(SHA 3e69d6f5b7, num_constraints=28938)에서 반복적으로 OPTIMAL → UNKNOWN. LLM이 매번 다른 변형을 시도하는데 자꾸 이 한 문제가 무너진다. 이 문제는 phase1에서 무난히 풀던 문제 — phase2의 presolve 튜닝이 이 문제의 핵심 구조를 깬다는 의미. +

+ +
+ Phase2의 어려움. Phase1과 달리 phase2의 가능한 변형 공간이 이미 잘 작동하는 baseline에 비해 좁다. 한 발만 잘못 디뎌도 OPTIMAL → UNKNOWN. LLM이 conservative 영역에서 surf해야 함. +
+ +

7.6 Iteration 14 — 복구 (score 0.969)

+

+ 여러 차례 regression 후 iter 14에서 다시 OPTIMAL 회복: +

+ 
iter 14: combined_score = 0.9690
+         geomean_speedup = 1.0113
+         geomean_wall_speedup = 1.2008
+         efficiency = 0.8794
+         solved_rate = 1.0
+

+ Parent는 iter 4 (04718c2f, score 0.7969)의 자식이지만 iter 1의 검증된 SIZE_BUCKETS 패턴으로 회귀. 즉 LLM이 artifact를 통해 "3e69d6f5b7에서 무엇이 깨졌는지" 학습 후 안전한 영역으로 복귀. +

+ +

7.7 종합 — Phase2가 보여주는 OpenEvolve 동작 양상

+ + + + + + + + + + + +
특징이 로그에서 확인된 증거
Phase 체이닝phase1 best (cp_model_probing_level=1)이 phase2 initial로 import. 시작 점수 이미 0.9976.
점진적 개선iter 1: 0.9976 → 1.0000 (+0.0024). 점프가 아닌 미세 조정.
Efficiency vs Wall 트레이드오프iter 1은 wall은 약간 느려졌지만 efficiency가 좋아져 종합 점수 상승.
STAGE3_OVERRIDES 활용LLM이 "outlier 전용 공격적 설정"을 분리 — stage1/2 안전, stage3에만 적용.
반복적 동일 문제 실패iter 7~13: 같은 문제(3e69d6f5b7)에서 반복 UNKNOWN — phase2 변형이 이 문제 구조를 깸.
Artifact 학습 복구iter 14에서 LLM이 regression 메시지를 보고 안전 영역으로 회귀.
Cascade 평가모든 iteration이 stage1만 평가됨 — 점수 1.0 이상 도달 못 해 stage2 진입 못 함 (phase2의 한계).
+ +

+ 20 iteration 후 phase2의 best 변형은 iter 1 (combined_score=1.0000, SIZE_BUCKETS + STAGE3_OVERRIDES 추가). 이 best가 추출돼 phase3의 initial_program.py로 import되고 다음 표면(LP/cuts) 튜닝이 시작된다. +

+ +
+ Phase1 vs Phase2 비교. Phase1은 score 0 → 1.0822로 큰 도약 (탐색). Phase2는 0.9976 → 1.0000으로 미세 조정 (수렴). 두 phase의 진화 양상이 본질적으로 다르며, 이게 OpenEvolve의 multi-phase cascade가 단계마다 적절한 최적화를 하는 이유. +
+
+ + +
+

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..19865ec40f --- /dev/null +++ b/docs/openevolve-intro/slides.html @@ -0,0 +1,1303 @@ + + + + + +OpenEvolve — 슬라이드 발표 + + + + + +
+ + 1 / 35 + + + + +
+ + +
+

키보드 단축키

+ + + + + + + +
→ Space PgDn다음 슬라이드
← PgUp이전 슬라이드
Home첫 슬라이드
End마지막 슬라이드
?이 도움말
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.

+

OpenEvolve = Google DeepMind AlphaEvolve의 오픈소스 구현.

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+
1. TL;DR
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+
+ + +
+
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. +
+
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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.

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+
+
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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).

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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.

+
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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.

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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.

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+
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.

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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 + +
+
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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 + +
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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
16 / 35
+
+ + +
+
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 phase2 진화 로그 읽기

+
+

이론은 끝. 실제 phase2 로그를 따라가며 LLM이 CP-SAT presolve 파라미터를 미세 조정하는 과정 관찰.

+ +

+ 로그: input/cpsat-bench/evolve/phase2_presolve/openevolve_output/logs/openevolve_20260527_154008.log +

+ +

실행 환경

+ + + + + + + + + +
LLM Ensembleclaude-sonnet-4-6 (0.80) + claude-haiku-4-5 (0.20)
Random seed42 (재현성)
Island3개
총 iteration20
LOCKED 파라미터random_seed=0, num_search_workers=1
Phase 위치phase1 best 상속 → presolve 표면 튜닝
+
+
7. 실전 로그
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+
+ + +
+
7.2 시작점

이미 높은 baseline — score 0.9976

+
+

Phase2 초기 프로그램은 phase1에서 검증된 minimum 설정만 들고 시작.

+ +
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,
+}
+
+SIZE_BUCKETS = [
+    (50_000,         {}),
+    (150_000,        {}),
+    (float("inf"),   {}),
+]
+
+STAGE3_OVERRIDES = {}
+ +
+ 평가 결과:
+ combined_score = 0.9976 (시작부터 매우 높음)
+ geomean_speedup = 1.0409 (dtime 4.1% 단축)
+ geomean_wall_speedup = 1.2351 (wall 23.5% 빠름)
+ solved_rate = 1.0 (10/10 OPTIMAL)
+ efficiency = 0.8802 (일부 문제 conflicts 약간 증가) +
+
+
7.2 시작점
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+
+ + +
+
7.2 시작점

Phase1과의 큰 차이

+
+ + + + + + + + +
항목Phase1Phase2
초기 점수0.0000 (망가짐)0.9976 (이미 좋음)
탐색 공간search/subsolvers (큰 공간)presolve/probing (좁은 공간)
변형 방향"근본 수정" (멀티워커 옵션 제거)"미세 조정" (SIZE_BUCKETS 채우기)
기대 진행큰 점프 (0→1.0)점진적 (0.9976→1.0)
+ +
+ Phase2의 본질. phase1이 잘 작동해 미세 조정만 남은 상태. 어설픈 욕심은 오히려 regression. LLM은 conservative 영역에서 surf해야 함. +
+
+
7.2 시작점
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+
+ + +
+
7.3 Iteration 1

LLM의 진단 (thinking 로그 번역)

+
+
+
    +
  1. "score 0.9976 — 이미 매우 높음. 큰 점프 대신 점진적 개선이 필요."
    2. +
  2. "cp_model_probing_level=1은 글로벌 기본인데, 작은 문제(<50k)는 SAT-유사라 probing을 더 높게 가져도 안전."
    3. +
  3. "SIZE_BUCKETS 첫 버킷에 cp_model_probing_level: 2 추가. 중/대 버킷은 그대로 — LP-heavy 인스턴스 회귀 방지."
    4. +
  4. "Outlier(stage3 전용)는 더 공격적: probing_level: 3, symmetry_level: 3. STAGE3_OVERRIDES는 outlier에만 적용되니 stage1/2 손해 없음."
    5. +
+
+

LLM이 buckets별로 다른 강도를 적용하는 layered tuning 전략 채택.

+
+
7.3 Iteration 1
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+
+ + +
+
7.3 Iteration 1

변형 적용 → 미세 개선

+
+
GLOBAL_OVERRIDES = {
+    "cp_model_probing_level": 1,    # 글로벌 보수적 유지
+}
+
+SIZE_BUCKETS = [
+    (50000,  {"cp_model_probing_level": 2}),   # 작은 문제만 올림
+    (150000, {}),                              # 중간 — 그대로
+    (inf,    {}),                              # 큰 문제 — 그대로
+]
+
+STAGE3_OVERRIDES = {
+    "cp_model_probing_level": 3,               # outlier — 강력
+    "symmetry_level": 3,
+    "probing_num_combinations_limit": 35000,
+}
+ +
+ 평가 결과:
+ combined_score: 0.9976 → 1.0000 (+0.0024)
+ efficiency: 0.8802 → 1.0000 (페널티 사라짐)
+ wall_speedup 1.2351 → 1.1226 (살짝 하락하지만 score는 상승)
+ 🌟 New best solution found at iteration 1 +
+
+
7.3 Iteration 1
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+
+ + +
+
7.4 Iter 5~6

욕심내다 efficiency 페널티

+
+
iter 5: combined_score=0.7969  geomean_speedup=0.8460  efficiency=0.8356
+iter 6: combined_score=0.6964  geomean_speedup=0.7689  efficiency=0.7427
+ +

OPTIMAL은 다 맞췄는데 점수 큰 폭 하락. 원인 둘:

+ +
    +
  • geomean_speedup < 1.0 — variant가 baseline보다 dtime 더 씀
    • +
  • efficiency 하락 — conflicts 5,221 → 7,817~8,670 (솔버 헤맴)
    • +
+ +
+ 학습 포인트. 이미 1.0인 상태에서 추가 개선은 매우 어렵다. 어설픈 욕심이 오히려 후퇴. Score 식 자체가 안정성을 강조. +
+
+
7.4 Iter 5~6
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+
+ + +
+
7.5 Iter 7~13

Regression 폭주 — 같은 문제 반복

+
+
iter 7:  combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+iter 8:  combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+iter 10: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+iter 11: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+iter 12: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+iter 13: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
+ +

같은 문제(SHA 3e69d6f5b7, constraints=28938)에서 반복적 OPTIMAL → UNKNOWN. 매 iteration LLM이 다른 변형 시도하지만 자꾸 이 한 문제가 무너짐.

+ +
+ Phase2의 어려움. 가능한 변형 공간이 이미 좋은 baseline에 비해 좁다. 한 발만 잘못 디뎌도 핵심 문제 구조를 깬다. LLM이 conservative 영역에서 surf해야 함. +
+
+
7.5 Iter 7~13
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+
+ + +
+
7.6 Iter 14

Artifact 학습으로 복구

+
+
iter 14: combined_score = 0.9690
+         geomean_speedup = 1.0113
+         geomean_wall_speedup = 1.2008
+         efficiency = 0.8794
+         solved_rate = 1.0
+         parent = iter 4 (04718c2f, score=0.7969)
+ +

여러 차례 regression 후 iter 14에서 다시 OPTIMAL 회복. Parent는 iter 4의 자식이지만 iter 1의 검증된 SIZE_BUCKETS 패턴으로 회귀.

+ +
+ 핵심. LLM이 artifact error 메시지("3e69d6f5b7: OPTIMAL→UNKNOWN")를 반복적으로 보면서 "이 영역은 위험"이라는 신호를 학습. 안전 영역으로 자율 회귀. +
+ +

점수만 줬을 때는 불가능한 인과적 디버깅 — artifact 피드백의 가치 실증.

+
+
7.6 Iter 14
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+
+ + +
+
7.7 종합

Phase2 로그가 보여준 동작 양상

+
+ + + + + + + + + + +
특징로그 증거
Phase 체이닝phase1 best이 phase2 initial로 import. 시작 점수 이미 0.9976
점진적 개선iter 1: 0.9976 → 1.0000. 점프 아닌 미세 조정
Efficiency vs Walliter 1: wall 살짝 느려도 efficiency 개선으로 종합 점수 상승
Layered tuningSIZE_BUCKETS + STAGE3_OVERRIDES로 문제 크기별/outlier별 분리 적용
반복 동일 실패iter 7~13: 같은 문제(3e69d6f5b7) 반복 UNKNOWN
Artifact 학습 복구iter 14에서 LLM이 위험 영역 회피, 안전 영역으로 자율 회귀
+ +

20 iteration 후 best = iter 1 (combined_score=1.0000). 이 best가 phase3의 시작점으로 import.

+ +
+ Phase1 vs Phase2. Phase1은 0 → 1.0822 큰 도약 (탐색). Phase2는 0.9976 → 1.0000 미세 조정 (수렴). multi-phase cascade가 단계마다 다른 최적화 양상을 보임. +
+
+
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
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+
+ + +
+
+

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
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+
+ + + + + From 41862303c460ca282025cd6af948427e9969465e Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 28 May 2026 14:17:18 +0900 Subject: [PATCH 34/42] add phase5 custom subsolvers, worker logging, ignore cpsat run artifacts - process_parallel: pass log_file to workers and configure root logger - gitignore + untrack cpsat-bench shared run artifacts (samples, baselines, phase outputs) - add phase5_custom_subsolvers initial program - tune cpsat evolve config, samples, evaluator, worker Co-Authored-By: Claude Opus 4.7 (1M context) --- .gitignore | 9 + Dockerfile | 6 +- input/_lib/load_bench_config.py | 4 + input/cpsat-bench/Statistics/analyze.py | 35 ++ .../Statistics/analyze_outliers.py | 25 + .../Statistics/outliers_report.txt | 96 ++-- input/cpsat-bench/Statistics/outliers_top.csv | 40 +- input/cpsat-bench/Statistics/per_instance.csv | 512 +++--------------- .../Statistics/plots/01_runtime_hist.png | Bin 28111 -> 24440 bytes .../Statistics/plots/02_features_hist.png | Bin 39650 -> 31404 bytes .../plots/03_runtime_vs_size_scatter.png | Bin 134188 -> 80073 bytes .../Statistics/plots/04_box_by_status.png | Bin 27562 -> 19154 bytes .../Statistics/plots/05_box_by_hash.png | Bin 76659 -> 37438 bytes .../plots/06_heatmap_vars_constraints.png | Bin 29040 -> 27330 bytes .../Statistics/plots/07_residual_scatter.png | Bin 93573 -> 63807 bytes .../plots/08_outlier_constraint_kinds.png | Bin 32677 -> 32616 bytes input/cpsat-bench/Statistics/report.txt | 79 ++- input/cpsat-bench/evolve/build_samples.py | 102 +++- input/cpsat-bench/evolve/config.yaml | 19 +- input/cpsat-bench/evolve/extract_best.py | 4 + .../evolve/phase4_unified/initial_program.py | 63 ++- .../initial_program.py | 212 ++++++++ .../evolve/shared/_cpsat_solve_worker.py | 68 ++- input/cpsat-bench/evolve/shared/evaluator.py | 31 ++ .../evolve/shared/local_baseline.json | 492 ----------------- input/cpsat-bench/evolve/shared/outliers.json | 191 ------- .../evolve/shared/stage1_large_sample.json | 14 - .../evolve/shared/stage1_sample.json | 68 --- .../evolve/shared/stage2_sample.json | 68 --- .../evolve/shared/stage3_sample.json | 38 -- .../evolve/shared/stage4_sample.json | 128 ----- input/cpsat-bench/problems.jsonl | 512 +++--------------- input/run_phase.sh | 21 +- openevolve/controller.py | 8 + openevolve/iteration.py | 22 + openevolve/llm/claude_code.py | 30 +- openevolve/process_parallel.py | 34 +- pyproject.toml | 6 + scripts/docker-init-claude.sh | 15 + 39 files changed, 917 insertions(+), 2035 deletions(-) create mode 100644 input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py delete mode 100644 input/cpsat-bench/evolve/shared/local_baseline.json delete mode 100644 input/cpsat-bench/evolve/shared/outliers.json delete mode 100644 input/cpsat-bench/evolve/shared/stage1_large_sample.json delete mode 100644 input/cpsat-bench/evolve/shared/stage1_sample.json delete mode 100644 input/cpsat-bench/evolve/shared/stage2_sample.json delete mode 100644 input/cpsat-bench/evolve/shared/stage3_sample.json delete mode 100644 input/cpsat-bench/evolve/shared/stage4_sample.json diff --git a/.gitignore b/.gitignore index 651f8b5df1..39007b6e08 100644 --- a/.gitignore +++ b/.gitignore @@ -62,3 +62,12 @@ input/cpsat-bench/raw-data # z3-bench evolve run artifacts (locally regenerated) input/z3-bench/evolve/shared/local_baseline.json input/z3-bench/evolve/shared/phase*_best.json + +# cpsat-bench evolve run artifacts (locally regenerated) +input/cpsat-bench/evolve/shared/local_baseline.json +input/cpsat-bench/evolve/shared/outliers.json +input/cpsat-bench/evolve/shared/has_objective_cache.json +input/cpsat-bench/evolve/shared/*_sample.json +input/cpsat-bench/evolve/shared/phase*_best.json +input/cpsat-bench/evolve/shared/phase*_buckets.json +input/cpsat-bench/evolve/shared/phase*_stage3.json 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/input/_lib/load_bench_config.py b/input/_lib/load_bench_config.py index 795c54d5f0..961ddafea7 100644 --- a/input/_lib/load_bench_config.py +++ b/input/_lib/load_bench_config.py @@ -13,6 +13,9 @@ - dir: phase2_y ... unified_prepare_script: prepare_phase_unified.py # optional + unified_prepare_before_dir: phase4_unified # optional; fire prep + # 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 rebaseline_script: rebaseline_local.py # optional override @@ -94,6 +97,7 @@ def main(): _emit("PHASE_ITERS", " ".join(iters)) _emit("UNIFIED_PREPARE_SCRIPT", bench.get("unified_prepare_script")) + _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")) _emit("REBASELINE_SCRIPT", bench.get("rebaseline_script")) diff --git a/input/cpsat-bench/Statistics/analyze.py b/input/cpsat-bench/Statistics/analyze.py index fec46c2e35..d47421223a 100644 --- a/input/cpsat-bench/Statistics/analyze.py +++ b/input/cpsat-bench/Statistics/analyze.py @@ -41,6 +41,22 @@ # ---------- 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")): @@ -437,6 +453,12 @@ def main() -> int: 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(): @@ -449,6 +471,19 @@ def main() -> int: 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") diff --git a/input/cpsat-bench/Statistics/analyze_outliers.py b/input/cpsat-bench/Statistics/analyze_outliers.py index f5edd22232..bb6c777ff8 100644 --- a/input/cpsat-bench/Statistics/analyze_outliers.py +++ b/input/cpsat-bench/Statistics/analyze_outliers.py @@ -38,6 +38,17 @@ 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]: @@ -239,6 +250,11 @@ def main() -> int: 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(): @@ -249,6 +265,15 @@ def main() -> int: 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}") diff --git a/input/cpsat-bench/Statistics/outliers_report.txt b/input/cpsat-bench/Statistics/outliers_report.txt index dc39a4ecc8..81c102a19c 100644 --- a/input/cpsat-bench/Statistics/outliers_report.txt +++ b/input/cpsat-bench/Statistics/outliers_report.txt @@ -1,82 +1,52 @@ # CP-SAT runtime outlier diagnosis -records: 430, regression-kept: 430 -log10 baseline: log10(elapsed_ms) = -2.949*log10(vars) + 5.262*log10(cons) + -10.116 -R^2 = 0.855, RMSE(log10) = 0.266 -top-30 outliers slowdown range: 2.4x .. 19.8x +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 -9c28c175 fadf9341 OPTIMAL 5,307,350.0 20881 236975 267,796.7 1.30 949,858 3,779,328 -ea6187c1 fadf9341 OPTIMAL 4,689,630.0 20881 236975 267,796.7 1.24 1,180,208 4,605,661 -64e85d06 5df8f0e7 OPTIMAL 5,707,370.0 20867 246453 329,832.1 1.24 1,489,558 5,574,793 -5651d412 5df8f0e7 OPTIMAL 4,481,100.0 20867 246453 329,832.1 1.13 891,686 3,512,483 -fe2b12d0 fadf9341 OPTIMAL 3,111,920.0 20867 236494 265,473.2 1.07 632,852 2,956,918 -b14c571a 56ab92f3 OPTIMAL 1,873,900.0 20844 233581 249,521.1 0.88 738,946 2,570,390 -cdc476df 56ab92f3 OPTIMAL 1,549,180.0 20830 233076 247,184.6 0.80 443,078 1,855,860 -5f84583f fadf9341 OPTIMAL 6,436.6 1979 21981 1,027.2 0.80 436 9,745 -073ef8ba 56ab92f3 OPTIMAL 1,496,560.0 20830 233100 247,318.6 0.78 531,653 2,141,669 -7e5aa401 56ab92f3 OPTIMAL 1,482,010.0 20844 233557 249,386.2 0.77 548,234 2,019,729 -96e1d05e 56ab92f3 OPTIMAL 1,443,320.0 20844 233581 249,521.1 0.76 700,829 2,655,203 -484ac3f3 56ab92f3 OPTIMAL 1,380,120.0 20844 233557 249,386.2 0.74 498,838 1,688,437 -26f53ed6 fadf9341 OPTIMAL 856,786.0 17076 192805 163,700.3 0.72 218,233 1,168,305 -083beb02 56ab92f3 OPTIMAL 686,113.0 17058 190773 155,305.1 0.65 210,392 952,603 -bc676605 5df8f0e7 OPTIMAL 837,165.0 17070 199450 195,858.0 0.63 213,359 1,171,620 -9c548616 5df8f0e7 OPTIMAL 1,312,210.0 20853 245972 327,105.4 0.60 261,411 1,407,886 -303a6733 fadf9341 OPTIMAL 3,530.8 1850 20766 929.1 0.58 379 11,971 -d8130c88 5df8f0e7 OPTIMAL 3,748.3 1850 21650 1,157.0 0.51 531 12,139 -e2b2952c 5df8f0e7 OPTIMAL 2,894.6 1739 20421 1,021.0 0.45 229 7,101 -471adab2 5df8f0e7 OPTIMAL 3,311.7 1863 21835 1,185.3 0.45 303 10,282 -202fbcb0 5df8f0e7 OPTIMAL 3,024.1 1674 20236 1,089.0 0.44 310 10,067 -b040ac13 5df8f0e7 OPTIMAL 3,052.7 1766 20923 1,108.7 0.44 228 10,193 -c0a0dc04 fadf9341 OPTIMAL 4,920.2 2199 25921 1,792.5 0.44 712 19,587 -834873ba 5df8f0e7 OPTIMAL 3,081.6 1850 21650 1,157.0 0.43 794 13,010 -a87f850d fadf9341 OPTIMAL 180.5 715 7423 68.3 0.42 560 4,544 -c4fe2cc0 fadf9341 OPTIMAL 4,839.6 2326 26958 1,867.2 0.41 2,116 24,405 -9c6904d7 5df8f0e7 OPTIMAL 3,212.3 1977 22871 1,269.6 0.40 822 11,048 -7b9053fb 5df8f0e7 OPTIMAL 2,916.7 1859 21771 1,174.5 0.40 399 10,761 -c0bbae62 fadf9341 OPTIMAL 400,652.0 17070 192562 162,786.1 0.39 122,197 544,974 -63f1e1c6 5df8f0e7 OPTIMAL 2,893.8 1863 21835 1,185.3 0.39 382 10,557 +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 79,153.00 22,524.00 3.51 -n_cons 191,667.50 53,456.00 3.59 -n_enforce 156,156.00 42,812.00 3.65 +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 139,178.00 35,995.00 3.87 -linear_terms_max 161.00 66.00 2.44 -linear_terms_total 139,178.00 35,995.00 3.87 +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 5,499.00 1,941.00 2.83 +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 0.00 0.00 0.00 +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 166,294.50 2,016.00 82.49 -_num_branches 748,788.50 56,588.00 13.23 -_num_binary_propagations 199,726,371.50 3,507,452.00 56.94 -_num_integer_propagations 52,432,900.00 1,511,411.50 34.69 -_num_restarts 668.50 18.00 37.14 +_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 421.2 231.8 1.82 1.00 -bool_and 27,732.8 12,744.8 2.18 1.00 -bool_or 42,792.7 18,794.1 2.28 1.00 -linear 59,773.4 22,868.6 2.61 1.00 +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): - - n_vars: outlier median 79,153.0 vs baseline 22,524.0 (3.51x) - - n_cons: outlier median 191,667.5 vs baseline 53,456.0 (3.59x) - - n_enforce: outlier median 156,156.0 vs baseline 42,812.0 (3.65x) - - linear_coef_abs_sum: outlier median 139,178.0 vs baseline 35,995.0 (3.87x) - - linear_terms_max: outlier median 161.0 vs baseline 66.0 (2.44x) - - linear_terms_total: outlier median 139,178.0 vs baseline 35,995.0 (3.87x) - - wide_int_count: outlier median 5,499.0 vs baseline 1,941.0 (2.83x) - - _num_conflicts: outlier median 166,294.5 vs baseline 2,016.0 (82.49x) - - _num_branches: outlier median 748,788.5 vs baseline 56,588.0 (13.23x) - - _num_binary_propagations: outlier median 199,726,371.5 vs baseline 3,507,452.0 (56.94x) - - _num_integer_propagations: outlier median 52,432,900.0 vs baseline 1,511,411.5 (34.69x) - - _num_restarts: outlier median 668.5 vs baseline 18.0 (37.14x) + - _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 index 256fe5193b..dad4774df2 100644 --- a/input/cpsat-bench/Statistics/outliers_top.csv +++ b/input/cpsat-bench/Statistics/outliers_top.csv @@ -1,31 +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 -9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,1.2970725496824338,5307350.0,20881,236975,949858,3779328,1284803830,336295337,3742,97998,236975,194373,1.0,191,172845,2000000001.0,2.0,6913,1000000000,False,0,0,0,109273,77125,49850,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -ea6187c13380778e85ba5d23d7e056bb2abb811dea32e9675213085586a23a06,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,1.2433334003277663,4689630.0,20881,236975,1180208,4605661,1460251131,383038351,5302,97998,236975,194373,1.0,191,172845,2000000001.0,2.0,6913,1000000000,False,0,0,0,109273,77125,49850,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -64e85d066f667ad5185c60b610746320099aafce4e68f71ded08c02a10c15ec2,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,1.2381431698374108,5707370.0,20867,246453,1489558,5574793,1928527875,481837838,6126,102050,246453,203246,1.0,191,182197,2000000001.0,2.0,6913,1000000000,False,0,0,0,115431,79440,50855,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -5651d412c1f0510a7ff1b43da9e22935ec2c616c7ad6d2f15b7d8cb00930480c,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,1.133091777448728,4481100.0,20867,246453,891686,3512483,1239961259,304654974,3452,102050,246453,203246,1.0,191,182197,2000000001.0,2.0,6913,1000000000,False,0,0,0,115431,79440,50855,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -fe2b12d0f0ce68302c564ec85a5cf7640583a9cf878d8ac3d467d8cab178e90e,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,1.069007782061628,3111920.0,20867,236494,632852,2956918,774505293,240882484,3149,97811,236494,193920,1.0,191,172720,2000000001.0,2.0,6906,1000000000,False,0,0,0,109162,76936,49669,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -b14c571a562972071899d8eb08ed99fa4dfc944eedf4613632bbc2008ae90e23,OPTIMAL,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,0.8756390854867613,1873900.0,20844,233581,738946,2570390,1196466883,267712424,3140,96549,233581,190803,1.0,191,170807,2000000001.0,2.0,6890,1000000000,False,0,0,0,107019,76425,49410,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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-96e1d05ec560b176485b6da3c8924718e9a3efa0e6605dda68c85a8354cfac91,OPTIMAL,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,0.7622553039424815,1443320.0,20844,233581,700829,2655203,928474398,232999472,3060,96549,233581,190803,1.0,191,170807,2000000001.0,2.0,6890,1000000000,False,0,0,0,107019,76425,49410,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -484ac3f3201370073c6a5c037e22b9f57078489440e357c56a6d9520b4c08955,OPTIMAL,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,0.7430443637073942,1380120.0,20844,233557,498838,1688437,773107760,152021540,1585,96537,233557,190779,1.0,191,170807,2000000001.0,2.0,6890,1000000000,False,0,0,0,107019,76413,49398,727,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -26f53ed6235393586f5beac50e9c02ddb551c8112f394470fa9f3c1764d3c5b0,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.7188228191471246,856786,17076,192805,218233,1168305,240841668,68122509,977,79711,192805,157488,1.0,161,139823,2000000001.0,2.0,5505,1000000000,False,0,0,0,88116,63144,40927,618,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -083beb025bc59908b1bdace2fac047f8f204cdb3fc4f947c0af0327a928abdcf,OPTIMAL,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,0.6452098909063135,686113,17058,190773,210392,952603,271814160,67147700,801,78841,190773,155304,1.0,161,138533,2000000001.0,2.0,5493,1000000000,False,0,0,0,86709,62771,40675,618,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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-471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.44623053094988485,3311.66,1863,21835,303,10282,415999,213023,1,9208,21835,17518,1.0,26,14816,2000000001.0,2.0,773,1000000000,False,0,0,0,9198,7475,5059,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -202fbcb0eaf3a2173b0d1f86dc29d0d1f31403f91ef9ffc3b4539a004d4291d0,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.4435643369239455,3024.07,1674,20236,310,10067,340378,170355,3,8547,20236,16105,1.0,26,13143,2000000001.0,2.0,730,1000000000,False,0,0,0,8038,7111,4985,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -b040ac13f61b42c52e957df48581cfd54d36eedaad63da36839fdde9183dece6,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.43988588482448865,3052.7,1766,20923,228,10193,374340,189319,1,8812,20923,16689,1.0,26,13886,2000000001.0,2.0,725,1000000000,False,0,0,0,8527,7278,5016,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -c0a0dc04538c4ae54401b99c91b6b743811aab04bbc638360c16b4fc9b915728,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.43851503778542833,4920.17,2199,25921,712,19587,622076,306569,9,10942,25921,20513,1.0,36,16398,2000000001.0,2.0,973,1000000000,False,0,0,0,10033,9211,6545,132,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -834873ba45a488c7f8b1d6ad0d1d689ce41cb19ae9186807eabc801920b926a5,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.42543640356891776,3081.59,1850,21650,794,13010,566747,290602,6,9151,21650,17372,1.0,26,14772,2000000001.0,2.0,819,1000000000,False,0,0,0,9113,7404,5031,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -a87f850d84c25316081211e9935add586d4abd88a36e386cc59db72975479dc9,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.4217202793170176,180.457,715,7423,560,4544,30469,43812,5,3182,7423,5763,1.0,11,4723,2000000001.0,2.0,304,1000000000,False,0,0,0,2912,2643,1822,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.41361338316732565,4839.59,2326,26958,2116,24405,946049,442534,12,11385,26958,21437,1.0,36,17561,2000000001.0,2.0,1042,1000000000,False,0,0,0,10767,9444,6614,133,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -9c6904d7802ccf85e6e37fed810f6c741d114f7244b89d3d8801e90f0cf52cf4,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.403130605161544,3212.27,1977,22871,822,11048,635610,304646,6,9638,22871,18389,1.0,26,15794,2000000001.0,2.0,826,1000000000,False,0,0,0,9837,7747,5183,104,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -7b9053fb13e462eae04cb54e9e64f7929247a3ece739347a45fdc8deacd46646,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.39503502501284604,2916.72,1859,21771,399,10761,460076,229866,3,9180,21771,17453,1.0,26,14792,2000000001.0,2.0,771,1000000000,False,0,0,0,9180,7449,5039,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c,OPTIMAL,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,0.39114990000400063,400652,17070,192562,122197,544974,158611075,37718100,536,79465,192562,157008,1.0,161,139823,2000000001.0,2.0,5505,1000000000,False,0,0,0,88116,63141,40687,618,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -63f1e1c6c7452e11af1db799f7a380dabc9c9b6edb7083f59d57c4952dfdb16d,OPTIMAL,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,0.38764579685835177,2893.75,1863,21835,382,10557,472961,227459,3,9208,21835,17518,1.0,26,14816,2000000001.0,2.0,773,1000000000,False,0,0,0,9198,7475,5059,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +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 index 1ed9ea5aa7..b3a9271e97 100644 --- a/input/cpsat-bench/Statistics/per_instance.csv +++ b/input/cpsat-bench/Statistics/per_instance.csv @@ -1,431 +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 -0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6896.45,4352,2861,1491,51623,8,3843,79884,15.5139 -018944a5119d79b5bdb7d0f820725cb3e77a31d5a38971d95a209aada532a096,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1619.97,2565,1783,782,28940,8,6524,31795,3.34384 -024c5d8184d58d42edd90236a3922645eb1d0d94f1691afc6068b1d66a87ac5a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2818.52,1977,1377,600,22871,8,466,10294,1.87483 -0324fb5c1ded91eeb6343304c5b7ec103f1475463de93455f7e05db48fc927e9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3153.47,4053,2932,1121,44889,8,2310,45318,6.42205 -03290f094ddaee42319d3fce34ebe0773ab0c7b9178d512c34a7a674fa6df88b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4071.48,2199,1403,796,25969,8,10692,110220,5.38499 -053c5aa97705ce165e112c926e29a13534fbff557d9b76ed1a39c7e200ca3dbe,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1757.49,2736,1938,798,30419,8,752,13177,1.81954 -064e7f90eab7005c302ae3626c590ea2ea39dbf29fbfe2e34ce291c2df2ce620,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3432.26,2583,1793,790,30380,8,719,18056,3.00165 -06fbcfd06f4072d8d6f383d95876de2effc910f204c4919171cc0133cc4c61b9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5832.93,3243,2301,942,36071,8,6222,47499,5.09695 -072937c5444c0281a85dd5a43662dc4191d5103b014c7ff6a20acb50e48d8db2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,651.086,1856,1264,592,20575,8,326,9895,0.978946 -073ef8ba3455034f551b19285f0e25708263e1fbe2bbb1bbcdeb5bc650889151,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1496560.0,20830,16429,4401,233100,8,531653,2141669,5148.16 -07455570bdf201ce07c60133a216975122a176ca884afa8c1807cb3f4dbdf806,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2408.23,2583,1793,790,28808,8,806,17531,1.88466 -0776535f7fe277d05332ab5ee5a53406868e0bbca6f2b620ff23da961bc074a6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,76005.7,11130,8496,2634,126364,8,21705,179333,96.2732 -083beb025bc59908b1bdace2fac047f8f204cdb3fc4f947c0af0327a928abdcf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,686113,17058,13335,3723,190773,8,210392,952603,1489.62 -0976039f182f3d2a82c23ebc237b080434e5f24b43329696d42000dc1865231e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,19431.4,5306,3673,1633,62598,8,18082,110936,28.5748 -09b3336890b5addd1fa433349acab7d0176679c2f186defa993924904829995b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2863.91,3239,2301,938,35861,8,973,27904,5.14864 -0a450b93bbd0545e214c9a793193351e259a1e32771d669a198d0fba1efb72cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,791.232,1975,1377,598,21682,8,618,9748,1.17229 -0a65b64b80dcebb76c352dc449f577b4714d5ecc4a7ab1636d9e5af952bea067,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11540.4,4117,2621,1496,49739,8,3451,61663,14.9515 -0aacd1b2a9a7a61f87d99ef0728de2625def376d3645fe070371a3420c7a589a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3376.2,3920,2783,1137,44084,8,1006,39304,6.19883 -0b5a5d8fe6093b3e6c45425af340542f2a3bd34542ee682b454a49b4aa67705c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,21570,5306,3673,1633,59452,8,20491,154670,39.738 -0bee8fdc0a4660e484baf6cff49eabb8f4a2c3168c967946e6db71687d0511bc,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,15283.6,5306,3673,1633,62598,8,11744,95041,21.2578 -0c68f43a998e91f6c74281e46e857667f366541be2284fd71fc4c1ffd82996a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3163.12,2350,1548,802,28441,8,332,15805,2.86989 -0ccee639439a7f01de07d3403b825b7544b7f26135afdff0360ce08772e3dcff,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3179.9,3309,2177,1132,38578,8,592,29758,5.42069 -0cf8551fd210cf5de103cc4a8d940381b56720edb72d5d99de9b58aa2f2569ae,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8478.85,5099,3604,1495,58973,8,1828,60444,8.28154 -0f76b306846b01e1e74c68b600f2ba9fe1e5d46af433f651fa2f0ce318f7727f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1793.37,1979,1377,602,21981,8,421,9067,1.30256 -0f9d15a24c955761f758c013de78aff07607ff19742191782f36b8c2fae2168d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3133.96,3220,2288,932,36135,8,5755,41690,6.06757 -101813dbc2cd82c9225e1d67ae8811a9df8e03a45923d78f1d21c83cb40e3dfa,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5920.07,4058,2932,1126,47233,8,1675,41046,5.62571 -11aca2d6d83b06bda52d2422b0e2b6b838e96a546f38dc6ebc30ac2e06d5a4e9,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3212.64,2729,1936,793,32083,8,1944,23123,3.16658 -126d36a0c356addecc5d63023bcb7bc80dea30317e5aba3571793a5c61ebaf9c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,956.451,1766,1167,599,20139,8,688,11031,0.823945 -12e68b0b5089d173f44977d0bc3a9a85c4639b3691b74fce9d76d473e3231d38,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4031.48,4352,2861,1491,51623,8,1735,58712,8.22221 -146c661d0892f93dc45fe43d3bdf5e9dac1b905efe3b42cd4d7e009969203e29,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8340.34,5366,3525,1841,67205,8,2182,67141,7.58171 -14e56e8a3ffccdd8d7dee84a4b0c7beff6e12fcc60194b80f09f4f7d76cada03,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11915,5310,3673,1637,59718,8,9685,111280,22.8773 -14ea5ccece18b8bffb5947b963c7d0f9e5a2f1c56d1ccd70e1493be39380ba78,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4358.96,4582,3316,1266,51584,8,2446,59082,8.16986 -168f8a25314745f152b4c4d27441a8a1d4c98e6b6fef5fe601d482eab77bafd5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7334.94,5109,3610,1499,61718,8,1881,53873,7.7169 -16ddfc872fb328839f72f9bd7d6d59da1d99fd679081747c6254398c487fb1dc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,639.354,1670,1070,600,19247,8,264,9558,0.780845 -184cec13061b02fc944b3ee3d77582c469339ff6a52e0dc4a832626fd13442fd,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9272.03,5310,3673,1637,59718,8,7031,70527,16.1077 -19248a80fe2178fd62092756a8cc72ade4d6558a05e6b272bc0f5a48812762ed,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5989.49,5277,3652,1625,58767,8,2626,63032,8.33573 -19935b04b1afdc8890b51f69ff535944eee70782e95845e9762310d6aa40eec1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,579.064,1236,794,442,13884,8,305,4852,0.491991 -1a1c06a66af7443f6136b4ba851fd267ab7fff909953005391a5e81fc0a57af0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1500.26,2731,1941,790,30201,8,633,15281,2.17348 -1ad17a6edd2a5346387af674a30925c64333696d3516ead1e2523db8f70164f2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6436.84,4046,2924,1122,44899,8,1543,44055,6.42034 -1b7b53e5826b74b03f9dc2164811a7f976f22d2cfe33eafa2359179b8a91b6f0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1075.14,1860,1264,596,20792,8,472,9986,1.00964 -1bbeb46bf12047759193db1b6d07e587c7274c1c0b7623c1f146535eb7920965,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4447.05,3226,2288,938,36510,8,2759,36801,6.49673 -1bcfca2846ceffabd1af6106173e4ac515273c3cd36e3f223a7a622745a941ad,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1852.25,2325,1530,795,26394,8,560,15937,1.41769 -1c4137672b9f46074a8bea1111d80bb4049b3297d0b47be31ce79efd1df60327,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2781.61,2325,1530,795,27378,8,430,15591,2.72813 -1cbb4fb20584a2df3ff85a980e992daccf8381fb6da66657c08a35a9f6f61fe3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5391.62,3243,2301,942,37775,8,1172,28223,5.21599 -1da421e660098ad22f7cf645e4955a7d621856372d3d89ace5722c48699b9b5d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,623.536,1856,1264,592,20594,8,302,9945,0.856701 -1dbf42ee67c4b55c5a86c29d821e82932d84939a718d266e518494e0ffd44caf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2326.58,2735,1941,794,30473,8,851,12692,1.78377 -1e97b710f28cc2ce69da71bcd101dcb4bd35a9030f0a0b6d1955e36a58b0b65b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,12651.7,8580,6402,2178,94129,8,9916,105448,40.5777 -1f71e23e551c8ed60353acbeaeb177c66d93eba8dd1d4127d654ea0aa634ce54,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7400.86,5354,3525,1829,63693,8,4016,100243,16.5819 -202fbcb0eaf3a2173b0d1f86dc29d0d1f31403f91ef9ffc3b4539a004d4291d0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3024.07,1674,1070,604,20236,8,310,10067,2.45745 -21b4ac699ad2cbd802516f108e297eae44fcaa4fa6917198659e3bf8d0f2324b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1752.13,2730,1938,792,30076,8,1102,17773,2.87432 -2370931b8c81ddd151a53b0df69696c2453222fb8710bd50c12646f411267648,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,473.166,1325,878,447,14241,8,187,4573,0.369579 -25142c3ac9e62f0301845be186d7838f243977b50106dcdb7b7269608537967d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16208.8,5366,3525,1841,64265,8,4652,95995,16.4779 -256f7a0f25c2590dd389f9974a8fb93c3d0b1d4b98149a7b04e290b770a21d17,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7271.21,5298,3673,1625,59077,8,12081,81515,23.7767 -26f1613b2617a48822770702a0bbac8cc1c0384a271769c883357b944c5ebe8a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,182.409,715,461,254,7651,8,0,0,0.0310824 -26f53ed6235393586f5beac50e9c02ddb551c8112f394470fa9f3c1764d3c5b0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,856786,17076,13341,3735,192805,8,218233,1168305,1441.38 -27507e95cd8529c2ce7e9fcadb691f9c84506e44508d53f94b13bfaa503d7763,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,45439.7,8588,6402,2186,97536,8,23149,144434,64.4278 -27a639ae4f3c7b232b758f7a4b7a8193bbdf2375bc6ad9f0ed832df3259ed18c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1373.64,1860,1266,594,20658,8,419,10594,1.17965 -2915f65a6cfc4ccf2288beea5e5ebb3d1feee0493a7b8fb1b0bbe6115110f04c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10447,7837,5854,1983,85434,8,11186,96516,32.4593 -2a397e2e9ffb11e6b1bdd4bd271d454a5d960113f85ee54f45618eca730a98b2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4173.09,3243,2301,942,36127,8,843,26130,4.73902 -2a3d88962c8df3c83dccaa767c32c47d92ce214b908873271a271a052038d528,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,540.252,1316,869,447,14599,8,183,4384,0.336128 -2a6fe0572eb447f61b2b572c3baf70606f1ce4104009bbe1ecf2db6e0554834a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5430.81,3313,2177,1136,38711,8,578,29008,4.12991 -2abe830de840839cc8700da827e94f67e8231e6dbc5fdba2042dc0a5cfc022ff,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1961.23,3094,2149,945,33706,8,4561,34650,2.96945 -2b23fb6ce337942ebf3b5f99dddf477b2018725b28bccdc7e9a216501a3e1be8,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,89192,10404,7751,2653,121524,8,35393,255576,143.2 -2b46c9bcb4db1d88bc5550d8ed871f0057565ff847c85dc046ff6685df86097d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6416.71,4058,2932,1126,45101,8,2508,46309,7.0559 -2c03def2b6183b004d5bef89a2575278be78259c02e94ea24efac5475d14ed39,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1809.76,2713,1924,789,30408,8,712,13423,1.76708 -2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1361.71,1864,1266,598,20886,8,964,13872,1.28691 -2c48be2530d7932fabd485c4b3ece1fca3639c6cf6d593f0c44e0b8e56601c3b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,47175.5,11135,8504,2631,126459,8,10043,145482,56.8539 -2cf09323b6357707934b8bab13217c744b0633a6bf11f0d7b51b0961f33f9264,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4828,3104,2156,948,35170,8,844,31973,4.86196 -2cff9dca070f35f4b8632474dbe9ed80dff49bec7ad39db8f3dd1046c4d6acb4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4467.43,5073,3597,1476,57478,8,2835,59220,8.37719 -2d527c24410310a430c63e56560a9f6346660fde96829775a284645f63c86f17,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,695.523,1860,1266,594,20658,8,517,10517,1.0021 -2d8643eec9c4c62aeac7390db2bd48954a840858967aad0bbeb00b0424328b76,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11725.9,5079,3597,1482,57732,8,3735,74766,13.7156 -2e8cdd720fcb4ef0bb3bacbef8633ae847d1bca269122e5c51a98e830b9d03bf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3120.77,2350,1548,802,27313,8,418,15636,2.72092 -2ed7c12aa205b907de5d68f8daaa7fed1802f93aaad89e247b789fffd4c42d33,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16990.7,7843,5854,1989,85855,8,7633,97024,30.8048 -2f5479c6c8eed384098dac360bc532cf1934e9f1efb2b3686c5e03c680ba178d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,10429,5306,3673,1633,59452,8,7701,75544,16.0411 -303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3530.81,1850,1257,593,20766,8,379,11971,1.76994 -31ef7d4aea2e6b1e902710d43ae9fc51e98680924221e5c4362ef1ef4651eaa3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3386.23,4049,2930,1119,45238,8,4770,51971,7.21038 -321b859ee1b2e3f9874968cf86a89765dcc94f1daa4f4081f8ad231b50911dd7,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3472.19,3520,2381,1139,40984,8,3904,96041,7.69355 -327885af89164e64baa11fb9e8e4b9af8e7ab53c427d41ad23c216f3877453d6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,694.654,1325,878,447,14805,8,212,4812,0.646128 -3368a03f50cb20d1ca1ec5365507aa3096295da9a97f1688543cb9200aa911c0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,14676,8588,6402,2186,94632,8,6765,85872,24.7064 -33aca880c9efd51e3b11c9b865a5d96e10f7bf573ab2b76d1f56c929dad9b12b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,13176.2,5079,3597,1482,57732,8,6925,81417,15.7646 -34d64d3b3f09c059453e8a7eb2aa2d691cce300d5dbc94e1dd4bae24698c6a06,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,715.521,1762,1167,595,19934,8,256,10329,0.84016 -36c5b829921f254af40d517a9f1455da914471d2a17d7e9286d869735e1f7646,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3964.82,5073,3597,1476,57478,8,2828,58028,8.34405 -3707720aa7119866f34152927dba916a89b9b4980bc74daf4a4ce04c7c83a74e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,23136.9,7843,5854,1989,88415,8,10910,107959,29.2903 -379dc9168e43d8e9d0b62890fa2e276bb4107c588e6360ee40d3c7cdfb1de6f1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6175.92,5114,3610,1504,58789,8,1634,41974,7.64516 -37f84b16de74e01a63c4bdcf999c82d18ebf6d0e03406bd9ee65adb21ae54b71,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2603.74,2734,1941,793,30392,8,675,16106,1.93163 -38b4c712ec417b25f8c1fd58752402dfff72d8f346edac6d35c10d02d216bcd4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3772.51,3104,2156,948,35226,8,751,29001,3.78127 -38e83ff258692eadb3c637027ef94f7c4f1ff1790c584444cba3c4f450281b9d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,52982,11127,8496,2631,135485,8,12391,168556,65.4763 -3921ba5449869b9fca2c8b7808e99e3816492dbfbe55f3956019cbea156ed88d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6438.96,5112,3610,1502,58744,8,4341,60532,8.15447 -395b9abdb72014e256f558ea722b7df71fedf88a40e4bf0f70b34ed75d3e7ce1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1394.5,2707,1924,783,30064,8,788,15786,2.14286 -39ad61e32e7f3ecfed4123371232df4c15298efd0c20d9d3dd956c69d772db27,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3002.76,3239,2301,938,35861,8,973,27904,5.14887 -3b522f278d5967449cb3fc8dcdcf198cc3ecd7bd5f5c03cec433b4bb332433c0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2334.59,3235,2300,935,36199,8,1276,30370,4.11721 -3cbde998e188f4dfded4b435da864ce8b877c66367a6160132a29208b67f19e3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8571.61,5109,3610,1499,58459,8,3506,52995,8.33814 -3cf489848cf21fe1a27e0cdecd87845ba3e67495375dc33d4fc861f33c6039f6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,273.786,1236,794,442,13896,8,109,4450,0.275355 -3d58d5670d05e6b5f03761602fe37e31f464f8e740d8b5d658f543f2e56ca9ce,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6812.85,4589,3316,1273,54218,8,2229,58285,7.44364 -3e2b36379a2919afebb3b79a5e34bd8fd946fbc28059edd6a832ad467f412fc3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,626.761,1856,1264,592,20575,8,326,9895,0.978811 -3e69d6f5b7856ea1d07e24a00ad14594b6ba146d0685bdb57e54a8483065b9d0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1384.5,2565,1783,782,28938,8,955,19368,2.31893 -3edc7a438782c8c667a53e1e8de98b1335e55876bdc718a1196ba66d8824b9f5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2204.94,2322,1531,791,26774,8,4661,23327,3.42086 -3f9aacb091d6566f953a6f7bc9c46073305546a98e45d4e798ad1c3b2bafdff4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7769.88,4806,3325,1481,56210,8,1852,61786,8.39234 -3f9eb14b64530ab041bc300966e5f7fa1a44b578d0360f41468139f57fb9b6d8,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,701.966,1316,869,447,15389,8,0,0,0.141513 -40d5a4d59eb7467a69cb957e595b84c37998dd03e00f071ad1063e219053a924,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3245.66,4040,2924,1116,44649,8,1366,41353,6.65273 -4248a692d844a140c65947d9334b1b07f2b57b9d6ca9bbefd3d11a7bc76d1ef4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2662.56,2322,1531,791,26774,8,362,13873,2.77881 -42af128a10b50d2ba614168ff1e913357115bba1cd35e3a7e93b5bebc8ba2456,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7075.59,4054,2930,1124,45469,8,3684,73635,7.57263 -435a351b429715b8e1cd5932672f1f7a52fd843f3599ce90c7127f27a5906ab1,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3472.88,3488,2362,1126,40581,8,1390,41486,7.61202 -458c6b0e91f3e132ee1877e7eaa1d6e887bfe937db034ec7c79e5c13067e57a0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,71202.6,20862,16429,4433,236709,8,9602,121165,66.0884 -45b13b7a52849e075a4815eff8fce183160b2507b29f5f44276bd5b283cce180,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1761.78,2579,1793,786,28601,8,826,18196,2.0476 -46c0155e6d81759ee93e16aa244b6dbf0a7349c44a245165b83dfd4ef442ff9c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,32597.8,11114,8496,2618,124184,8,15039,170009,88.9703 -471adab20a3091cc680bb5711324febc0100c3a3ef98bc2c30d1f5812cff17ce,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3311.66,1863,1266,597,21835,8,303,10282,2.27321 -47888d1b56a42fa20c5c9457a9527199b1d09ec2b5528820a793234e19f5328e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3264.07,4040,2924,1116,44637,8,1980,44483,6.892 -484ac3f3201370073c6a5c037e22b9f57078489440e357c56a6d9520b4c08955,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1380120.0,20844,16436,4408,233557,8,498838,1688437,4132.21 -496c320b8ef688332fdb62ad282e05824ed7c6897f1e1782de65658d4f17aa51,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3964.92,5104,3610,1494,58090,8,2044,54259,8.29502 -49e2fc3d508cdb66bff21c054a927b03b334d0a849540dffb80c05d3fb77e18b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4810.32,3526,2381,1145,41425,8,1252,42558,7.70899 -49f5aba306220ddb56df3e237238e80ae4455ad2d41ef43856f5175dd6de48fa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8176.08,4589,3316,1273,51858,8,2546,59699,8.33407 -4a009d145752df05b177c81952c03a84a9ddae56fd662e856e212d32cd60dcee,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,445.985,1317,869,448,14660,8,107,3158,0.297181 -4b5c183b293875eb4e0b73aabd4581cbd17ba882b436d0dd49299632ccbbc661,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4210.68,4107,2621,1486,49290,8,3008,60246,8.15272 -4bec91f2bc8464588c77cbc76d4f59803f890548d8fc74c8a3346ff20c5dab37,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2137.43,2571,1783,788,29248,8,904,18832,2.15797 -4c230e355482c3652bd60bf83cf4ad77382cdb0b9abe08c27007683545e8f071,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3021.82,3722,2581,1141,42727,8,2499,49481,6.34042 -4d103dff0316dc6ba3d27838f49dd311510147101f6f497b97412b98eec9dae5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5044,2326,1531,795,28202,8,795,16032,4.34903 -4d5a013b0cfc8432770fee3b3594f7a67323a3f280da178bb34e7332743f96a2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4414.12,2326,1531,795,26958,8,364,13035,3.54087 -4dc032eadf8f2c84d493c917a5356a18eb9633a34763a134563923b85548d7ba,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3844.03,2330,1531,799,27198,8,495,12992,2.81166 -4e4bc1ad622d2addafb58df0886f20fabf9c872e18cba83057410255a6e2f356,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3107.88,3220,2288,932,36135,8,1375,31948,5.32494 -4e6c3b06b6ba4421cdf19aa4425c5ba46ca6421a9b95557cbfcf7c23f95673c8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,14327.9,8580,6402,2178,94141,8,11425,111123,46.945 -4e8c7686d89bded81fddef4fc8ea2e0b4ad22cb6b922a354bdaa316218669561,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,305.266,1313,869,444,14481,8,111,3720,0.3288 -505e49204f509b76db5e8fa0ecb2bc04befc0982ac388ed84d9f004e9d08a70a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5857.53,5114,3610,1504,58789,8,1724,50485,8.03885 -5085cb22f613e8a3f67099037e603983140390c5fcb8244df5383b667978713f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3216.8,3920,2783,1137,44096,8,1153,40430,6.91569 -5128497ce49b3c14f96b33bfecec456cd44208259435d5457c8c779c7f382bcb,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1799.35,2726,1936,790,30292,8,699,15185,2.55558 -5216fc2158f95b1730c1fd3689b0b5a0d9415f13f1dee52b6266266215e21ed9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,9137.48,7837,5854,1983,85434,8,5533,109722,24.3041 -5228181749e5d9e41dbf585a386a3b46df5239edc6c9e853f3c4bfba86708de8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4951.53,5094,3604,1490,58639,8,2474,50561,8.24999 -5248aba6397b3d34d16c24463aa8e1a7a3591f670a094a30becd777ecf915a9a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2600.23,2729,1936,793,30473,8,686,15497,2.07778 -53f9fe6243b44776260bb4960e2aab00cd17c495f0d955d618c7a7ded01a48ab,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4129.05,5104,3610,1494,58096,8,1937,57423,8.30293 -548bd4883b8031f78a5996556af64afcade4691492c8df8ed78f8df468cc652f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3091.2,3098,2149,949,33949,8,1072,27025,2.68433 -55194630a32dfef1e51e9adc4cd97e19a58f98d99e9d0668428a70b57f0539e5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4354.72,4053,2932,1121,44877,8,4442,51709,7.15852 -55dbffbe3c3057d4a4cebcd6be12812ec51a731ee21cade11d5bdc09035f083b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5903.16,3158,2012,1146,39594,8,532,30142,6.48306 -561e359c0dfd474aef398b08edbcaa429b63f38953cd73fb76d42e34595d7066,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11165.7,5099,3604,1495,61857,8,3701,72784,13.4906 -5651d412c1f0510a7ff1b43da9e22935ec2c616c7ad6d2f15b7d8cb00930480c,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4481100.0,20867,16436,4431,246453,8,891686,3512483,7757.25 -568cb19ecec5f12612462493e54e7af582f6b43606359e934281a90751fda3c7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,969.978,1739,1149,590,19373,8,248,7114,0.652094 -57b21c3f6c8f3ceb646f5ce6950949114d86bc1a225d6db5437e6f654821a6c6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4231.64,5273,3652,1621,58497,8,2224,71568,7.95616 -57b759f315f04cd49b38987b95a953459c7c29e4f53de8dd7a16f65c5057c92b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3337.57,2711,1924,787,31874,8,801,17137,3.11203 -58d58a76371ef7762bf96386ce5d2677d1bfc7d7df745d40f4a02bd30006662d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,12685.6,5076,3230,1846,64588,8,2679,103365,14.722 -597355ebc6ef3cf4559ca40bef67472ce681fc7773899b3e6d151cf06282e67c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1079.47,2323,1530,793,26307,8,1269,28923,1.56722 -5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,105.102,713,461,252,7361,8,557,4453,0.0895648 -5978abbd9b38469692be7af1f78c5fd15d0736c6192c38ba083c6ca4778659a5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3841.35,5094,3604,1490,58639,8,1311,55094,8.26568 -5b643f5787956a7cc645eb43384ea0fa8631a71c3d429bc8da67e2d26fd720a1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1158.32,1859,1264,595,20738,8,353,9988,0.909669 -5b7f113c4c253c96360a1b1a8d0556ac77d08c5de1df11b21ae6c21fd7a5b10e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10133.3,5064,3230,1834,61076,8,4204,96355,23.0398 -5b8c61f99d31fed9077ef475613e5be6648f55f2a47bcc92c0e5d0e3b41ecb94,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,19871.1,7843,5854,1989,85727,8,11530,108844,32.3834 -5c113822cd66b2d552f8963bfb44ea77942c0912a9d61f00e742058e73472939,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6474.67,4812,3325,1487,56624,8,1458,52985,8.2366 -5c6b8b896ddf71d458fd0af11ccecaa00e0217f98d21c0ac6fc53e49742d5003,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7406.27,4358,2861,1497,54463,8,3399,58637,7.80385 -5c7fbb637ba552db26f14b8bc9914ed528a7f81c6cf0d066f175b9c8709a9310,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,670.551,1670,1070,600,19247,8,264,9558,0.780983 -5d27c27284a1efabd0e614c927db18ff7ba60ac4c6d120867ae49804606d33f6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4029.71,2350,1548,802,27265,8,422,16484,2.60462 -5d3e7651fac0ff61e2af85a19b94e89acbc3264678e3d7ccf3b767afdd415a1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3438.95,3722,2581,1141,42739,8,1428,46037,7.29287 -5e03f49283bdafdd3a0f637bf19dde9b77735fd8f4a0e8cd4e8af9e3bb006e4a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6061.9,3158,2012,1146,37830,8,6561,61517,7.28667 -5e7e1a665985eea7a66215f3b479b1c945f9562025c4281a9ba00f455c187e46,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,32885.9,5076,3230,1846,61648,8,9305,123328,32.1626 -5f84583fd6d8cd9e732ba4f0c0922ce8935d1ef2820ebbd9fe01a87ec3121746,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6436.58,1979,1377,602,21981,8,436,9745,1.9513 -5fd76f37541dc792dccaecc2dc74a06253b8fe3ab9cd70a8026fb24aa6fa4fef,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3508.55,3098,2149,949,35981,8,857,24069,3.39139 -6048b517bbdeda0a3da0f391f05dfddeeab6a848e1c620d1e152821537c8d307,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4085.09,3722,2581,1141,42739,8,5788,70467,7.30942 -612b13f52e47be29ba0bc3a2be5c4e042a513765e87fb0b8676d152f8a8409f5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3910.15,4053,2932,1121,44889,8,1334,41508,6.01053 -62835d0eb69fc765c6c7a3e9536c90861584976cf492d552c8207ee2c9893abc,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11005.1,5081,3592,1489,61665,8,2742,65991,14.2197 -62c6e2f566388ddc13097f1369c9ad4569c1b4f54f888066e8ab2bfe96d3a0b4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4069.74,5273,3652,1621,58497,8,2224,71568,7.95576 -638dcb77f7553a70d99b39378c3be82213f350c928231057239ca5680b6fd284,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,518.256,1316,869,447,14603,8,222,4388,0.312387 -6396211e6842d6969cce80194013fff987eb3e308c435eccd942b5007dec4639,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3194.54,2733,1938,795,31882,8,601,16127,2.75747 -63f1e1c6c7452e11af1db799f7a380dabc9c9b6edb7083f59d57c4952dfdb16d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2893.75,1863,1266,597,21835,8,382,10557,2.23047 -643f0a85b4d3d3c13ef9cfbbbc20b1dc8f9d837a181ad2ebb84c6823e91829b5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6785.44,3727,2581,1146,43015,8,1010,38777,6.0984 -64e85d066f667ad5185c60b610746320099aafce4e68f71ded08c02a10c15ec2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5707370.0,20867,16436,4431,246453,8,1489558,5574793,10405.8 -64f19a6620a3451dd37087293c226c20f9ba01beb928e880795025bc17642ade,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1575.51,1739,1149,590,20421,8,260,7247,1.11017 -664907ffb2bdbd0cb7b5fb4c3b2c5d9c700b96a8267691ee4330a5dd2f370ead,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,60117.3,10407,7751,2656,117505,8,29456,160280,136.939 -66754667bf567d1f18152078c6781cbd453cccac416c9d06db5087e6a10c51f0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3208.92,3100,2156,944,35023,8,839,29713,4.43972 -674b484eb726690bc9d4878cb217bc6b22f30eac4f8d512be6e2713a303545dd,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3104.21,3220,2288,932,36135,8,5755,41690,6.06734 -67bbbc9a42158b6dfe51a3f52021cdb80ae1535eeeca21150ce10283948d9710,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,112.046,713,461,252,7361,8,557,4453,0.0896132 -67c6242c56a30b0610b4b4df8c9e802609c922c84515ddb24daa8a265ed05798,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1796.49,2713,1924,789,30412,8,624,12812,1.73543 -67f5fac68782f2951e196e4ae8273a46728b821ae433617aaf6d3242d36d5657,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,737.151,1240,794,446,14600,8,0,0,0.216815 -683f174a879de4bb230fe99336ee2df4ccb16d43051717a616a6bd5bfbdabfa5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6172.22,5073,3597,1476,57490,8,2870,62515,14.731 -6875434ae774ccafedbb3e729b922aa0025634c80f76d4fb5549f4901c57e8a3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2019.3,1864,1266,598,20886,8,646,10998,1.23111 -68df20b101dafbea7130c31d0faaf60971ddd458e7cfcd8f30bec787b4203df7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5416.5,4091,2956,1135,45445,8,4557,49531,6.7395 -69355a5e8d4028c466ba29d38fe021eba053515808a48e2f4895a50a0941a160,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1065.54,1860,1264,596,20822,8,365,9195,0.956687 -6ab3700646ed23d7b144c448e625ed94598db5806f3daf3cfe7a2a242cece1db,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7410.33,4806,3325,1481,58766,8,1826,58107,7.78839 -6c64b95d90a6caf1573da836e471273a2845a145a3ce07ccc4124cf7654bc226,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1883.75,2736,1938,798,30419,8,782,13357,2.07542 -6c7a1a5ce29490757a4fd01319c3af2f090f1488a9bd158edeb09a45ef95d8e0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6126.11,3226,2288,938,36510,8,1106,29478,6.1997 -6e1f2b89cfa03bff27e2308fb05c8a9e68e05dad0397cf805d949b6cf09e3064,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,324.559,1313,869,444,14485,8,373,4195,0.359571 -6e2d9a811b1dbbdf1d92a811e3fb65e4ec1a840585342b877f45d7842ec8f114,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1411.02,2730,1938,792,30064,8,619,15735,2.30004 -6ecbb5bc3a035ad7b51348fac58112aa35296e0e6ae9405119a1296fb9384045,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5580.53,5075,3592,1483,58433,8,3493,67375,15.4415 -6f3adead4b151af69acd980b16f5bd8e2ed0f0ae28c615f1c210a8e853827df5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6042,4046,2924,1122,47161,8,1848,42404,6.50539 -6f71d8acff2f8d22f7622dd943331d53409bc982b8d281f34259c40b915c9730,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3936.7,2330,1531,799,27198,8,483,13168,3.35132 -70b8dcc665126c1a6341f0c4e577dbcaefdab47475e576a7d72888677072a109,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3846.75,3098,2149,949,35981,8,1124,35050,3.48767 -7134b69950a5e555fc3439bf93781e0a9a7d79f30137214b96cf3956fb2324e6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3668.23,3150,2012,1138,37492,8,2201,73048,6.85616 -71b18e5d4bd0b04f2c9fed4d07a1cb5c359ffd3fd7034036cedb8b182b135075,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1074.74,2323,1530,793,26295,8,341,14652,1.45079 -7228b93c395db27bb2e8fc8976b27e1025c87d6f7c6463ca60078bae327b213e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4758.69,2781,1832,949,33969,8,1937,30801,4.63674 -728ea222fda0214f17e45d17dd9e9974b21d6bde8c9e88a6ac25d25a46de74cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,12843.4,5354,3525,1829,63705,8,9041,126376,31.9076 -72f2ec3d785c0a14f4295abb781aa2a7f7b023e0c82639e8bda7e1b6fb1e5bbe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5357.91,5104,3610,1494,58108,8,1981,54661,8.33971 -733d7abf574a3c54ca8522ba92c9d0de317b2ca06e621e45dec6c2673a3bd083,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1401.03,1674,1070,604,19452,8,283,8914,1.00189 -734ea7701ae361fbdb717ea3a6e7793574906a8fc6ef1353f0754dc99f858cd3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2078.5,3100,2149,951,34071,8,567,13553,1.50231 -744c046e89479d1befb90b75f08b4a15b2b687a779750b598a1e3f7572ddf98e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3569.97,4087,2956,1131,45114,8,4115,60998,6.88747 -757c9e1a076dc69239f063376288a84a499a2aabb892ed69d39fd637a7f0bd5d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9298.88,5109,3610,1499,58438,8,1320,43497,8.21765 -75ef625090c4c170b36923586cc4ba39c91471211bc12b328b231f06239be71c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3310.99,3375,2442,933,37535,8,1284,21599,3.05343 -76cda9dfd7936f002acb90d01c9ad5dd792fd17e5ccd3ca0f49a2fcc9f510244,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3072.18,2193,1403,790,25682,8,299,14001,5.90394 -78ef90514e8df4b0ad1b8b639c5cba31dc1d71011570850c043b79da490b6f35,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5812.56,5087,3592,1495,59195,8,2816,52241,8.45404 -78efbf308c9f28e54d3cad8847352b9a8b3abd79a7017e40247b96b73692c5a3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12361.7,4820,3334,1486,56476,8,8813,120664,14.9513 -7935760e9618796c7f423ed3035ff53456b7cda07a348ee8060af5cd0aeb85f4,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,14663.3,7843,5854,1989,85855,8,6845,87740,24.6145 -79b2a8894050b4767afa5d2ef42656d32485579057f443bc7e5d85c4597ef3d9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3276.46,3100,2156,944,35023,8,709,29850,5.10741 -7a78d4951c1a8b67cebf1dd62ec358ef792f9fd6000319b75cda30b8a24775e6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3195.98,2569,1783,786,30546,8,542,18144,3.25983 -7a95410b6970cc899b5df0fc6c9c83cdd9b96f7cf8d34d6dd8c6220dd5457be4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1216.52,2726,1936,790,30288,8,808,15391,2.11419 -7b4c2358a5ad5bc41a3c0dcd67791b76b50cf252d8ee23e3290bf043f327ffb2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3598.47,3488,2362,1126,40569,8,1142,39320,7.16565 -7b4e94bce09b292527422552f4ddada7ccd62e28804f75b9bc88c4d64fad00db,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2388.08,2729,1936,793,30477,8,623,15347,2.11807 -7b9053fb13e462eae04cb54e9e64f7929247a3ece739347a45fdc8deacd46646,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2916.72,1859,1264,595,21771,8,399,10761,2.09439 -7bd5e487a816e310cf226295707e1f41f4b697bc67bddaaa8c309ea93e77a727,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,154.832,715,461,254,7431,8,31,985,0.0753838 -7c9525e45522c75edc617d4accd3fa5b23a03cff1bc3a39523f13637cacf91d8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,506.153,1737,1149,588,19236,8,243,5443,0.611709 -7d5ef007c15860b85ba57ea7a09ccb08ac2d31f296bc47936d625e7af0883661,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7474.2,5298,3673,1625,59077,8,9257,85199,24.3461 -7e5aa4016ad68c0efd86e36186d0de0f5d8c69fc9d27fdd2b3976e26905325de,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1482010.0,20844,16436,4408,233557,8,548234,2019729,3843.75 -7e6b650d4435c812acac4ca343afb0734e8d22f3d1b64370345d98ece33807a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1183.34,1977,1377,600,21823,8,399,9558,0.909728 -7f37b05dbeb0f5013414a3c61b10aeb8dff6b9ab4ad520a8a9205964a26d102c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3097.71,3235,2300,935,36199,8,1276,30370,4.11745 -8073c7c85295abafcc3ca9dde8e50e775e59175274e1b7d3eec04570581b4469,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1649.08,2565,1783,782,28926,8,709,19482,2.86391 -80ac2372ce125d2b203a9d3c0111ef887a2ba0ab73248e5fd7d258f67025dfda,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6437.82,4592,3316,1276,52067,8,2738,52199,8.05118 -80f73e52da8a244ea0be2c39d85c197bc238b5af594944e77ba4baff43bee2ed,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6225.3,4117,2621,1496,49835,8,2111,75272,7.63856 -821f15ac4478cffdd0f615fe71e0eeb1c47b989f91a47f0e9e8760f5518f4868,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1855.33,2775,1832,943,32281,8,557,24512,3.10161 -82c981cebdc67d969363e30496d5e1278592f85aca9bffae6fb8135aade2369c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4100.31,4049,2930,1119,45237,8,2629,43878,6.32821 -82edf01d7c20302d494f05b2f335473d3535b086de4f8bb170f707cbdc06a7f5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,9249.91,5277,3652,1625,58663,8,1749,67067,8.18564 -832bd4924ac7dec74dd3b8e9a14a0e380cac3ca9c041a6c9d765d6dba454895f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,104633,10404,7751,2653,117200,8,38734,248491,176.807 -834873ba45a488c7f8b1d6ad0d1d689ce41cb19ae9186807eabc801920b926a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3081.59,1850,1257,593,21650,8,794,13010,2.23403 -84327bfe5801100327e40bc478403a949d8947fd6a15ddcc2bcb0f06ce32eea6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5728.96,3224,2288,936,36358,8,927,30205,5.90036 -855c207bbe508dd6bc5c8e557648607de72dbe98e732548d00a14f1692fa30e9,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3036.42,2733,1938,795,31882,8,777,15687,3.01651 -859f76e784d7572cac7dbbf5a6b8358da4120fd0da76af247da8bdbe91f209ba,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,795.193,1739,1149,590,19405,8,255,5415,0.635959 -867ec278968f0337e74a78c449201e06acb674774dde6d598377e9a123d1d6cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6078.24,4825,3334,1491,56887,8,1766,61829,8.15386 -86c2f0304e765fa13c3e8d0c970082ec0f321665e56f4caeb7505d1a652b1958,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,24569.5,11114,8496,2618,124184,8,5451,107151,32.4269 -86c635d438e9048aeb8352c427f1b36966fa53086f50512d36c0f265d893155e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6348.77,3524,2381,1143,41227,8,6997,74326,7.5385 -87106998551ed8b22ec13067c8b3a05d47ccb12637e779a1040a9def4e9765b6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2555.9,3098,2149,949,33949,8,833,22856,2.43486 -87a7d382f1e5cd88801c156daf84fd0739c090390db57bd7622d86f7775dbb48,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6350.34,3727,2581,1146,43015,8,1791,41316,6.90988 -89264583d12fec785e020bbb3d0c29872c17db7a2aea98f0c936e42d568a8eb9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5541.49,3224,2288,936,36358,8,1466,31284,5.09327 -8a25ce239e8b745b34e8418960573b61c84d9f9af96681e7f5b54725b86b6bfe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5309.81,5075,3592,1483,58445,8,2382,66426,14.7412 -8a3a0575a6af4727c9e27c2e63a5da1f147282a3478b1278fd9bc8069e8110b1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6624.34,4054,2930,1124,45468,8,1748,41177,5.8758 -8b1db26428e486f04d080ee1a5589c05a5a1251c112b8d2af9dfb6d55ae0dcf4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4787.76,3224,2288,936,38028,8,2472,37155,4.98498 -8b264feb93eae5932741a7f9e9db294c22ea3cb66c4957c56c11a257c0e84e2b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3475.49,3220,2288,932,36135,8,1375,31948,5.32517 -8b439dc017e4e505dd41661ef4684654990664c10d073efcb6cbd7f77d2e1f75,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3044.89,2729,1936,793,32079,8,901,16035,3.18278 -8bf3c36029cc57273c55537c51f36e4d506c54f004db71af04c2f3833513a6ea,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3910.28,5094,3604,1490,58627,8,1354,54858,8.30722 -8d6e8152f2a7acab40571eb94f4ab02bf8dcd2f246844a3366ade55f64647001,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4449.32,3104,2156,948,36382,8,1352,35025,4.71866 -8e4e16238b7a5266c68e8a99bb41c18f3f2b3864ab9b439c946c2d99eeee0364,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7342.41,5064,3230,1834,61088,8,4363,91953,15.6115 -8edd162afaa39a55870723c221d056c251e1846b40ab5ac7042388639fbbca3a,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11819.5,5079,3597,1482,59796,8,3578,76643,13.8424 -8f16d86d9b158570c045a8f7cf0c19b66d812d400677574b99f8f2270c9c7e3f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1669.89,3094,2149,945,33706,8,892,22010,2.7846 -8f41589af6050547301acab498a4f5b7894920b712156a7e2fac1664966dd04e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1599.16,2730,1938,792,30064,8,1148,16541,2.64948 -8f61a3cb2dd7e6d2b832e5a18ca464a3f6e93ae0072c5251e2d4f7582069d539,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3057.05,4053,2932,1121,44877,8,2050,42750,6.67817 -8f967cbd48541a625efc6f9e641373a0015889f904fe744dc6d5e7061a7a36fe,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1979.49,2325,1530,795,26442,8,581,16233,1.67653 -90e1185ded530d8fbb2b773076a2fdbdfbb9a725f2fc07b4d8b8280e93069b54,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1323.68,1847,1257,590,20644,8,3410,21289,1.66283 -90e16615aa9873fcba0b9ffeaec5584d26877834691f2b3011096e29281d26ec,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5772.7,4800,3325,1475,55906,8,5705,120717,14.8884 -915f8918d0b3d7d39a423af66871046decda0ae1e8230bbdeb43f12a08e79fc7,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1537.86,2579,1793,786,28589,8,876,16280,2.10021 -91dee20f1695cde5ba284269b5a1cddd4253c16a5e56639578ebbb8855c2c2fa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5312.01,4440,3147,1293,50667,8,1717,52956,7.43839 -94fbb6eb1148ca15aff1945c9b987c281b8ac300531a6c4ed88840629233828f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2360.01,2585,1793,792,28922,8,912,15157,1.93857 -9507e3b4ced4efa84827b4febcf8c4bf480ec914bc957af7ebe337c2a25fa887,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7026.39,4440,3147,1293,50587,8,1692,52454,7.8701 -95dffeb21af47c0da4d0cd31a1d8f2a02c049546ee31d53568c3c87ec796b9b7,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6499.23,3924,2783,1141,45864,8,1678,45114,6.78036 -96e1d05ec560b176485b6da3c8924718e9a3efa0e6605dda68c85a8354cfac91,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1443320.0,20844,16436,4408,233581,8,700829,2655203,4843.77 -97c67ce44962a7b1afacbdf7610259b634c74cf7953cf2b5766bfccf3e347b13,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3578.34,4434,3147,1287,50329,8,1997,53648,7.28891 -9801d683d790b79a89846abab1f9539026a1ca565629939ff874f65922709702,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4600.21,4050,2924,1126,45133,8,2345,44779,6.52967 -99406389a054df8283649e3669fd2a07fa770b3472003d54b6331d59527a15d1,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,422.549,1240,794,446,14036,8,103,4068,0.26473 -99c5ac6d121d8b750f0bddfa010f32222a75f7d3c2235a87cc7883a876e3d654,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,798.026,1975,1377,598,21682,8,377,9553,1.18037 -99db0e67a00812cd88e5186525387f7f146332da1adcc35d7218f6d13b32b927,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6204.55,5104,3604,1500,59384,8,1650,51769,8.33136 -99dd837b2fb977cecca2cd812b39804c750269ea4317fa660ded1bfd10361217,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,28200.8,7843,5854,1989,88415,8,13095,128740,35.9572 -9b68c85c1a117037be6720b3f7a9f0c2b040b41c9b725525fb162df7f3a2910c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7909.24,4358,2861,1497,51975,8,969,45703,7.91396 -9be78e91ac11a52ea9632536e893507ce0081319840e6ec750614f774115ce96,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2300.62,3100,2149,951,34071,8,684,14103,1.8593 -9c28c175d2cd7871aaff707f1cdbb5132941625137f09fd93fa2116fc9b3470e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5307350.0,20881,16450,4431,236975,8,949858,3779328,10022.4 -9c2be28afd9a6d476a647468dccac7801a653e7be6684ac128de72030efcb3f5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1161.53,1859,1264,595,20727,8,305,10055,0.839053 -9c3c55cd54e6074a7e2732ccfaac2e90698141e7e9fb30c946657a21fb186061,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4022.2,5104,3610,1494,58096,8,1777,58426,8.31302 -9c5486163986ecf6d1316c2ad9ab5a154f3847a1b7e01d23f2ad6251435b68dd,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1312210.0,20853,16429,4424,245972,8,261411,1407886,2214.53 -9c6904d7802ccf85e6e37fed810f6c741d114f7244b89d3d8801e90f0cf52cf4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3212.27,1977,1377,600,22871,8,822,11048,2.12357 -9c801bcf5dc6a4fa11be76710e268871e68ba55da83399f00fb9e0390f675962,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3098.94,2569,1783,786,29118,8,1617,21084,2.66028 -9d6512b86417f81a043a4abfa11ada9209f4e3e59b8b56f8f2e234e823c89eab,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4094.81,4049,2930,1119,45225,8,3832,89975,7.82779 -9e71a16c2592f15f94948241daaa4468f4f47ad791aa02a3751e03e97a2d377d,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1034.56,1766,1167,599,20107,8,351,10858,0.75633 -9e867e2a560f391fe00509c9a263a7b25e7beaadcc151da54b616a068bff4d1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7404.83,5304,3831,1473,59864,8,1468,61490,14.4768 -9f1faefa3b6d0fde8da0886706df654164a7ba690fa4c6c337401c865e233cfe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3171.96,3520,2381,1139,40996,8,1193,38351,6.84478 -9f330e3d7528eea46bbfbd640e4dc0f7d732bf1fdc1dd9e39c323406b9cede01,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3351.44,4087,2956,1131,45102,8,1767,38543,6.44192 -9faddb505594f68b886e5540640f408e1d511fb47d3fde017a7466621b7d5d8d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11228.7,4358,2861,1497,54463,8,5127,73239,13.756 -9fef160ec7a392c9691777eb5c599e48956eed4b519eb0ef968c6da06a547db3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,640.309,1856,1264,592,20564,8,316,9914,0.898999 -a04dbde35f5eda88bf42a75eaf0d43d9b0665177cfa971469f3e5e9fb8225fd0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5754.89,4057,2930,1127,45699,8,1585,40818,6.47583 -a0bbbe5321ee24c3dbf69f223c4c0e8a0f62a2dfbf1e58c2f9414e2255a0122f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,850.031,1975,1377,598,21682,8,618,9748,1.17243 -a0cd1b349e85c374dac7fd4570d5dc90928422dea74e38afe67defa14d8a13a5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6657.63,3224,2288,936,38028,8,7110,91485,6.30523 -a1a2584786c08975b608e6ba0d2d576dc3993ca614b721341a5bfd21f2c4bdac,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3956.08,4352,2861,1491,51635,8,670,40595,7.45242 -a2471fdf2d809899bf51327d180164d2523cb5bc14222327fda360f2f6677b7b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1979.36,2346,1548,798,27043,8,334,15496,2.57625 -a27f90201b577988626385f9254e8b786910326e6e8fb7b6cb1544a744743db8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3366.15,3150,2012,1138,37480,8,1494,43537,7.2959 -a2a435c469f2205b3c9cca2d78b3efb8ea3542308e986ea66d8774b36dcc3f99,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,316.487,1323,878,445,14094,8,166,4721,0.38152 -a2dfa72e3403f1488e1d679e3d8bf35a040fbfb8b52101a438626dfe9587aa58,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2377.49,2711,1924,787,30268,8,1266,30734,1.95116 -a325be32cc0287d707ddde2c5556e868387fda094272991cfe5cd1998091f5a9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5134.63,3158,2012,1146,37902,8,433,24742,6.26043 -a34a2f667fa7daad838285edacb2c038301c3bd073c6d83a9290a6cce2364723,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11295.1,5366,3525,1841,64385,8,2482,78855,16.0043 -a3865ae1eb5fb989347e0391f653d399c19e0050af1fcb79594dc0659bf374f0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6495.42,3924,2783,1141,44292,8,3579,74959,7.06788 -a49d307cfcf3ca9dff031a54cca31086a1882b7c0e65c1da60de651a8c9a79b8,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10574.9,7837,5854,1983,85434,8,10534,98410,32.0729 -a4a7c4b150b49a6cf24302d9fd052b821060e59abd5de1589a34c32c368dfefb,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1840,1977,1377,600,21823,8,455,9769,1.15662 -a4ce40350fd94acc8dfa277dc1bcc0cc82a3a600ee96d7a5ca1342cc7a2cc18d,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3986.44,5073,3597,1476,57490,8,2761,59642,8.21734 -a4dbf4239260ca8770d6c3f876a98ba92dd42102400396cf211e7f9696e4776c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1500.8,2707,1924,783,30068,8,695,16497,2.08018 -a87f850d84c25316081211e9935add586d4abd88a36e386cc59db72975479dc9,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,180.457,715,461,254,7423,8,560,4544,0.0901002 -a8a5fdd87f0511775828ec72413b40065d20587aa2603638713d72f05bc543a2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3134.13,2711,1924,787,31870,8,740,15627,3.02381 -aa6fca326d5509b8593566dd692566d7aae1391f75b3ae9b3f5f2c09409f27c5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4676.26,2781,1832,949,32565,8,707,25709,3.30281 -ab06f38ddf16d391ca4564bd92864ba0d062b576927c24a606894d3b2ba5324f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3487.46,2571,1783,788,29262,8,6635,36704,2.86413 -ab0dc15b9cb918a6625a4f314188fadbf7851cbb0fb04d8237c821251021e07f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,42433.4,10391,7751,2640,116641,8,35646,377527,153.145 -ab4c547d9c6b55b6ba9d99b755dc01399f1a9eca8a3c1a53ec603955459bbb01,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6536.66,3727,2581,1146,45243,8,878,33840,6.79979 -ab65fced530e53ab8e8820b1566667bcc263f0a4616f8fcf0119dca9c2d6adfa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2168.04,1851,1257,594,20861,8,414,11107,1.5309 -abb5959746b98f2e3ca3e372ea9123d269d907763eeaa8974b931b9fe9066d9c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,10397.7,5298,3673,1625,59077,8,17088,157373,39.3144 -abff5e6c75b21e295633c7d3920862fb20a7ed5a81d3b6c3d7adefe375a2331f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3515.33,4434,3147,1287,50329,8,1781,51076,7.59026 -aca45a10435494d79a4838efee7c3d968577b0426bebae02d39d196e4cf95d20,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3671.42,4815,3334,1481,56186,8,2414,53301,8.1817 -ad2944e678573fd35127a68e3818c4049d6c7f50988ef9031ad9f2c5669a9ef5,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2371.29,1859,1264,595,21741,8,305,9961,1.70667 -ad5b37c3d671388ea80e099a9ee23706555adb1f1768f6393b9643e07c2a141c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5988.72,4364,2861,1503,52449,8,1772,55986,8.07535 -ad6577c2c75004d6adc5e7753d29ccebfb79bf175f4476e183c69c606b5091a6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,590.302,1325,878,447,14217,8,139,4506,0.34056 -aec821f70ed62b536c07791cb8cb7cc48661194d53fe27e8b65b139c13aec39a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1317.35,2726,1936,790,30276,8,759,14872,2.31358 -af7f7518658f6a121da81b663950506b43c7657af03a0b3e3e51e058163a472f,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,748.329,1975,1377,598,21682,8,377,9553,1.18051 -afaba40d5fa8903139f80aa365c6ab850a2df54221f9be87383281c552513466,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1811.58,2732,1936,796,30680,8,612,14213,2.1616 -afb6bce329d577a5f52f98b748d2157c1bbc23473bc701cd560fb2b57d85a791,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,309.44,1323,878,445,14106,8,151,4649,0.378169 -afb7051a1f3226c7a23f4d22f224bd36ff41ca896c8e3be24c3d2d7c9da2ffcf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2133.87,2732,1936,796,30684,8,1314,16008,2.20437 -afe82892cb3a54c1449823bb460e1a9e59967e18729277426693d180e0125266,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,21205.3,7843,5854,1989,85727,8,11110,111181,31.961 -b040ac13f61b42c52e957df48581cfd54d36eedaad63da36839fdde9183dece6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3052.7,1766,1167,599,20923,8,228,10193,2.02794 -b13bb2432694ba5fe9768abc03525698986ba13ffe70a7112b90bd7fbad02aec,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,8842.37,5079,3597,1482,57828,8,2133,60233,12.7949 -b14c571a562972071899d8eb08ed99fa4dfc944eedf4613632bbc2008ae90e23,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1873900.0,20844,16436,4408,233581,8,738946,2570390,6668.87 -b18203536680dcbad9276ec9d8db50f6ef463cbcbbea5aaeabff959652209e3c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,6300.32,5104,3610,1494,58078,8,3809,110454,15.1082 -b1994f8885fd57c17b4817fa3515dcbf21b49b1bcf65f915336b3a9fe858e651,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5483.02,4061,2932,1129,45302,8,4083,46562,6.52935 -b1b2b8caa3156c8273d827504518306416d1e2909060667e4af05787fc173a38,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,5984.27,4800,3325,1475,55918,8,7904,99296,16.3801 -b24ab4139208bdc598f3f70510ac9f58334d0af4bcce109a96f1dd48133ae1e3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,959.743,1739,1149,590,19373,8,308,7215,0.627408 -b290214fc63e3dcbf5bb5f263ebdec94aaa0ef84fcfa33180675b6346565b5b7,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,28277.1,8588,6402,2186,94488,8,11791,108949,39.9897 -b43c4de7b33424859986111645c0f3535ce55c97d536fdfc7eda147261d92ad3,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,9668,5304,3831,1473,59852,8,5829,81909,15.8384 -b4543584e1dea20783dd43c0d72808ddc8596c59707ffb1c7383a0b926b22a5e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6886.08,4058,2932,1126,45101,8,3426,74733,6.61894 -b5699531416576bc6f180919ba95643a25b1287458d36123f55eb76d8d5b47d2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1965.09,3094,2149,945,33706,8,4561,34650,2.96967 -b6027761fc923f4b03b977ca7530948fa7351cd7d5ffe1f0eff7e3b62577325e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3431.8,4049,2930,1119,45226,8,4101,48128,7.43628 -b72a77b55f9675d08f39dfdae6b58ce09d723623c71d67141407f794f2f5792a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1001.91,1860,1264,596,20803,8,744,12584,1.03077 -b7e392cb6562f741b79089dd527327fb8437eb6fb609701512465727cec3acd0,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4531.41,2326,1531,795,28202,8,2030,22093,5.07546 -b91d4698bc045a1f72fa7423165efe4caac0ebee02edb3eed9f3846a1bc01bba,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7481.13,4054,2930,1124,47567,8,6808,70130,7.20177 -b9c06d6c62aab9e07e4ff671bd0cfb111de0618a95c82783a2facf40d681bcdd,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3654.93,4352,2861,1491,51635,8,1666,60664,8.20979 -ba0b9ea5e8f2328cb61bd31c0d61dafaec59744031371bbf7d594553e5b1c8c0,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1315.97,1863,1266,597,20821,8,408,10424,1.05133 -ba3c3ec1af3ebbaee88b5d85beac349a3d26be07ff2aab68e6ae3790c8c47f3f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3264.35,2569,1783,786,29104,8,884,19554,2.58996 -ba86b8aa2d73b9780ab98d8557143022a9c326ee7dddeebc7e1572fe12abd770,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,66907.9,20876,16436,4440,237190,8,9936,121156,69.7478 -bac1fb9016d7061a81abe52a4479744cd8c8b9b890d0d2df020f2eed60a814e8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12741,5081,3592,1489,58781,8,8190,64546,15.7151 -baf98ab4178a2d5e0ed95a96d12df90ea8f4ddd6fd76e3cb74521afdc686a725,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4895.28,4582,3316,1266,51584,8,2446,59082,8.1702 -bb5648a91b469ef23f1155a766b8e6fd05b14bcf42940e511f7c88aefbc8ec47,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3262.15,2569,1783,786,30560,8,2070,30975,3.92594 -bb9eb5259a99d62dd29494b30eecaa36ff1f6fe5698d4793423bef4137a7b1a9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1017.39,1847,1257,590,20644,8,1010,13357,1.26922 -bbcf7fef3811556bc56b16655a1a8c18732092a7bd82e0c47313134504ed991f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,INFEASIBLE,68275,20876,16436,4440,237190,8,11901,119272,73.1088 -bbcfb05efc5b22e60cea2c2aadf94a452df9e1d1a80e743926b755526773e3cb,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,11690.6,5109,3610,1499,58438,8,6207,130018,13.9314 -bbdd48b171ac2a86a8e9c8dc148b5301278cde418f992a4d2efe2d41378c31aa,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6444.66,3494,2362,1132,40817,8,1164,39616,6.93193 -bc676605802ed5fb85e5006a7df234667d32237dd086e56b2a8bd20a578155e4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,837165,17070,13335,3735,199450,8,213359,1171620,1353.18 -bcaed7c25140d0d5731f397b188a271512394f14fabe67b495076b23436e1d57,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,502.374,1737,1149,588,19236,8,243,5443,0.611565 -bd4be90c6f160c8932088ffb1f966190355a060720c71113a8784e00d8c2cd57,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4816.56,3924,2783,1141,44364,8,1919,42736,6.62824 -bea0526af2a0ef3f3fc16f4b0daa81f8c077307921ed0cbc02b78a60d008a2a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,10744.1,5076,3230,1846,61768,8,3721,100111,16.4477 -bef168aea12ee9d5a0b12880cb3c69ebc80ff968b9f2500a907c5f3f16f0d369,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,7530.89,5298,3673,1625,59077,8,8859,83041,23.9536 -bf95b4aa258e822d108e65d7ce10e99104db1720fb2fe84d3b5f105ff9f2af39,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1132.06,1860,1266,594,20658,8,419,10594,1.17979 -c07c5abd63a1f29258c3989976e428c4e3195ed80d0c8e7c145fba613d394abe,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,11003.3,8580,6402,2178,94141,8,9050,97946,32.2612 -c0a0dc04538c4ae54401b99c91b6b743811aab04bbc638360c16b4fc9b915728,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4920.17,2199,1403,796,25921,8,712,19587,4.70544 -c0bbae62f14d03166e20cbff5fabab7917480464a9baf804ec42409b26e0d32c,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,400652,17070,13335,3735,192562,8,122197,544974,817.73 -c198fac818f333764f4b5d0b08c72cae3b903978b4a6e19b138902d969dbb86b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,1988.07,1859,1264,595,21752,8,372,10428,1.6284 -c1e1e1a8dc3fcef074f8642dc2d3db8001a120f65a6263335206fafe704fb56a,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2431.35,2733,1938,795,30242,8,646,15490,2.17952 -c299a3f6d8e558df79357fcea15b8092424ca74e1cd3664e2dc128b62980c065,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,304.492,1313,869,444,14493,8,163,3749,0.333838 -c330aa34330c3723403b96c7b9cbc4ad2b656ef04275965f8e2996e1b57a46a2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2670.68,2733,1938,795,30242,8,590,15794,2.03907 -c407b925a91b430515ea23a95e35cb5075023a6b3f81624547928adfac80ea30,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7808.57,5099,3604,1495,61857,8,2283,62251,7.84759 -c4fe2cc04ae7a35679d2f3e71e91d1476eaaa48ed0e0eea02642695224d1a3a5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4839.59,2326,1531,795,26958,8,2116,24405,4.23301 -c50ae7a1597320837a67154b00c457247005405a0d61ae03b18207f42c3d2be5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,633.046,1856,1264,592,20594,8,302,9945,0.856833 -c636f4a25733401ac79f10e9031c1eb15798423352413076b31a5be0f9dd1ef8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,33804.5,8588,6402,2186,94488,8,18283,134769,56.5736 -c640cc60784b5f00e395f07384f65c540c7f59d3766fc098f1c34f8645b0e4ae,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,4313.07,2199,1403,796,27097,8,307,13962,5.09543 -c6a6b2aab48ad0047d3e14c25d630fb1e22e3844608af935b5e481c1965a8010,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3883.39,5094,3604,1490,58627,8,2010,62433,8.22645 -c74e506b46863a2dc803d33e1af1b9c9f6e286bbe1e710d9cdfaa284b1c6f7db,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7692.09,5099,3604,1495,58973,8,1835,59762,8.13808 -c867ab59144791c88799ce95599bbd25090dec398595de1c1e35d01b8a7f06ec,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1564.42,2707,1924,783,30080,8,951,16490,2.14116 -c8c8858fad7755abe96b96b28bcbb93492fb84c5213e56797044f8ec51fbdf8a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,8928.37,7837,5854,1983,85434,8,6789,90678,24.054 -c8f846435e761ae39d9095f91dec1ff5daba0e88fbb838766d52cc6d9469aaec,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1152.52,1674,1070,604,19420,8,172,8896,0.839951 -ca043f0850c14f2d99c0a48b1a959387505b2facf3551fa053ddb9c68bfa58b5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3101.11,3313,2177,1136,38783,8,493,26836,3.2243 -caa6c275ceb224cc3efdf55f2c42a0d803b5ce6afe66cff65abaf8a50980b9cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,7255.7,4358,2861,1497,51975,8,3095,59900,8.11072 -cb3ba9f21e52b233f5241e5003b1e388d1d1ffec427094d421a66c28df2f292e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,642.02,1762,1167,595,19934,8,256,10329,0.840026 -ccfac7f93fb9565b6c623253e6a722369a005a8479fe7e6d3112942ed39d274f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5546.81,4091,2956,1135,47437,8,2328,42380,6.34357 -cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,987.922,1316,869,447,15385,8,183,4481,0.725161 -cdc476dfd87c9b11d9a46dc7e8aeaa0f3efa9708cac0093587abb35f1228ffe0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1549180.0,20830,16429,4401,233076,8,443078,1855860,4027.99 -cea92f2d552c17f09f2927186ad61dba3f09e3405f0a58a1007d0769d2334c75,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6403.38,4091,2956,1135,45373,8,1734,40697,6.78591 -cf629228e69ab5f293c8c30459175cccac150e85ec84539bfd8d5dc27540cf1a,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2972.04,3369,2442,927,37121,8,1320,25163,4.09745 -cf93115a0bf38977ba3f8e8bc9ea9378389b50f513ebde8f4d5660d6158ee1ad,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2736.12,2711,1924,787,30264,8,931,17958,1.97305 -cfb91dee56a02f4826b9ee3cdda17de95706a33d8d5e844946a2316281641585,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,377.333,1313,869,444,14497,8,1116,25101,0.418748 -cfe62c4328920f71a44857714728227a3db0ab8e2b1535406ef09b4b6c841719,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,636.406,1856,1264,592,20564,8,316,9914,0.898867 -d000926320c493a1b1062c0f4f5e690e07761f43709e4362c20c25558d5cb175,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1605.55,2346,1548,798,27031,8,335,15513,2.20673 -d011c7fba6d3d11fa56d0962808f364fe43c519590a2c0b0c5d4b9d4000c971f,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,15126.3,8588,6402,2186,94632,8,6826,86123,24.2877 -d08d3590e759b910bf4e5c31c39dd187ad6ca894a5da5c26f0e2343ffe9223c5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1772.15,2730,1938,792,30076,8,1612,16281,2.54555 -d0cb084bf73c6a8bf5cd88cfb595b6f89b05a25e54817adec872eb5b699f83c6,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3731.05,4815,3334,1481,56174,8,1819,62821,8.34385 -d1b2c9992efda91256f54b5da850272632e2cd7a46cab2a4e81f74cedf156620,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5355.17,3313,2177,1136,40187,8,937,35168,4.77691 -d28acbdfd6c0f8dc93e215ce1a2fc55b369d63cd6b94efdbfaad6045b6f882ea,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,974.173,1847,1257,590,20644,8,1010,13357,1.26936 -d2bf66ca178e3511de17fd1d1baee9bdf66ca21ecd831f227837ce9271dd99b4,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,241828,17058,13335,3723,190797,8,113751,616288,753.539 -d3d1ba1cf734596936c45e703ae6c602900ea669015657296757813ef56804c2,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7758.26,4820,3334,1486,59032,8,2512,61449,7.84112 -d573733a5742e214054d9dcb2da9184aab7fbeb1af376b55fafb32a18244db01,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5402.52,3732,2581,1151,43342,8,2651,84649,7.43684 -d6ac13401de0afd9760e8f8853fc4fa523bc083e017c97628ace4cc6213b7630,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,2636.93,2781,1832,949,32621,8,591,23984,2.88591 -d6dade98259be38e7313f9777ef831cea9573770df96f4f369515d2dfdf64892,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4844.67,3496,2362,1134,41015,8,897,33449,6.95599 -d75c887fb9536d1adc4dd996a2c83002aafd4a33c57da7f3f9ae4b1375acc563,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6416.26,5104,3604,1500,59384,8,1981,59854,8.47782 -d7df934f3b3ba0e8254473ce06758550361bdc3f1491b9f8d9053d63822f60cf,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1764.03,2731,1941,790,30189,8,4158,57953,2.6842 -d7e09c73e7c387ce90ed94c06031780e15353ec9e09ac3a8d2ce6cb1af0dc6f9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,688.182,1860,1266,594,20658,8,517,10517,1.00223 -d7f5127fbe51e0b6301e5092d44dacf3947138e0a6d52aa0820e05e552dcf8d6,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3980.14,3372,2442,930,39222,8,1168,27661,4.24827 -d8130c88625b627297a53f53c8e762e3364a3a80df8f3c156308af5f39bfd89d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3748.33,1850,1257,593,21650,8,531,12139,2.30902 -da0d5cddd84af0e7503d69de955621895f5e4ed02463ce7b994926416ee15e90,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4490.47,4107,2621,1486,49278,8,1914,57190,8.23222 -da292d01044cc894931fcfb089158d65a1cd8e38a789c2f6c73be0fd81859ae3,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11693.6,5310,3831,1479,63086,8,2652,73629,14.342 -daa215241452279f6bd5d569a96d188cea9dd668a82c7db3f1463c7b4122bf78,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1287.87,1851,1257,594,20861,8,325,10876,1.2279 -dad4ea991056ab6474c77d2983ea583b7475e7b62144be92e9414819af6c453b,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,3551.33,2734,1941,793,31998,8,803,16588,3.23431 -db7603182e6c01908b60a5198cc807e5d50446b92f36df74879d8cff59ededf9,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1895.54,2565,1783,782,28952,8,3683,32229,2.78948 -e08d820df1d9743f539badc0bc1e61665bee76c052dd71840fb75c3567894d1b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,4168.07,5104,3610,1494,58108,8,1594,55705,8.38265 -e0dee47cd630ceac71b5c13970dc2c1445ba7a9a780d4c8dc47ea48a94a76d13,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3910.49,3372,2442,930,37320,8,1120,26394,3.00747 -e0f3c7a20537704888438728a07497f4807978fb801fb82031069eca0220188b,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3140.42,3309,2177,1132,38566,8,5606,75969,5.024 -e272c0e4899bd231855a0205e36e42663a9208b44d081e1149dc37bdafe3a33d,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11336.5,5109,3610,1499,61671,8,3325,65832,13.2456 -e2b2952cbec18758e829524bf6dbc3429f6a22fa6d02d67a9a769b04e568acda,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,2894.64,1739,1149,590,20421,8,229,7101,1.82151 -e2bbfb9a6518269bb6c17eb4174bdb2c455f86b24df806b2a70d3efc3cc2559b,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,16580.1,5310,3831,1479,60202,8,8747,86424,22.3038 -e2cb022ee2177db3e3a7d0013600ebdd0e836478a0de3e772bf2e43758f48b4f,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6297.97,3494,2362,1132,42717,8,797,35952,6.38374 -e3f05dc6a31d4730a876f0feb1b4b2e907503a838b62458292ca91a970a07b98,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2054.76,2322,1531,791,26786,8,500,14190,2.89887 -e5061a18e1dccc8890efc254baf8645d165384227e9b8a2a184fa92c8a56b751,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,12804.9,5312,3831,1481,60364,8,5457,68944,14.0536 -e5089b40043a2bd13c56d857868c30d39802c527a9f88960040cf46ad486d533,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5253.12,3240,2300,940,36452,8,2043,31440,4.28569 -e56d749a21bc8a639822cf600ca2b22078c1a5352377f0b84164691f615d3915,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4887.83,4057,2930,1127,45640,8,2631,43623,7.24441 -e5c9e667fbee60ca8a589904e1a7afc30c81394efd58e388a9f63470fa4ea406,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6608.39,4054,2930,1124,47566,8,1763,39458,6.43063 -e66de9b63db44a62c14bf4e342fb5e7c7c704ed8af07cafdc0b0bae1575561d2,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1117.04,1859,1264,595,20757,8,286,9923,0.817957 -e70a9ef2b4f8aaa2b009cfae1b6ce00546089bbb5aa32842f75da342b97b5c62,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7970.52,5079,3597,1482,59796,8,2467,65020,7.82318 -e848603289ccaac1aabdf4923a6c533d552120e0deb2e5a7dcc5575e4fbf4d15,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1251.32,2726,1936,790,30280,8,733,15802,2.15822 -e8dab90c05bca526c4ca4dacc22f9973f240d5a80273896fcca6fa45ea919bb8,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,916.404,1739,1149,590,19405,8,271,5748,0.718591 -ea6187c13380778e85ba5d23d7e056bb2abb811dea32e9675213085586a23a06,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4689630.0,20881,16450,4431,236975,8,1180208,4605661,9645.78 -eae6008a7290df6b48bd01fe9114fe7c8405b73d714b28dd55a48088f412303e,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,2575.79,2193,1403,790,25694,8,255,13536,4.3558 -ec107dc4aa7dbb1386df6da2db1f5b4dd192245085e5beee12265c82c9381edc,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3548.75,3369,2442,927,37121,8,1320,25163,4.09768 -ec4b042c8f9a198d1b01cca74aa2bcd5acc11f565478ec964f0ba812e5f5f5c5,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4618.5,3732,2581,1151,43342,8,2166,52761,6.90447 -ed81feb815fdb34cb44257fc30a6d9f24de2ed8fffcec1c868977a4d70d4395c,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,26603,10391,7751,2640,116641,8,16842,255182,88.5344 -ed822dccbbfc8c02c2e04bd547202571cb79271a808d730d8742d31920e58213,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1608.48,3094,2149,945,33706,8,892,22010,2.78436 -ed8349c414d687ac9dd6fc5f0f2406149f5e340dbddbb010a78b646aa1dff047,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,3318.12,3722,2581,1141,42727,8,4463,93544,7.18218 -ed8b6e9582c28720d48b5839465e62d1837c0324ea9725c4d8af71b5718e8e81,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,32383.5,8588,6402,2186,97536,8,15627,122298,43.2025 -ed9224086383052b0c3c6e5d47f8be3b109682bde7c4f0606548d115a458c204,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4562.37,4061,2932,1129,45302,8,1402,41147,5.86141 -ee7841a529956537bd53bd0d06c598113f2c32871c93e105a3967b75e9f64199,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7673.95,5109,3610,1499,61718,8,2756,52996,7.88379 -eef410430ef1cdcadce5480be841daef9bd294a8d1a7a1912d559e62eee37fb3,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1380.36,1850,1257,593,20766,8,497,11760,1.03943 -ef01a35c4b42507472b9855a2a470b8020f570bb1518eb15334873fe917bf877,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1974.88,2775,1832,943,32281,8,557,24512,3.10185 -ef97c0560bd7e6e8d095f4f4c12bd07e9f93e382ba24f038963f8e1d82c9e5c5,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1667.51,2322,1531,791,26786,8,494,13933,2.20254 -f2899679b09262dca05f75c6fe70f8eca2a6daeb2bd59727c37c1b6658e24802,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,14595.1,8580,6402,2178,94129,8,13022,121753,48.0783 -f37d6966a7d23940fbb6746e26270b8f66f95534c8caf0eda6d1a692b7f03694,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6340.31,3524,2381,1143,43127,8,876,36477,6.44549 -f38c4b16a01010ad99c30db049af26efc7fa7366d244e2d8b09c59fb44bc51f6,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,6043.44,4364,2861,1503,52449,8,1677,58474,8.10386 -f44f61b4bcf0f5938c1b2e6436886c89427480ea10dc0835eb7889990b9aa4ea,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,1400.94,1863,1266,597,20821,8,365,10412,0.933528 -f5931bc8ee19786a4ade31e56bdc19d415c4dbfa42da07785765944fe6748326,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5490.98,3727,2581,1146,45243,8,1721,44355,6.24261 -f7041abae337b502aa03618b1c0f40cc693acb9c0bd48efe21b41c42a7849bef,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,5229.07,3240,2300,940,38286,8,1162,27920,5.07577 -f77cdd0764d8aa490133369275f4d62d71f47133f6102d90caa9a799c69ddc63,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,6273.65,4058,2932,1126,47233,8,2166,42479,6.69212 -f78fe695ba9e97a63ab5ce95b254e44fa166fac58e2775b3ddf94f49ea7903cf,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,4646.35,3241,2300,941,36541,8,1577,27157,4.01477 -f7bef5557bfa7900b2878f94c5cb50ef62b55ae98590c3348883692be9e0fe3e,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,11713.1,5277,3652,1625,60763,8,2608,83027,11.1826 -f990df9b39aa8360f64fe986746d28d6ce1c06c5498b465aa979d3ad19016ee0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1523.17,2707,1924,783,30076,8,677,16212,2.42093 -f9e4e9cdb24462a34649e1e803328a7ce8332623aa743a6197d0c69af13b02d2,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,668.915,1737,1149,588,19236,8,296,6114,0.713344 -fa619c63ddf9ad9612bc3a615ca437be781fa0014734de2b854bf496453301ac,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,1364.16,1847,1257,590,20644,8,3410,21289,1.66269 -fbec9f8410da7935d158884bbe39a6d2ec9ea9a9cc81656e0df797d8a91227b0,56ab92f31c63b342e524772e52db2f544c639d6e4bcc0ce661f062ed31738024,OPTIMAL,576.826,1737,1149,588,19236,8,296,6114,0.713486 -fc3b0f50b62cc1d9992a8e57b9ec42ca803833d372c275e62594a21cd3424008,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,522.676,1317,869,448,14656,8,146,3462,0.308244 -fdd2abfa77414b11517b8142ddf00539bd7f46bcf54d45a1c4c63054c4ad6702,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,436.964,1240,794,446,14012,8,87,4299,0.24253 -fe2b12d0f0ce68302c564ec85a5cf7640583a9cf878d8ac3d467d8cab178e90e,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,3111920.0,20867,16443,4424,236494,8,632852,2956918,6244.86 -fe91eb0bb912529d421e5a02bf6ae7806d24d2ed126a32040f3805cf9880ffa4,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,7010.29,4440,3147,1293,52387,8,2009,55718,6.805 -ff6697b1a9d57aeb43f770c91918c33c2b88b2ed07ceea158d351ecb9e4fe8ab,5df8f0e757df0723112604075d205601310374d953777709be4c1ef611f244bd,OPTIMAL,8016.85,4117,2621,1496,52091,8,2704,66347,7.69068 -ffb713809ac8c43a2c5b9cf1de5f841511eab9db95cd9bd4ac5653992f9b2b14,fadf9341e734c7807e8d86051974b880b382afc02505113658a5a3965eda0f3c,OPTIMAL,5931.36,5079,3597,1482,57828,8,1982,61712,8.19457 +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 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with_obj.append(p.name[:-len(".cpsat.pb")]) + cache_path.write_text(json.dumps( + {"pb_count": pb_count, "with_objective": with_obj}, indent=2) + "\n") + print(f"objective scan: {len(with_obj)}/{pb_count} problems have objective" + f" (cached at {cache_path.relative_to(_BENCH.parent)})") + return set(with_obj) + + def _runtime_key(d): return (d.get("cpsat_status") or {}).get("elapsed_ms", 0) @@ -380,11 +425,37 @@ def _write_sample(path, picks, label, criteria): def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument( + "--optimize-only", + action="store_true", + help="Filter the sample pool to problems whose CpModelProto carries " + "an objective (drops pure-feasibility models). Default off — " + "keep all problems.", + ) + args = ap.parse_args() + rows = _scan_raw() if not rows: raise SystemExit(f"no *.meta.jsonl found under {_RAW}") print(f"scanned {len(rows)} problems") + if args.optimize_only: + obj_shas = _has_objective_set() + if obj_shas is None: + print("warning: --optimize-only requested but ortools unavailable " + "— keeping all problems", file=sys.stderr) + else: + before = len(rows) + rows = [d for d in rows if _id_key(d) in obj_shas] + print(f"objective filter: kept {len(rows)}/{before} problems " + f"(dropped feasibility-only)") + if not rows: + raise SystemExit("no problems with objective — nothing to sample") + else: + print("objective filter: OFF (use --optimize-only to drop " + "feasibility-only problems)") + with open(_PROBLEMS, "w") as f: for d in rows: f.write(json.dumps(d) + "\n") @@ -471,12 +542,21 @@ def _bucket_range(b): f"{int(_runtime_key(d)):>7}ms") # ---- large profile sample (OPENEVOLVE_PROFILE=large) ---- - # Single sample file with STAGE1_LARGE_N top-residual outliers (default 1). - # evaluator dispatches every cascade stage entry point to one outlier-set - # evaluation, so no stage2/3/4 split is needed. + # Single sample file with STAGE1_LARGE_N outliers, sorted by + # STAGE1_LARGE_SORT_BY ("elapsed_ms" → slowest baseline first; "residual" + # → highest log10 deviation first). evaluator dispatches every cascade + # stage entry point to one outlier-set evaluation, so no stage2/3/4 split. if csv_all: eligible = [c for c in csv_all if c["sha"] in rows_by_sha] - # csv_all already sorted by descending residual in _load_outliers_top. + if STAGE1_LARGE_SORT_BY == "elapsed_ms": + eligible.sort(key=lambda c: -c["elapsed_ms"]) + elif STAGE1_LARGE_SORT_BY == "residual": + eligible.sort(key=lambda c: -c["residual"]) + else: + raise ValueError( + f"unknown STAGE1_LARGE_SORT_BY: {STAGE1_LARGE_SORT_BY!r} " + f"(expected 'elapsed_ms' or 'residual')" + ) large_picks = eligible[:STAGE1_LARGE_N] large_rows = [rows_by_sha[c["sha"]] for c in large_picks] else: @@ -484,8 +564,8 @@ def _bucket_range(b): large_rows = [] _write_sample( _STAGE1_LARGE, large_rows, "stage1_large", - f"top-{STAGE1_LARGE_N} residual outlier(s) from outliers_top.csv " - f"(W=8 tuning set)", + f"top-{STAGE1_LARGE_N} outlier(s) from outliers_top.csv " + f"sorted by {STAGE1_LARGE_SORT_BY} (W=8 tuning set)", ) # Clean up stale empty stage{2,3,4}_large files from older builds — they diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index 3d61bb5291..f1bd22660a 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -15,7 +15,14 @@ bench: iters: 40 - dir: phase4_unified iters: 40 + - dir: phase5_custom_subsolvers + iters: 40 unified_prepare_script: prepare_phase_unified.py + # The unified-prepare script materializes phase4_unified from phase{1,2,3} + # winners. phase4 is no longer the last phase (phase5 is), so the prep must + # fire before phase4 specifically, not "before the last phase". When unset, + # run_phase.sh falls back to the legacy "before last phase" trigger. + 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" # optional script-name overrides (defaults shown): @@ -36,7 +43,7 @@ parallel_solvers: 8 max_iterations: 40 checkpoint_interval: 10 -log_level: "INFO" +log_level: "DEBUG" random_seed: 42 diff_based_evolution: true @@ -374,8 +381,14 @@ database: similarity_threshold: 0.95 evaluator: - timeout: 1800 # 30 min wall cap per variant — stage4 worst case - # ≈ 20 problems × 120s × 1.3 / parallel_solvers. + 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) diff --git a/input/cpsat-bench/evolve/extract_best.py b/input/cpsat-bench/evolve/extract_best.py index a7ad7446b1..033b8c9056 100644 --- a/input/cpsat-bench/evolve/extract_best.py +++ b/input/cpsat-bench/evolve/extract_best.py @@ -17,6 +17,10 @@ 1: "phase1_search", 2: "phase2_presolve", 3: "phase3_lp_cuts", + # phase4 is no longer terminal (phase5 follows it), so it gets extracted to + # phase4_best.json / phase4_buckets.json / phase4_stage3.json for phase5 to + # inherit. phase5 (custom subsolvers) is the terminal phase — no extract. + 4: "phase4_unified", } if __name__ == "__main__": diff --git a/input/cpsat-bench/evolve/phase4_unified/initial_program.py b/input/cpsat-bench/evolve/phase4_unified/initial_program.py index 34bfd99c65..17708d4ae1 100644 --- a/input/cpsat-bench/evolve/phase4_unified/initial_program.py +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -35,13 +35,66 @@ # EVOLVE-BLOCK-START -GLOBAL_OVERRIDES = {} +# Auto-generated by prepare_phase_unified.py from union of prior phase winners. +GLOBAL_OVERRIDES = {'clause_cleanup_period': 20000, + 'cp_model_probing_level': 2, + 'cut_cleanup_target': 2000, + 'cut_level': 1, + 'diversify_lns_params': True, + 'interleave_search': True, + 'max_consecutive_inactive_count': 150, + 'max_num_cuts': 6000, + 'mip_check_precision': 1e-06, + 'mip_drop_tolerance': 1e-07, + 'mip_max_bound': 10000000.0, + 'mip_var_scaling': 1, + 'probing_num_combinations_limit': 40000, + 'root_lp_iterations': 3500, + 'share_glue_clauses': True, + 'share_glue_clauses_dtime': 0.4, + 'solution_pool_size': 8, + 'stop_after_first_solution': False, + 'symmetry_level': 3, + 'use_feasibility_jump': False, + 'use_feasibility_pump': False, + 'use_lns_only': False} SIZE_BUCKETS = [ - (50_000, {}), - (150_000, {}), - (float("inf"), {}), + (50000, {'add_mir_cuts': True, + 'add_zero_half_cuts': True, + 'cp_model_probing_level': 3, + 'cut_level': 2, + 'linearization_level': 2, + 'max_num_cuts': 8000, + 'merge_at_most_one_work_limit': 500000000.0, + 'presolve_inclusion_work_limit': 300000000, + 'use_feasibility_jump': False, + 'use_feasibility_pump': True}), + (150000, {'cp_model_probing_level': 2, 'cut_level': 1, 'use_feasibility_jump': False}), + (float('inf'), {'add_mir_cuts': True, + 'add_zero_half_cuts': True, + 'cp_model_probing_level': 1, + 'cut_level': 2, + 'linearization_level': 2, + 'max_num_cuts': 8000, + 'root_lp_iterations': 5000, + 'use_feasibility_jump': True, + 'use_feasibility_pump': True}), ] -STAGE3_OVERRIDES = {} +STAGE3_OVERRIDES = {'add_mir_cuts': True, + 'add_zero_half_cuts': True, + 'cp_model_probing_level': 3, + 'cut_level': 2, + 'extra_subsolvers': ['lb_tree_search'], + 'hint_conflict_limit': 50, + 'inprocessing_dtime_ratio': 0.25, + 'linearization_level': 2, + 'max_num_cuts': 10000, + 'num_violation_ls': 3, + 'optimize_with_lb_tree_search': True, + 'presolve_bve_threshold': 1000, + 'repair_hint': True, + 'root_lp_iterations': 6000, + 'symmetry_level': 2} # EVOLVE-BLOCK-END 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..2d7358d03f --- /dev/null +++ b/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py @@ -0,0 +1,212 @@ +""" +Phase 5: add CUSTOM SUBSOLVERS to the CP-SAT portfolio. + +Reference: shared/cpsat_params_reference.md + (any SatParameters field may be set INSIDE a custom subsolver's `params`; + invalid names surface as `invalid_param: subsolver_params.` and + zero the score.) + +WHY a dedicated phase for this +------------------------------ +CP-SAT solves a model with a PORTFOLIO of subsolvers, each with a slightly +different configuration (e.g. different linearization_level). You may add your +OWN subsolver with a specific configuration via `subsolver_params` (a named +SatParameters set) referenced from `extra_subsolvers`. + +CRITICAL — do NOT tune top-level parameters here. Top-level params apply to +EVERY subsolver, including the LNS workers. A very expensive propagation +technique enabled top-level would also fire inside LNS workers and make LNS so +slow it becomes useless, unbalancing the whole portfolio. You can also create a +default subsolver incompatible with the model: e.g. an objective-requiring +config on an objective-less model makes CP-SAT drop most/all subsolvers from +the portfolio, leaving the solver inefficient or non-functional. + +The safe pattern is a SINGLE extra subsolver carrying the expensive technique: + - if the technique does NOT help → only that one worker is slow; the rest of + the portfolio is untouched. + - if it DOES help → that worker shares its solutions and variable bounds with + the others, lifting whole-portfolio performance. + +This is exactly the packing-problem case: a high-cost propagation can speed up +search dramatically when isolated to one worker, but would cripple search if +forced on everyone. + +Inheritance +----------- +Inherits the phase4 unified winner (shared/phase4_best.json + phase4_buckets + +phase4_stage3) as the top-level configuration and does NOT modify it. Phase 5 +ONLY appends custom subsolvers on top. num_search_workers is pinned to +PHASE5_WORKERS (8) so the added subsolvers actually get worker slots; portfolio +sharing requires interleave_search, which is locked True. + +Evolve surface (EVOLVE-BLOCK) +----------------------------- + CUSTOM_SUBSOLVERS — applied to every problem + STAGE3_CUSTOM_SUBSOLVERS — added ONLY for stage3 outliers (long-tail / hard) + +Each entry is a dict: + { + "name": "unique_subsolver_name", # required, unique string + "params": { ...SatParameters fields }, # the isolated configuration + "needs_objective": False, # optional; if True, skipped on + # objective-less models (avoids + # CP-SAT dropping the portfolio) + "min_constraints": 0, # optional inclusive lower gate + "max_constraints": None, # optional exclusive upper gate + } +Only put the expensive/experimental knob(s) in `params`. Keep it to one +technique per subsolver so its effect is attributable. + +Do NOT modify locked keys (random_seed, num_search_workers, interleave_search). +""" +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 + + +PHASE5_WORKERS = 8 + +PHASE_LOCKED = { + "random_seed": 0, + "num_search_workers": PHASE5_WORKERS, + # Portfolio determinism + the cross-worker sharing that makes an isolated + # custom subsolver pay off (it broadcasts solutions / variable bounds). + "interleave_search": True, +} + + +def _load_prev_dict(name): + p = _SHARED / name + if p.exists(): + return json.loads(p.read_text()) + return {} + + +def _load_prev_buckets(name): + p = _SHARED / 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] + + +def _load_objective_shas(): + """SHAs whose model carries an objective (build_samples cache). Used to skip + objective-requiring custom subsolvers on feasibility-only models.""" + p = _SHARED / "has_objective_cache.json" + if not p.exists(): + return None # unknown → treat every problem as having an objective + try: + data = json.loads(p.read_text()) + return set(data.get("with_objective") or []) + except (json.JSONDecodeError, OSError): + return None + + +# Inherit the consolidated phase4 unified winner as the top-level config. +_PHASE4 = _load_prev_dict("phase4_best.json") +_PHASE4_BUCKETS = _load_prev_buckets("phase4_buckets.json") +_PHASE4_STAGE3 = _load_prev_dict("phase4_stage3.json") +_OBJECTIVE_SHAS = _load_objective_shas() + + +# EVOLVE-BLOCK-START +# Custom subsolvers added to the portfolio. Start empty; the LLM introduces +# one isolated expensive technique at a time. See module docstring for schema. +# Example (commented — DO NOT enable blindly): +# { +# "name": "max_lp_heavy", +# "params": {"linearization_level": 2, "add_mir_cuts": True, +# "max_num_cuts": 12000, "cut_level": 2}, +# "min_constraints": 50000, +# }, +CUSTOM_SUBSOLVERS = [] + +# Added on top of CUSTOM_SUBSOLVERS only for stage3 outliers. +STAGE3_CUSTOM_SUBSOLVERS = [] +# EVOLVE-BLOCK-END + + +def _problem_has_objective(problem): + if problem is None: + return True + if _OBJECTIVE_SHAS is None: + return True + sha = problem.get("sha") + if sha is None: + # fall back to the recorded baseline objective when sha is absent + 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 buckets in (_PHASE4_BUCKETS or [],): + for upper, override in 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): + """Append custom subsolvers to the portfolio WITHOUT touching top-level + params. Each spec → one entry in subsolver_params + one name in + extra_subsolvers (deduped against any inherited names).""" + if not specs: + return + sub_entries = [] + new_names = [] + for spec in specs: + name = spec["name"] + entry = {"name": name} + entry.update(spec.get("params") or {}) + sub_entries.append(entry) + new_names.append(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) # inherited top-level config — left untouched below + 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(_PHASE4_STAGE3) + + _apply_custom_subsolvers(p, _collect_specs(problem, stage)) + + p.update(PHASE_LOCKED) # re-enforce phase lock last + return p diff --git a/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py index 3c33eacda2..c651fbb693 100644 --- a/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py +++ b/input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py @@ -60,19 +60,25 @@ def main(): 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.python.cp_model import: {e}"}) + "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. - try: - solver.parameters.max_time_in_seconds = float(timeout_s) - except Exception: - pass + 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 @@ -86,11 +92,56 @@ def main(): if k in _DROP: continue try: - field = getattr(solver.parameters, k) + 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[:] @@ -102,7 +153,7 @@ def main(): return else: try: - setattr(solver.parameters, k, v) + setattr(params_proto, k, v) except AttributeError as e: emit({"invalid_param": k, "error": str(e), "result": "Unknown", "elapsed_ms": 0}) @@ -113,6 +164,9 @@ def main(): "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: diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 99fd5c815c..9abc934899 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -27,12 +27,15 @@ import importlib.util import inspect import json +import logging import math import os import pathlib import sys import traceback +logger = logging.getLogger(__name__) + TIMEOUT_FACTOR = 1.3 MIN_TIMEOUT_S = 5 @@ -266,6 +269,24 @@ def _evaluate(program_path, problems, stage_name): # Per-phase lock (worker count varies by phase) overrides the global lock. phase_locked = getattr(program, "PHASE_LOCKED", None) locked = phase_locked if isinstance(phase_locked, dict) else LOCKED + + # DEBUG: surface the resolved global params delta vs BASELINE so the run + # log shows exactly which knobs the LLM mutated this iteration. + if logger.isEnabledFor(logging.DEBUG): + global_delta = { + k: params[k] for k in params + if k not in locked and params.get(k) != BASELINE.get(k) + } + logger.debug( + f"[{stage_name}] resolved_global_delta_vs_baseline={global_delta} " + f"program={pathlib.Path(program_path).name} " + f"phase_locked={dict(locked) if isinstance(locked, dict) else locked}" + ) + for attr in ("GLOBAL_OVERRIDES", "SIZE_BUCKETS", "STAGE3_OVERRIDES"): + val = getattr(program, attr, None) + if val: + logger.debug(f"[{stage_name}] {attr}={val}") + violations = {k: params.get(k) for k in locked if params.get(k) != locked[k]} if violations: return _err_result( @@ -355,6 +376,16 @@ def _solve(idx_p): per_params[k] = v if "num_search_workers" in params: per_params["num_search_workers"] = params["num_search_workers"] + if logger.isEnabledFor(logging.DEBUG): + per_delta = {k: per_params[k] for k in per_params + if per_params.get(k) != params.get(k)} + if per_delta: + logger.debug( + f"[{stage_name}] sha={p['sha'][:10]} " + f"num_constraints={p['num_constraints']} " + f"is_outlier={p['is_outlier']} " + f"per_problem_delta_vs_global={per_delta}" + ) core = _core_pool.get() try: r = run_cpsat(input_path, per_params, timeout_s, diff --git a/input/cpsat-bench/evolve/shared/local_baseline.json b/input/cpsat-bench/evolve/shared/local_baseline.json deleted file mode 100644 index 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diff --git a/input/run_phase.sh b/input/run_phase.sh index 3ff51707eb..daeba666cc 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -327,17 +327,28 @@ run_one_phase() { return 0 fi - # Unified-prep on the last phase - if [ "$phase" -eq "$LAST_PHASE" ] && [ -n "${UNIFIED_PREPARE_SCRIPT:-}" ]; then + # Unified-prep: materialize a phase's EVOLVE-BLOCK from prior-phase winners. + # Fires before the configured UNIFIED_PREPARE_BEFORE_DIR (so a later phase + # can still follow the unified phase); falls back to the last phase when the + # key is unset (legacy benches with the unified phase terminal). + local prep_here=0 + if [ -n "${UNIFIED_PREPARE_SCRIPT:-}" ]; then + if [ -n "${UNIFIED_PREPARE_BEFORE_DIR:-}" ]; then + [ "$dir" = "$UNIFIED_PREPARE_BEFORE_DIR" ] && prep_here=1 + elif [ "$phase" -eq "$LAST_PHASE" ]; then + prep_here=1 + fi + fi + if [ "$prep_here" = "1" ]; then local missing=0 - for ((i = 1; i < LAST_PHASE; i++)); do + for ((i = 1; i < phase; i++)); do if [ ! -f "$ROOT/shared/phase${i}_best.json" ]; then - echo "phase $LAST_PHASE requires shared/phase${i}_best.json (run phase $i first)" >&2 + echo "phase $phase ($dir) requires shared/phase${i}_best.json (run phase $i first)" >&2 missing=1 fi done [ "$missing" = "1" ] && return 1 - echo "[run_phase] materializing phase$LAST_PHASE EVOLVE-BLOCK via $UNIFIED_PREPARE_SCRIPT..." + echo "[run_phase] materializing $dir EVOLVE-BLOCK via $UNIFIED_PREPARE_SCRIPT..." python "$ROOT/$UNIFIED_PREPARE_SCRIPT" fi 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/claude_code.py b/openevolve/llm/claude_code.py index 7a07912892..391ec891c6 100644 --- a/openevolve/llm/claude_code.py +++ b/openevolve/llm/claude_code.py @@ -106,6 +106,10 @@ async def _query_once( TextBlock, query, ) + try: + from claude_agent_sdk import ThinkingBlock + except ImportError: + ThinkingBlock = None # older SDK opts_kwargs: Dict[str, Any] = { "max_turns": 4, @@ -126,11 +130,28 @@ async def _query_once( opts_kwargs["cli_path"] = self.cli_path if self.cwd is not None: opts_kwargs["cwd"] = self.cwd + # When reasoning is requested (effort or max_thinking_tokens), default + # to adaptive thinking with `display=summarized` so ThinkingBlock + # actually carries text — Opus 4.7+ defaults display to "omitted" + # (signature-only), which would zero-out our DEBUG reasoning log. + # User can still override via claude_code_options.thinking. + if (effort is not None or self.max_thinking_tokens is not None) \ + and "thinking" not in self.extra_sdk_options: + 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"max_thinking_tokens={opts_kwargs.get('max_thinking_tokens')}" + ) + text_chunks: List[str] = [] + thinking_chunks: List[str] = [] result_text: Optional[str] = None async for msg in query(prompt=prompt_text, options=options): @@ -138,11 +159,18 @@ async def _query_once( 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 final = result_text if result_text else "".join(text_chunks) - logger.debug(f"[claude_code] response chars={len(final)}") + 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 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 a36690f4a3..db8c8fd1d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,12 @@ dev = [ ] 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] diff --git a/scripts/docker-init-claude.sh b/scripts/docker-init-claude.sh index 9fa871517e..17adfab14d 100755 --- a/scripts/docker-init-claude.sh +++ b/scripts/docker-init-claude.sh @@ -64,6 +64,21 @@ else 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" From 4dc2be8c2c444cb6664e7f3a76cff82c5ff37c09 Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 28 May 2026 18:25:07 +0900 Subject: [PATCH 35/42] [docs] update slides --- docs/openevolve-intro/index.html | 200 +++++++++++------------- docs/openevolve-intro/slides.html | 246 +++++++++++++++--------------- 2 files changed, 208 insertions(+), 238 deletions(-) diff --git a/docs/openevolve-intro/index.html b/docs/openevolve-intro/index.html index 104fdc8d7c..f2054cbe58 100644 --- a/docs/openevolve-intro/index.html +++ b/docs/openevolve-intro/index.html @@ -31,7 +31,7 @@

목차

  • 한 iteration의 생애
  • LLM이 어떻게 적용되나
  • Z3 파라미터 최적화 — 실제 결과
    • -
  • 실전 예시: CP-SAT phase2 진화 로그 읽기
    • +
  • 실전 예시: CP-SAT 3-Phase 진화 로그 읽기
  • repo 활용: z3-bench / cpsat-bench
  • 새 솔버에 적용하려면
  • 용어 미니 사전
    • @@ -707,164 +707,142 @@

    6. Z3 파라미터 최적화 — 실제 결과

    -

    7. 실전 예시 — CP-SAT phase2 진화 로그 읽기

    +

    7. 실전 예시 — CP-SAT 3-Phase 진화 로그 읽기

    - 여기까지가 이론. 이제 실제 로그를 따라가며 LLM이 어떻게 CP-SAT 파라미터를 튜닝하는지 본다. 로그 출처: input/cpsat-bench/evolve/phase2_presolve/openevolve_output/logs/openevolve_20260527_154008.log. Phase2는 phase1의 best 결과를 물려받아 presolve/probing 표면을 미세 조정하는 단계. + 여기까지가 이론. 이제 실제 로그를 따라가며 LLM이 어떻게 CP-SAT 파라미터를 튜닝하는지, 그리고 phase 체이닝이 이득과 regression을 동시에 전달하는 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 실행 환경

    +

    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 seed42 (재현성 확보)
    Island3개
    총 iteration20
    고정(LOCKED) 파라미터random_seed=0, num_search_workers=1 — LLM이 못 건드림
    샘플 문제stage1=10개, stage2=10개, stage3=outlier (cascade 평가)
    Phase 위치phase1 best 상속 → presolve 표면 튜닝
    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(20)
    Phase 체이닝phaseN best → phase(N+1) initial_program.py로 상속
    -

    7.2 시작점 — 이미 높은 baseline (score 0.9976)

    +

    7.2 Phase 1 — search / subsolvers: 큰 도약 (0.71 → 1.46)

    - Phase2의 초기 initial_program.py는 phase1에서 검증된 minimum 설정만 들고 있다. Phase1이 다양한 search 옵션을 시도한 뒤 살아남은 핵심 파라미터: + Phase1은 가장 큰 표면(search 전략 + subsolver 조합)을 단독으로 탐색한다. 초기 config는 탐색이 무거워 deterministic_time은 느리지만 branch/conflict 수는 적다(efficiency 2.25).

    - 
    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 1,
-}
+  
    initial: combined_score=0.7123  geomean_speedup=0.5436  efficiency=2.2518  (solved 10/10)
+winner : combined_score=1.4582  geomean_speedup=1.1786  efficiency=1.8948  (solved 20/20)
+         🌟 new best @ iter 1, 4, 31
    
 
-SIZE_BUCKETS = [
-    (50_000,         {}),
-    (150_000,        {}),
-    (float("inf"),   {}),
-]
+  
    winner가 채택한 핵심 — feasibility 휴리스틱:
    
+  
    GLOBAL_OVERRIDES = { "use_feasibility_pump": True }   # 작은/중간 문제 dtime 단축
+SIZE_BUCKETS[large] = { "use_feasibility_jump": True, "use_feasibility_pump": True }
+STAGE3_OVERRIDES = { "repair_hint": True, "hint_conflict_limit": 50 }
    
 
-STAGE3_OVERRIDES = {}
    +
    + 결과. deterministic_time 17.9% 단축 (geomean_speedup 1.1786), stage4까지 통과해 20/20 OPTIMAL. 큰 탐색 공간이라 점수가 0.71 → 1.46으로 크게 도약. (LLM 생성 timeout 10회 — claude_code 3-retry 후 skip.) +
    +
    + 복선. winner가 켠 use_feasibility_jump/pump가 phase2 initial_program.py로 상속되며 곧바로 문제를 일으킨다. +
    -

    평가 결과:

    - 
    combined_score = 0.9976
-geomean_speedup = 1.0409          ← deterministic_time 4.1% 단축
-geomean_wall_speedup = 1.2351     ← wall time 23.5% 빠름
-solved_rate = 1.0 (10/10 OPTIMAL)
-efficiency = 0.8802               ← 일부 문제에서 conflicts 약간 증가 (88점)
    +

    7.3 Phase 2 시작 — 상속이 깨졌다 (score 0.0000)

    +

    + Phase2(presolve/probing)는 phase1 winner를 상속한다. 그런데 초기 평가가 0점. phase1에서 켠 use_feasibility_jump=True가 num_search_workers=1 환경에서 사실상 유일 solver로 돌면서 한 문제에서 feasibility를 잃었다. +

    + 
    initial c69923ed: combined_score=0.0000  solved_rate=0.0000
+artifact.result_mismatch = {
+    "sha": "b5e191a8...", "baseline_result": "OPTIMAL", "got_result": "UNKNOWN"
+}
+artifact.suggestion = "Variant lost feasibility on a problem baseline reached optimal.
+    Revert params that disable a needed subsolver or shorten search."
    -
    - Phase1과의 큰 차이. Phase1은 score=0에서 출발했지만 Phase2는 시작부터 0.9976. 즉 phase1이 잘 작동해 미세 조정만 남은 상태. 큰 도약 대신 점진적 개선이 목표. +
    + 체이닝의 양날. phase 체이닝은 이득뿐 아니라 regression도 전달한다. 단 하나의 OPTIMAL→UNKNOWN이 solved_rate=0을 만들고, score 식의 solved_rate² 항이 combined_score를 0으로 떨군다. 하지만 evaluator가 돌려준 result_mismatch + suggestion artifact가 다음 iteration들의 단서가 된다.
    -

    7.3 Iteration 1 — SIZE_BUCKETS와 STAGE3_OVERRIDES 채움 (score 1.0)

    +

    7.4 Phase 2 회복 — 안전하지만 느린 곡선

    - Iteration 1에서 ensemble이 claude-haiku-4-5를 샘플링. LLM thinking 로그를 한국어로 요약하면: + 이후 iteration들은 "일단 다 풀리게" 만드는 보수적 변형으로 점진 회복한다. solved 10/10이지만 dtime은 baseline보다 느리다(speedup < 1).

    - + 
    iter 1 (4a07c113): combined_score=0.8807  geomean_speedup=0.881   solved 10/10
+iter 3 (2fa91fef): combined_score=0.9771  geomean_speedup=0.901
+iter 9 (5e2691fb): combined_score=0.9905  geomean_speedup=0.946
    - LLM 사고 요약 (번역): -
      -
    1. "현재 score 0.9976 — 이미 매우 높음. 큰 점프 대신 점진적 개선이 필요하다."
      2. -
    2. "Phase2는 presolve/probing 튜닝 단계. cp_model_probing_level=1이 글로벌 기본인데, 작은 문제(<50k)는 SAT-유사 특성이라 probing을 더 높게 가져도 안전하다."
      3. -
    3. "SIZE_BUCKETS의 첫 버킷에 cp_model_probing_level: 2 추가. 중/대 버킷은 그대로 두어 LP-heavy 인스턴스 회귀 방지."
      4. -
    4. "Outlier(stage3 전용)에는 더 공격적으로 — cp_model_probing_level: 3, symmetry_level: 3, probing_num_combinations_limit: 35000. STAGE3_OVERRIDES는 outlier에만 적용되니 일반 stage1/2 성능 손해 없음."
      5. -
    + 국소 최적의 함정. regression은 사라졌지만(solved 10/10) 모두 geomean_speedup < 1.0 — baseline보다 dtime을 더 쓴다. LLM은 "안전한 0.9x"에 머물러 0점은 피했으나 phase1 수준(1.18)엔 못 미치고, stage1 gate를 못 넘어 stage2로도 진입하지 못한다.
    +

    7.5 Phase 2 돌파 — iter 23, 근본 원인 진단 (1.4582)

    - LLM이 적용한 새 EVOLVE-BLOCK: + iter 23에서 LLM이 thinking 로그에 root cause를 적고 직접 고친다:

    - +
    + "Phase1 winner가 use_feasibility_jump=True를 켠 듯하다. W=1에서는 이게 유일 solver로 돌아 LP-heavy 문제에서 optimality 증명 없이 FEASIBLE로 끝난다. 조기 종료를 유발하는 두 휴리스틱을 명시적으로 끈다." + 
    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 1,    # 글로벌은 보수적으로 유지
+    "cp_model_probing_level": 2, "symmetry_level": 1,
+    "use_feasibility_jump": False,   # ← root-cause fix
+    "use_feasibility_pump": False,
+    "stop_after_first_solution": False, "use_lns_only": False,
 }
-
 SIZE_BUCKETS = [
-    (50000,  {"cp_model_probing_level": 2}),   # 작은 문제 — probing 올림
-    (150000, {}),                              # 중간 — 그대로
-    (inf,    {}),                              # 큰 문제 — 그대로 (LP-heavy 보호)
-]
-
-STAGE3_OVERRIDES = {
-    "cp_model_probing_level": 3,               # outlier 전용 — 강력
-    "symmetry_level": 3,
-    "probing_num_combinations_limit": 35000,
-}
    - -

    평가 결과:

    - 
    iter 1: combined_score = 1.0000  (0.9976 → 1.0000, +0.0024)
-        solved_rate = 1.0  (10/10 OPTIMAL)
-        geomean_speedup = 1.0000
-        geomean_wall_speedup = 1.1226
-        efficiency = 1.0000           ← efficiency 페널티 사라짐
-        🌟 New best solution found at iteration 1
    - + (50_000, {probing2, cut_level1, add_mir_cuts, add_zero_half_cuts}), # 작은 문제 + (150_000, {probing2, cut_level1}), # 중간 + (inf, {probing1, linearization_level1}), # 큰 LP-heavy +]
    - 주목할 점. wall_speedup은 1.2351 → 1.1226으로 살짝 하락. 하지만 combined_score는 증가. 이유는 efficiency 0.88 → 1.00. score 식이 wall만 보지 않고 efficiency까지 가중치를 두기 때문에, "솔버가 더 적은 일을 해서 같은 결과를 얻은 변형"이 더 높은 점수를 받는다. + 돌파. stage1 gate 통과 → 전체 cascade(stage4, 20문제) 진입. combined_score=1.4582, geomean_speedup=1.1786 (+17.9%), efficiency=1.8948. phase1과 동일한 속도 수준을 더 안전한 config로 회복 — 즉 phase2의 본질은 "신규 이득"이 아니라 체이닝이 만든 regression을 진단·복구한 것.
    -

    7.4 Iteration 5~6 — 욕심내다 efficiency 페널티

    +

    7.6 Phase 2 실패들 — 좁은 표면의 대가

    - Iteration 5, 6에서 LLM이 더 공격적인 presolve 옵션을 시도. 결과는 의외: + presolve/probing 표면은 이미 잘 도는 baseline 위라 변형 여지가 좁다. 한 발만 잘못 디뎌도 OPTIMAL→UNKNOWN이 나고, 돌파 이후에도 다른 가지에서 같은 종류의 regression이 반복된다.

    - 
    iter 5: combined_score=0.7969  geomean_speedup=0.8460  efficiency=0.8356
-iter 6: combined_score=0.6964  geomean_speedup=0.7689  efficiency=0.7427
    -

    - OPTIMAL은 다 맞췄지만 (solved_rate=1.0) score는 큰 폭으로 하락. 원인은 두 가지: -

    -
      -
    • geomean_speedup < 1.0 — variant가 baseline보다 deterministic_time을 더 많이 씀
      • -
    • efficiency 하락 — conflicts 5,221 → 7,817~8,670개로 증가. 솔버가 헤맴.
      • -
    - -
    - 학습 포인트. 이미 score 1.0인 상태에서 추가 개선은 매우 어렵다. 어설픈 욕심이 오히려 후퇴. score 식 자체가 "더 빠르거나 더 효율적이면 보상, 양쪽 다 나쁘면 페널티" 구조라 stable한 변형이 valuable. + 
    iter 25, 32: combined_score=0.0000
+             error="result regression on dd4063e9bc: baseline=OPTIMAL got=UNKNOWN"
+LLM 생성 timeout: iter 5,10,12,18,19,22,30,35  (claude_code 3-retry 후 skip)
    +
    + 그래도 수렴하는 이유. MAP-Elites + island가 0점 가지를 버리고, artifact의 result_mismatch가 LLM을 반복적으로 안전 영역으로 끌어와 iter 23 돌파를 가능케 했다. 점수만 줬다면 불가능한 인과적 디버깅 — artifact 피드백의 가치다.
    -

    7.5 Iteration 7~13 — Regression 폭주와 같은 문제 반복

    +

    7.7 Phase 3 — LP / cuts (W=8): 평탄한 표면

    - Iteration 7, 8, 10, 11, 12, 13 모두 같은 에러: + Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠다. 시작부터 이미 baseline 수준(상속 config가 강함).

    - 
    combined_score = 0.0000
-error = "result regression on 3e69d6f5b7: baseline=OPTIMAL got=UNKNOWN"
    + 
    initial c2e091f9: combined_score=0.9930  geomean_speedup=0.9930  (solved 10/10, W=8)
+winner  620d4019: combined_score=1.0225  geomean_speedup=0.9778  efficiency=1.1437
+         🌟 new best @ iter 3, 27
    +

    winner config (요약):

    + 
    GLOBAL: max_num_cuts=6000, root_lp_iterations=3500, symmetry_level=3,
+        interleave_search=True, share_glue_clauses=True, solution_pool_size=8
+SIZE_BUCKETS[small]: probing3 + linearization2 + cut_level2
+SIZE_BUCKETS[large]: root_lp_iterations=5000 + MIR/zero-half cuts
    +

    - 같은 문제(SHA 3e69d6f5b7, num_constraints=28938)에서 반복적으로 OPTIMAL → UNKNOWN. LLM이 매번 다른 변형을 시도하는데 자꾸 이 한 문제가 무너진다. 이 문제는 phase1에서 무난히 풀던 문제 — phase2의 presolve 튜닝이 이 문제의 핵심 구조를 깬다는 의미. + 그런데 winner의 combined_score 1.0225는 speedup가 아니라 efficiency factor가 끌어올린 값이다:

    - + 
    combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
+              ≈   0.9778        × 1.1437^0.333
+              ≈   0.9778        × 1.046              ≈ 1.0225
    - Phase2의 어려움. Phase1과 달리 phase2의 가능한 변형 공간이 이미 잘 작동하는 baseline에 비해 좁다. 한 발만 잘못 디뎌도 OPTIMAL → UNKNOWN. LLM이 conservative 영역에서 surf해야 함. + 한계 효용(diminishing returns). deterministic_time(0.978)은 오히려 baseline보다 2.2% 느리다. 줄어든 branch/conflict(efficiency 1.14)가 점수를 1 위로 들어올렸을 뿐, 실질 속도 개선이 아니다. 쉬운 이득은 phase1/2가 이미 가져갔고 LP/cuts 표면은 이 시점에 거의 평탄하다. score 숫자를 해석할 땐 어떤 항이 들어올렸는지 봐야 한다.
    -

    7.6 Iteration 14 — 복구 (score 0.969)

    -

    - 여러 차례 regression 후 iter 14에서 다시 OPTIMAL 회복: -

    - 
    iter 14: combined_score = 0.9690
-         geomean_speedup = 1.0113
-         geomean_wall_speedup = 1.2008
-         efficiency = 0.8794
-         solved_rate = 1.0
    -

    - Parent는 iter 4 (04718c2f, score 0.7969)의 자식이지만 iter 1의 검증된 SIZE_BUCKETS 패턴으로 회귀. 즉 LLM이 artifact를 통해 "3e69d6f5b7에서 무엇이 깨졌는지" 학습 후 안전한 영역으로 복귀. -

    - -

    7.7 종합 — Phase2가 보여주는 OpenEvolve 동작 양상

    +

    7.8 종합 — 3-Phase가 보여주는 OpenEvolve 동작 양상

    - + - - - - - - - + + +
    특징이 로그에서 확인된 증거
    Phase표면 / Winitialwinnergeomean_speedup (dtime)양상
    Phase 체이닝phase1 best (cp_model_probing_level=1)이 phase2 initial로 import. 시작 점수 이미 0.9976.
    점진적 개선iter 1: 0.9976 → 1.0000 (+0.0024). 점프가 아닌 미세 조정.
    Efficiency vs Wall 트레이드오프iter 1은 wall은 약간 느려졌지만 efficiency가 좋아져 종합 점수 상승.
    STAGE3_OVERRIDES 활용LLM이 "outlier 전용 공격적 설정"을 분리 — stage1/2 안전, stage3에만 적용.
    반복적 동일 문제 실패iter 7~13: 같은 문제(3e69d6f5b7)에서 반복 UNKNOWN — phase2 변형이 이 문제 구조를 깸.
    Artifact 학습 복구iter 14에서 LLM이 regression 메시지를 보고 안전 영역으로 회귀.
    Cascade 평가모든 iteration이 stage1만 평가됨 — 점수 1.0 이상 도달 못 해 stage2 진입 못 함 (phase2의 한계).
    1search/subsolvers · W10.71231.45821.1786 (+17.9%)탐색 — 큰 도약
    2presolve/probing · W10.00001.45821.1786진단·복구 — root-cause fix
    3lp/cuts · W80.99301.02250.9778 (−2.2%)평탄 — 한계 효용
    -

    - 20 iteration 후 phase2의 best 변형은 iter 1 (combined_score=1.0000, SIZE_BUCKETS + STAGE3_OVERRIDES 추가). 이 best가 추출돼 phase3의 initial_program.py로 import되고 다음 표면(LP/cuts) 튜닝이 시작된다. -

    -
    - Phase1 vs Phase2 비교. Phase1은 score 0 → 1.0822로 큰 도약 (탐색). Phase2는 0.9976 → 1.0000으로 미세 조정 (수렴). 두 phase의 진화 양상이 본질적으로 다르며, 이게 OpenEvolve의 multi-phase cascade가 단계마다 적절한 최적화를 하는 이유. +
      +
    1. 체이닝은 이득과 regression을 함께 전달한다. phase2 시작 0점은 phase1이 켠 휴리스틱 탓 — artifact가 LLM의 root-cause 진단을 가능케 했다.
      2. +
    2. score 식 해석에 주의. phase3는 dtime<1인데도 efficiency factor가 combined_score를 1 위로 들어올린다. 숫자 하나만 보면 오해할 수 있다.
      3. +
    3. 모든 phase가 이득을 내진 않는다. phase3는 평탄 — 그게 정상이고, cascade는 "어디서 멈출지"를 보여준다.
      4. +
    diff --git a/docs/openevolve-intro/slides.html b/docs/openevolve-intro/slides.html index 19865ec40f..b40a4cb522 100644 --- a/docs/openevolve-intro/slides.html +++ b/docs/openevolve-intro/slides.html @@ -456,6 +456,16 @@

    OpenEvolve

    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
    @@ -891,216 +901,198 @@

    실험 조건

    6. Z3 최적화 결과
    19 / 35
    - +
    -
    7. 실전 로그

    CP-SAT phase2 진화 로그 읽기

    +
    7. 실전 로그

    CP-SAT 3-Phase 진화 로그 읽기

    -

    이론은 끝. 실제 phase2 로그를 따라가며 LLM이 CP-SAT presolve 파라미터를 미세 조정하는 과정 관찰.

    +

    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/phase2_presolve/openevolve_output/logs/openevolve_20260527_154008.log + 로그: input/cpsat-bench/evolve/phase{1,2,3}_*/openevolve_output/logs/

    -

    실행 환경

    +

    공통 실행 환경 (3 phase 동일)

    - - - - - + + + + +
    LLM Ensembleclaude-sonnet-4-6 (0.80) + claude-haiku-4-5 (0.20)
    Random seed42 (재현성)
    Island3개
    총 iteration20
    LOCKED 파라미터random_seed=0, num_search_workers=1
    Phase 위치phase1 best 상속 → presolve 표면 튜닝
    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(20)
    Phase 체이닝phaseN best → phase(N+1) initial_program.py로 상속
    7. 실전 로그
    20 / 35
    - +
    -
    7.2 시작점

    이미 높은 baseline — score 0.9976

    +
    7.1 Phase1

    search / subsolvers — 큰 도약 (0.71 → 1.46)

    -

    Phase2 초기 프로그램은 phase1에서 검증된 minimum 설정만 들고 시작.

    +

    Phase1은 가장 큰 표면(search 전략 + subsolver 조합)을 단독으로 탐색. 초기 config는 탐색이 무거워 dtime은 느리지만 branch/conflict는 적다.

    -
    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 1,
-}
-
-SIZE_BUCKETS = [
-    (50_000,         {}),
-    (150_000,        {}),
-    (float("inf"),   {}),
-]
+
    initial: combined_score=0.7123  geomean_speedup=0.5436  efficiency=2.2518
+winner : combined_score=1.4582  geomean_speedup=1.1786  efficiency=1.8948  (solved 20/20)
+         🌟 new best @ iter 1, 4, 31
    
 
-STAGE3_OVERRIDES = {}
    +

    winner가 채택한 핵심 — feasibility 휴리스틱:

    +
    GLOBAL_OVERRIDES = { "use_feasibility_pump": True }   # 작은/중간 문제 dtime 단축
+SIZE_BUCKETS[large] = { "use_feasibility_jump": True, "use_feasibility_pump": True }
+STAGE3_OVERRIDES = { "repair_hint": True, "hint_conflict_limit": 50 }
    - 평가 결과:
    - combined_score = 0.9976 (시작부터 매우 높음)
    - geomean_speedup = 1.0409 (dtime 4.1% 단축)
    - geomean_wall_speedup = 1.2351 (wall 23.5% 빠름)
    - solved_rate = 1.0 (10/10 OPTIMAL)
    - efficiency = 0.8802 (일부 문제 conflicts 약간 증가) + 결과. deterministic_time 17.9% 단축 (geomean_speedup 1.1786), 20/20 OPTIMAL. 큰 탐색 공간 → 큰 점프. (LLM 생성 timeout 10회 — claude_code 3-retry 후 skip)
    +

    주의: winner가 켠 use_feasibility_jump/pump가 phase2로 상속되며 문제를 일으킨다 →

    -
    7.2 시작점
    21 / 35
    +
    7.1 Phase1
    21 / 35
    - +
    -
    7.2 시작점

    Phase1과의 큰 차이

    +
    7.2 Phase2 시작

    상속이 깨졌다 — score 0.0000

    - - - - - - - - -
    항목Phase1Phase2
    초기 점수0.0000 (망가짐)0.9976 (이미 좋음)
    탐색 공간search/subsolvers (큰 공간)presolve/probing (좁은 공간)
    변형 방향"근본 수정" (멀티워커 옵션 제거)"미세 조정" (SIZE_BUCKETS 채우기)
    기대 진행큰 점프 (0→1.0)점진적 (0.9976→1.0)
    +

    Phase2(presolve/probing)는 phase1 winner를 상속. 그런데 초기 평가가 0점. phase1에서 켠 use_feasibility_jump=True가 W=1 단독 실행 시 한 문제에서 feasibility를 잃었다.

    -
    - Phase2의 본질. phase1이 잘 작동해 미세 조정만 남은 상태. 어설픈 욕심은 오히려 regression. LLM은 conservative 영역에서 surf해야 함. +
    initial c69923ed: combined_score=0.0000  solved_rate=0.0000
+artifact.result_mismatch = {
+    "sha": "b5e191a8...", "baseline_result": "OPTIMAL", "got_result": "UNKNOWN"
+}
+artifact.suggestion = "Variant lost feasibility on a problem baseline reached
+    optimal. Revert params that disable a needed subsolver or shorten search."
    + +
    + 체이닝의 양날. phase 체이닝은 이득뿐 아니라 regression도 전달한다. 단 하나의 OPTIMAL→UNKNOWN이 solved_rate=0을 만들어 combined_score를 0으로 떨군다 (score 식의 solved_rate² 항).
    +

    하지만 evaluator가 돌려준 result_mismatch + suggestion artifact가 다음 iteration들의 단서가 된다.

    -
    7.2 시작점
    22 / 35
    +
    7.2 Phase2 시작
    22 / 35
    - +
    -
    7.3 Iteration 1

    LLM의 진단 (thinking 로그 번역)

    +
    7.3 Phase2 회복

    안전하지만 느린 회복

    +

    이후 iteration들은 "일단 다 풀리게" 만드는 보수적 변형으로 점진 회복. solved 10/10이지만 dtime은 baseline보다 느림(speedup < 1).

    + +
    iter 1 (4a07c113): combined_score=0.8807  geomean_speedup=0.881   solved 10/10
+iter 3 (2fa91fef): combined_score=0.9771  geomean_speedup=0.901
+iter 9 (5e2691fb): combined_score=0.9905  geomean_speedup=0.946
    +
    -
      -
    1. "score 0.9976 — 이미 매우 높음. 큰 점프 대신 점진적 개선이 필요."
      2. -
    2. "cp_model_probing_level=1은 글로벌 기본인데, 작은 문제(<50k)는 SAT-유사라 probing을 더 높게 가져도 안전."
      3. -
    3. "SIZE_BUCKETS 첫 버킷에 cp_model_probing_level: 2 추가. 중/대 버킷은 그대로 — LP-heavy 인스턴스 회귀 방지."
      4. -
    4. "Outlier(stage3 전용)는 더 공격적: probing_level: 3, symmetry_level: 3. STAGE3_OVERRIDES는 outlier에만 적용되니 stage1/2 손해 없음."
      5. -
    + 국소 최적의 함정. regression은 사라졌지만(solved 10/10) 모두 geomean_speedup < 1.0 — baseline보다 dtime을 더 쓴다. LLM은 "안전한 0.9x"에 머물러 0점은 피했지만 phase1 수준(1.18)엔 못 미침. stage1 gate를 못 넘어 stage2로도 못 간다.
    -

    LLM이 buckets별로 다른 강도를 적용하는 layered tuning 전략 채택.

    -
    7.3 Iteration 1
    23 / 35
    +
    7.3 Phase2 회복
    23 / 35
    - +
    -
    7.3 Iteration 1

    변형 적용 → 미세 개선

    +
    7.4 Phase2 돌파

    iter 23 — 근본 원인 진단으로 돌파

    +

    iter 23에서 LLM이 thinking 로그에 root cause를 적고 직접 고친다:

    +
    + "Phase1 winner가 use_feasibility_jump=True를 켠 듯. W=1에서는 이게 유일 solver로 돌아 LP-heavy 문제에서 optimality 증명 없이 FEASIBLE로 끝난다. 두 early-exit 휴리스틱을 명시적으로 끈다." + 
    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 1,    # 글로벌 보수적 유지
+    "cp_model_probing_level": 2, "symmetry_level": 1,
+    "use_feasibility_jump": False,   # ← root-cause fix
+    "use_feasibility_pump": False,
+    "stop_after_first_solution": False, "use_lns_only": False,
 }
-
-SIZE_BUCKETS = [
-    (50000,  {"cp_model_probing_level": 2}),   # 작은 문제만 올림
-    (150000, {}),                              # 중간 — 그대로
-    (inf,    {}),                              # 큰 문제 — 그대로
-]
-
-STAGE3_OVERRIDES = {
-    "cp_model_probing_level": 3,               # outlier — 강력
-    "symmetry_level": 3,
-    "probing_num_combinations_limit": 35000,
-}
    +# SIZE_BUCKETS: small=probing2+MIR/zero-half cuts, mid=probing2+cut1, large=probing1+lin1
    - 평가 결과:
    - combined_score: 0.9976 → 1.0000 (+0.0024)
    - efficiency: 0.8802 → 1.0000 (페널티 사라짐)
    - wall_speedup 1.2351 → 1.1226 (살짝 하락하지만 score는 상승)
    - 🌟 New best solution found at iteration 1 + 돌파. stage1 gate 통과 → 전체 cascade(stage4, 20문제) 진입. combined_score=1.4582, geomean_speedup=1.1786 (+17.9%). phase1과 동일 수준을 더 안전한 config로 회복 — 체이닝이 만든 regression을 진단·복구한 것.
    -
    7.3 Iteration 1
    24 / 35
    +
    7.4 Phase2 돌파
    24 / 35
    - +
    -
    7.4 Iter 5~6

    욕심내다 efficiency 페널티

    +
    7.5 Phase2 실패

    돌파 전후의 실패들

    -
    iter 5: combined_score=0.7969  geomean_speedup=0.8460  efficiency=0.8356
-iter 6: combined_score=0.6964  geomean_speedup=0.7689  efficiency=0.7427
    +

    좁은 변형 공간에서 한 발만 잘못 디뎌도 OPTIMAL→UNKNOWN. 돌파 이후에도 다른 가지에서 같은 종류의 regression이 반복.

    -

    OPTIMAL은 다 맞췄는데 점수 큰 폭 하락. 원인 둘:

    +
    iter 25, 32: combined_score=0.0000
+             error="result regression on dd4063e9bc: baseline=OPTIMAL got=UNKNOWN"
+LLM 생성 timeout: iter 5,10,12,18,19,22,30,35  (claude_code 3-retry 후 skip)
    -
      -
    • geomean_speedup < 1.0 — variant가 baseline보다 dtime 더 씀
      • -
    • efficiency 하락 — conflicts 5,221 → 7,817~8,670 (솔버 헤맴)
      • -
    - -
    - 학습 포인트. 이미 1.0인 상태에서 추가 개선은 매우 어렵다. 어설픈 욕심이 오히려 후퇴. Score 식 자체가 안정성을 강조. +
    + 왜 0점이 자꾸 나오나. presolve/probing 표면은 이미 잘 도는 baseline 위라 변형 여지가 좁다. 그래도 MAP-Elites + island가 0점 가지를 버리고, artifact의 result_mismatch가 LLM을 안전 영역으로 끌어와 iter 23 돌파를 가능케 했다.
    -
    7.4 Iter 5~6
    25 / 35
    +
    7.5 Phase2 실패
    25 / 35
    - +
    -
    7.5 Iter 7~13

    Regression 폭주 — 같은 문제 반복

    +
    7.6 Phase3

    lp / cuts (W=8) — 평탄한 표면

    -
    iter 7:  combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
-iter 8:  combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
-iter 10: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
-iter 11: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
-iter 12: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
-iter 13: combined_score=0.0000  error="3e69d6f5b7: OPTIMAL→UNKNOWN"
    +

    Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠. 시작부터 이미 baseline 수준 (상속 config가 강함).

    -

    같은 문제(SHA 3e69d6f5b7, constraints=28938)에서 반복적 OPTIMAL → UNKNOWN. 매 iteration LLM이 다른 변형 시도하지만 자꾸 이 한 문제가 무너짐.

    +
    initial c2e091f9: combined_score=0.9930  geomean_speedup=0.9930  (solved 10/10, W=8)
+winner  620d4019: combined_score=1.0225  geomean_speedup=0.9778  efficiency=1.1437
+         🌟 new best @ iter 3, 27
    -
    - Phase2의 어려움. 가능한 변형 공간이 이미 좋은 baseline에 비해 좁다. 한 발만 잘못 디뎌도 핵심 문제 구조를 깬다. LLM이 conservative 영역에서 surf해야 함. +

    winner config (요약):

    +
    GLOBAL: max_num_cuts=6000, root_lp_iterations=3500, symmetry_level=3,
+        interleave_search=True, share_glue_clauses=True, solution_pool_size=8
+SIZE_BUCKETS[small]: probing3 + linearization2 + cut_level2
+SIZE_BUCKETS[large]: root_lp_iterations=5000 + MIR/zero-half cuts
    + +
    + 관찰. 8개 워커 portfolio에 무거운 LP/cuts를 더해도 geomean_speedup=0.9778 — dtime은 오히려 2.2% 느려짐.
    -
    7.5 Iter 7~13
    26 / 35
    +
    7.6 Phase3
    26 / 35
    - +
    -
    7.6 Iter 14

    Artifact 학습으로 복구

    +
    7.7 Phase3 해석

    score > 1 인데 왜 dtime은 느린가

    -
    iter 14: combined_score = 0.9690
-         geomean_speedup = 1.0113
-         geomean_wall_speedup = 1.2008
-         efficiency = 0.8794
-         solved_rate = 1.0
-         parent = iter 4 (04718c2f, score=0.7969)
    +

    winner의 combined_score 1.0225는 speedup가 아니라 efficiency factor가 끌어올린 값:

    -

    여러 차례 regression 후 iter 14에서 다시 OPTIMAL 회복. Parent는 iter 4의 자식이지만 iter 1의 검증된 SIZE_BUCKETS 패턴으로 회귀.

    +
    combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
+              ≈   0.9778        × 1.1437^0.333
+              ≈   0.9778        × 1.046              ≈ 1.0225
    -
    - 핵심. LLM이 artifact error 메시지("3e69d6f5b7: OPTIMAL→UNKNOWN")를 반복적으로 보면서 "이 영역은 위험"이라는 신호를 학습. 안전 영역으로 자율 회귀. +

    즉 dtime(0.978)은 baseline보다 느린데, branch/conflict가 줄어든 efficiency(1.14)가 점수를 1 위로 들어올린 것.

    + +
    + 한계 효용(diminishing returns). 쉬운 이득은 phase1/2가 이미 가져갔다. LP/cuts 표면은 이 시점에 거의 평탄 — 점수 1.02는 "실질 속도 개선"이 아니다. score 숫자를 해석할 때 어떤 항이 들어올렸는지 봐야 함.
    -

    점수만 줬을 때는 불가능한 인과적 디버깅 — artifact 피드백의 가치 실증.

    +

    artifact 피드백 + 분해 가능한 score = 점수만 봤을 때 놓칠 인과를 드러냄.

    -
    7.6 Iter 14
    27 / 35
    +
    7.7 Phase3 해석
    27 / 35
    - +
    -
    7.7 종합

    Phase2 로그가 보여준 동작 양상

    +
    7.8 종합

    3-Phase가 보여준 동작 양상

    - + - - - - - - + + +
    특징로그 증거
    Phase표면 / Winitialwinnergeomean_speedup양상
    Phase 체이닝phase1 best이 phase2 initial로 import. 시작 점수 이미 0.9976
    점진적 개선iter 1: 0.9976 → 1.0000. 점프 아닌 미세 조정
    Efficiency vs Walliter 1: wall 살짝 느려도 efficiency 개선으로 종합 점수 상승
    Layered tuningSIZE_BUCKETS + STAGE3_OVERRIDES로 문제 크기별/outlier별 분리 적용
    반복 동일 실패iter 7~13: 같은 문제(3e69d6f5b7) 반복 UNKNOWN
    Artifact 학습 복구iter 14에서 LLM이 위험 영역 회피, 안전 영역으로 자율 회귀
    1search/subsolvers · W10.71231.45821.1786 (+17.9%)탐색 — 큰 도약
    2presolve/probing · W10.00001.45821.1786진단·복구 — root-cause fix
    3lp/cuts · W80.99301.02250.9778 (−2.2%)평탄 — 한계 효용
    -

    20 iteration 후 best = iter 1 (combined_score=1.0000). 이 best가 phase3의 시작점으로 import.

    - -
    - Phase1 vs Phase2. Phase1은 0 → 1.0822 큰 도약 (탐색). Phase2는 0.9976 → 1.0000 미세 조정 (수렴). multi-phase cascade가 단계마다 다른 최적화 양상을 보임. +
    +
      +
    1. 체이닝은 이득과 regression을 함께 전달. phase2 시작 0점은 phase1이 켠 휴리스틱 탓 — artifact가 LLM의 root-cause 진단을 가능케 함.
      2. +
    2. score 식 해석 주의. phase3는 dtime<1인데 efficiency factor가 score를 1 위로 들어올림.
      3. +
    3. 모든 phase가 이득을 내진 않는다. phase3는 평탄 — cascade가 "어디서 멈출지"를 보여줌.
      4. +
    -
    7.7 종합
    28 / 35
    +
    7.8 종합
    28 / 35
    From 35e87cf1a4c691c04364eda2810a6074f2b9b92b Mon Sep 17 00:00:00 2001 From: hdson Date: Thu, 28 May 2026 18:25:28 +0900 Subject: [PATCH 36/42] [feat] add small profile mode --- input/cpsat-bench/evolve/README.md | 142 +++- input/cpsat-bench/evolve/build_samples.py | 74 +- input/cpsat-bench/evolve/rebaseline_local.py | 70 +- input/cpsat-bench/evolve/shared/evaluator.py | 214 +++-- input/cpsat-bench/evolve/shared/score.py | 9 +- input/cpsat-bench/problems.jsonl | 773 +++++++++++++++++++ input/run_phase.sh | 10 +- 7 files changed, 1146 insertions(+), 146 deletions(-) diff --git a/input/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index 294eab78a9..881cd9b9e6 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -23,7 +23,7 @@ input/cpsat-bench/ ├── config.yaml # OpenEvolve config + custom knobs ├── run_phase.sh # phase runner (chmod +x) ├── build_samples.py # stage{1..4} sample selection - ├── extract_best.py # per-phase winner extraction + ├── extract_best.py # per-phase winner extraction (1..4) ├── prepare_phase_unified.py # materializes phase4 EVOLVE-BLOCK ├── rebaseline_local.py # measure baseline on local box ├── shared/ @@ -36,39 +36,40 @@ input/cpsat-bench/ │ ├── stage{1..4}_sample.json # small profile (cascade staging) │ ├── stage1_large_sample.json # large profile (single hardest outlier) │ └── local_baseline.json # built by rebaseline_local.py - └── phase{1..4}_/ - └── initial_program.py # EVOLVE-BLOCK + ├── phase{1..4}_/ + │ └── initial_program.py # EVOLVE-BLOCK (top-level param tuning) + └── phase5_custom_subsolvers/ + └── initial_program.py # EVOLVE-BLOCK (adds custom subsolvers) ``` ## Evaluation flow (cascade) +The cascade differs by profile — see [Profiles](#profiles-small-vs-large). +The **small** profile (default) is: + ``` LLM-mutated initial_program.py ↓ evaluator.py: 1. get_params(problem=None, stage=...) → global dict (workers/locked) 2. LOCKED-violation check (random_seed, num_search_workers, timeout_sec) - 3. stage1 (10 problems, runtime Q1+Q2 cluster centers): + 3. stage1 (10 problems, runtime c1+c2 cluster centers): get_params(problem=p, stage="stage1") per problem - invalid_param triggers early 0 + each solve repeated 10× and averaged (dtime); invalid_param → early 0 score → gate (cascade_thresholds[0]) → stage2 - 4. stage2 (10 problems, runtime Q3+Q4 cluster centers): same - 5. stage3 (5 OUTLIER problems, stratified small/mid/large @ cap=1.5M ms, - phase3/4 only — phase1/2 skips stage3 and goes direct to stage4): - get_params(problem=p, stage="stage3") — STAGE3_OVERRIDES applied - on top of GLOBAL_OVERRIDES + SIZE_BUCKETS match - score → gate (cascade_thresholds[2]) → stage4 (chained inside stage3) - 6. stage4 (20 broad-runtime problems): final metrics + artifacts + 4. stage2 (10 problems, runtime c3+c4 cluster centers): same + 5. stage3 slot = _finalize_small(): merge cached stage1 + stage2 results + into ONE combined final score. No outlier stage3, no broad stage4. ``` -### Why phase1/2 skip stage3 +Non-feasible variants are NOT aborted — their real (slow) timeout ratio plus +a `solved_rate` drop is the penalty. Scoring uses `deterministic_time`. -Stage3 holds outlier problems whose recorded baseline came from W=8 runs -(median ~25min). Running them at W=1 (phase1/2 PHASE_LOCKED) takes ~10× -longer with no useful signal — phase1/2 tune knobs (presolve, single-worker -search) that the outliers don't really exercise. `evaluate_stage3` checks -`_peek_workers(program_path)` and, when W=1, returns stage4 metrics -directly with a `stage3_policy=skipped` artifact note. +> The **large** profile bypasses staging entirely: every cascade entry point +> evaluates a single hardest-outlier set once (cache-backed). The default +> `build_samples.py` (no `--small`) still emits a 4-stage layout +> (stage3=outliers, stage4=broad) for ad-hoc analysis / `evaluate_stage4`, +> but the small cascade ignores stage3/4. ### Per-problem param resolution @@ -89,6 +90,58 @@ STAGE3_OVERRIDES (gated) → PHASE_LOCKED`. from `Statistics/outliers_top.csv`. Reference for valid CP-SAT param names: `shared/cpsat_params_reference.md` (275 fields, grouped by category). +## Phase 5: custom subsolvers + +Phases 1–4 tune **top-level** CP-SAT parameters. Phase 5 instead adds **custom +subsolvers** to the portfolio, leaving the inherited top-level config (phase4 +winner) untouched. + +Why a separate mechanism: a top-level param applies to *every* subsolver, +including the LNS workers. An expensive propagation technique enabled top-level +also fires inside LNS, making it useless; an objective-requiring config on an +objective-less model makes CP-SAT drop most/all subsolvers. Isolating the knob +in a single extra subsolver bounds the downside — if it doesn't help only that +one worker is slow; if it helps, that worker shares solutions / variable bounds +with the rest of the portfolio. + +Evolve surface (`phase5_custom_subsolvers/initial_program.py` EVOLVE-BLOCK): + +| Surface | Applied when | Purpose | +|---|---|---| +| `CUSTOM_SUBSOLVERS` | every problem | portfolio additions for all problems | +| `STAGE3_CUSTOM_SUBSOLVERS` | `stage == "stage3"` AND `problem.is_outlier` | extra subsolvers for long-tail outliers | + +Each entry is a dict: + +```python +{ + "name": "max_lp_heavy", # required, unique + "params": {"linearization_level": 2, "add_mir_cuts": True}, + "needs_objective": False, # optional; skip on objective-less models + "min_constraints": 50000, # optional inclusive lower gate + "max_constraints": None, # optional exclusive upper gate +} +``` + +`get_params()` turns eligible entries into `subsolver_params` (a named +`SatParameters` set per subsolver) plus `extra_subsolvers` names, on top of the +inherited phase4 config. `needs_objective` entries are dropped when the model +has no objective (`shared/has_objective_cache.json`), avoiding portfolio +collapse. Locks: `random_seed=0`, `num_search_workers=8`, +`interleave_search=True` (the cross-worker sharing that makes an isolated +subsolver pay off requires it). + +The worker (`shared/_cpsat_solve_worker.py`) builds params on a standalone +`sat_parameters_pb2.SatParameters` proto and assigns it wholesale, so nested +`subsolver_params` messages apply on recent ortools (9.15+) where in-place +mutation of repeated messages is unsupported. Invalid subsolver fields surface +as `invalid_param: subsolver_params.`. + +Phase 5 is the terminal phase (no extract step). Phase 4 is therefore no longer +terminal: `run_phase.sh` materializes its EVOLVE-BLOCK before it runs (config +key `unified_prepare_before_dir`) and extracts `phase4_best.json` after, which +phase 5 inherits. + ## Quick start ```bash @@ -109,13 +162,17 @@ python input/cpsat-bench/evolve/rebaseline_local.py ./input/run_phase.sh cpsat-bench 1 ./input/run_phase.sh cpsat-bench 2 ./input/run_phase.sh cpsat-bench 3 -./input/run_phase.sh cpsat-bench 4 +./input/run_phase.sh cpsat-bench 4 # unified refinement (top-level params) +./input/run_phase.sh cpsat-bench 5 # custom subsolvers (inherits phase4_best) # or all phases at once: ./input/run_phase.sh cpsat-bench ``` -After each non-final phase, `run_phase.sh` calls `extract_best.py` to write -`shared/phaseN_best.json`, which the next phase's `initial_program.py` loads. +After each non-final phase (1–4), `run_phase.sh` calls `extract_best.py` to +write `shared/phaseN_best.json`, which the next phase's `initial_program.py` +loads. Phase 5 is terminal (no extract). Running phase 5 standalone requires +`shared/phase4_best.json` — produced by phase 4, or regenerate with +`./input/run_phase.sh cpsat-bench 4 --extract-only`. ## Profiles (`small` vs `large`) @@ -124,23 +181,44 @@ tuning tracks: | | small (default) | large | |---|---|---| -| sample files | `stage{1..4}_sample.json` (cascade staging) | `stage1_large_sample.json` (single sample) | +| sample files | `stage1/2_sample.json` (stage3/4 empty) | `stage1_large_sample.json` (single sample) | | phase1/2 workers | 1 | 8 | | phase3/4 workers | 8 | 8 | -| evaluation | cascade stage1 → 2 → 3 → 4 | single eval on all stages (cache-backed) | -| problem set | clustered fast/mid + outliers + broad spread | top-`STAGE1_LARGE_N` residual outlier(s) from `Statistics/outliers_top.csv` | +| cascade | stage1 → stage2 → **combined final** (no stage3/4) | single eval on all stages (cache-backed) | +| solve repeats | **10× per problem, averaged** | 1× | +| problem set | clustered fast (c1+c2) + mid (c3+c4) | top-`STAGE1_LARGE_N` outlier(s) sorted by `STAGE1_LARGE_SORT_BY` | | output dir | `openevolve_output/` | `openevolve_output_large/` | +Both profiles use **deterministic_time** as the primary score metric (wall is +diagnostic only) and treat a **non-feasible variant's real measured time** as +its ratio — a timed-out solve contributes its slow timeout ratio + a +`solved_rate` drop, never a fixed `1e-6` sentinel and never an abort. + Profile sets `OPENEVOLVE_PROFILE` env var that: -- `evaluator.py` reads to pick sample file + dispatch every cascade stage - entry point (`evaluate_stage{1..4}`) to a single `_evaluate_large()` for - large profile (cache-backed so 3-stage cascade only runs 1 real eval). -- `rebaseline_local.py` reads to rebaseline only the large sample set. +- `evaluator.py` reads to pick sample files and route the cascade: + - **small** → `evaluate_stage1` (stage1), `evaluate_stage2` (stage2), + `evaluate_stage3` = `_finalize_small()` which merges the cached stage1 + + stage2 per-problem results into one combined score (no stage3/4). Each + solve runs `N_REPEATS_SMALL` (=10) times and averages dtime/wall/counters + to damp multi-worker jitter (`OPENEVOLVE_SOLVE_REPEATS` overrides). + - **large** → every entry point dispatches to a single `_evaluate_large()` + over the outlier set (cache-backed, 1× solve). +- `rebaseline_local.py` reads to rebaseline only the active profile's sample set. - `phase{1,2}_*/initial_program.py` reads to override `PHASE_LOCKED` `num_search_workers=8` (default 1). -Adjust `STAGE1_LARGE_N` in `build_samples.py` to widen the outlier set. +Build samples per profile: + +```bash +python build_samples.py # default: all 4 stages (stage3=outliers, stage4=broad) +python build_samples.py --small # small: stage1+stage2 only, stage3/4 empty +python build_samples.py --optimize-only # restrict pool to objective-bearing problems +``` + +`run_phase.sh --profile small` auto-passes `--small` to the build step. +Adjust `STAGE1_LARGE_N` / `STAGE1_LARGE_SORT_BY` in `build_samples.py` to +widen or re-rank the large outlier set. ```bash # large profile (all phases W=8, single hardest outlier) @@ -220,7 +298,9 @@ time_ratio = baseline_dtime / variant_dtime (primary) ## Locked params -`random_seed=0`, `num_search_workers` (per-phase: 1 in phase1/2, 8 in phase3/4). +`random_seed=0`, `num_search_workers` (per-phase: 1 in phase1/2, 8 in +phase3/4/5). Phase 5 additionally locks `interleave_search=True` (required for +the cross-worker sharing a custom subsolver depends on). Modifying any locked key in `get_params()` returns `combined_score=0` plus a `locked_violated` artifact identifying the offending key. Per-problem diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py index d61b98bc94..978be19d3c 100644 --- a/input/cpsat-bench/evolve/build_samples.py +++ b/input/cpsat-bench/evolve/build_samples.py @@ -433,6 +433,13 @@ def main(): "an objective (drops pure-feasibility models). Default off — " "keep all problems.", ) + ap.add_argument( + "--small", + action="store_true", + help="small-profile build: generate stage1 + stage2 only and leave " + "stage3 / stage4 empty (small cascade runs stage1+stage2). " + "Default off — generate all four stages.", + ) args = ap.parse_args() rows = _scan_raw() @@ -496,45 +503,54 @@ def _bucket_range(b): s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) - # Stage3: target-ms outlier pick from Statistics/outliers_top.csv — - # outliers whose baseline_ms is closest to STAGE3_TARGET_MS (within - # STAGE3_TARGET_TOL_RATIO). Keeps stage3 runtime variance low so cascade - # score is dominated by signal, not noise from a 1500ms vs 1500000ms split. - # Fallback to slow-cluster center pick if csv missing. + # outliers_top.csv feeds outliers.json (is_outlier flag + large-profile + # pick source) and, when generating all stages, the stage3 sample. rows_by_sha = {_id_key(d): d for d in rows} outliers_csv = _find_outliers_csv() s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv, all_rows=rows) \ if outliers_csv else (None, []) - if s3_outliers: - s3 = s3_outliers - stage3_criteria = ( - f"outliers from {outliers_csv.name} nearest " - f"{STAGE3_TARGET_MS}ms (tol={STAGE3_TARGET_TOL_RATIO}x, " - f"cap={STAGE3_MAX_BASELINE_MS}ms; target {STAGE3_N})" - ) + + if args.small: + # small-profile build: stage1 + stage2 only. stage3 (outliers → large + # profile) and stage4 (broad spread) left empty; the small cascade in + # evaluator merges stage1+stage2. + _write_outliers_json([], csv_all) + _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") + _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") + _write_sample(_STAGE3, [], "stage3", "EMPTY — --small build (stage1+stage2 cascade)") + _write_sample(_STAGE4, [], "stage4", "EMPTY — --small build (stage1+stage2 cascade)") + printed_stages = (("stage1", s1), ("stage2", s2)) else: - s3 = _center_pick(pool_c5, STAGE3_N) - stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " - "(outliers_top.csv unavailable or empty)") + # Default build: all four stages. + if s3_outliers: + s3 = s3_outliers + stage3_criteria = ( + f"outliers from {outliers_csv.name} nearest " + f"{STAGE3_TARGET_MS}ms (tol={STAGE3_TARGET_TOL_RATIO}x, " + f"cap={STAGE3_MAX_BASELINE_MS}ms; target {STAGE3_N})" + ) + else: + s3 = _center_pick(pool_c5, STAGE3_N) + stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " + "(outliers_top.csv unavailable or empty)") - _write_outliers_json(s3 if s3_outliers else [], csv_all) + _write_outliers_json(s3 if s3_outliers else [], csv_all) - # Stage4: broad spread across full decisive pool, dedup vs stage1-3. - used = {_id_key(d) for d in (s1 + s2 + s3)} - broad = sorted( - (d for d in candidates if _id_key(d) not in used), - key=_runtime_key, - ) - s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) + used = {_id_key(d) for d in (s1 + s2 + s3)} + broad = sorted( + (d for d in candidates if _id_key(d) not in used), + key=_runtime_key, + ) + s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) - _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") - _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") - _write_sample(_STAGE3, s3, "stage3", stage3_criteria) - _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") + _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") + _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") + _write_sample(_STAGE3, s3, "stage3", stage3_criteria) + _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") + printed_stages = (("stage1", s1), ("stage2", s2), ("stage3", s3), ("stage4", s4)) - for label, picks in (("stage1", s1), ("stage2", s2), - ("stage3", s3), ("stage4", s4)): + for label, picks in printed_stages: print(f"\n{label}:") for d in picks: print(f" {_id_key(d)[:12]} " diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py index 4e3c23501c..07e8849e48 100644 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ b/input/cpsat-bench/evolve/rebaseline_local.py @@ -90,6 +90,63 @@ def _stage_sample(stage_num): REBASELINE_TIMEOUT_S = 3600 +# Match the evaluator: small profile averages N repeats per solve so the +# baseline is measured the SAME way variants are (fair dtime ratio). large +# profile measures once. OPENEVOLVE_SOLVE_REPEATS overrides. +N_REPEATS_SMALL = 10 + + +def _solve_repeats(): + env = os.environ.get("OPENEVOLVE_SOLVE_REPEATS") + if env: + try: + return max(1, int(env)) + except ValueError: + pass + return N_REPEATS_SMALL if _profile() == "small" else 1 + + +def _average_runs(runs): + """Mean elapsed_ms / stats / objective over N run_cpsat dicts. Mirrors + evaluator._average_runs so baseline + variant are averaged identically.""" + import collections + import statistics + + 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))] + 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 + def _load_problem_index(): idx = {} @@ -211,14 +268,21 @@ def _fmt(b): return ",".join(str(x) for x in b) return str(b) if b is not None else "-" - print(f" workers={w}: parallel={n_parallel} blocks={[_fmt(b) for b in blocks]}", - flush=True) + repeats = _solve_repeats() + print(f" workers={w}: parallel={n_parallel} repeats={repeats} " + f"blocks={[_fmt(b) for b in blocks]}", flush=True) def _solve(task): i, meta, path = task block = pool.get() try: - res = run_cpsat(path, params, REBASELINE_TIMEOUT_S, cpu_core=block) + runs = [] + for _ in range(repeats): + rr = run_cpsat(path, params, REBASELINE_TIMEOUT_S, cpu_core=block) + runs.append(rr) + if "invalid_param" in rr: + break + res = _average_runs(runs) finally: pool.put(block) return i, meta, res, block diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py index 9abc934899..107b7aec98 100644 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ b/input/cpsat-bench/evolve/shared/evaluator.py @@ -39,6 +39,12 @@ TIMEOUT_FACTOR = 1.3 MIN_TIMEOUT_S = 5 +# small profile repeats each solve N times and averages (deterministic_time +# + wall + counters) to damp multi-worker run-to-run variance. large profile +# (single huge outlier) runs once — repeating a ~10 min solve 10× is wasteful. +# Override with OPENEVOLVE_SOLVE_REPEATS. +N_REPEATS_SMALL = 10 + _HERE = pathlib.Path(__file__).resolve().parent sys.path.insert(0, str(_HERE)) @@ -46,7 +52,7 @@ from score import score # noqa: E402 from cpsat_runner import run_cpsat # noqa: E402 from runtime import ( # noqa: E402 - parallel_solvers, cascade_threshold, core_range, alloc_core_blocks, + parallel_solvers, core_range, alloc_core_blocks, ) from openevolve.evaluation_result import EvaluationResult # noqa: E402 @@ -93,6 +99,9 @@ def _load_outlier_shas(): _PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") +# Raw per-problem results cache (small profile). {(program_path, stage): [recs]} +_SMALL_RESULTS_CACHE: dict = {} + _KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", "wall_time", "user_time", "deterministic_time") _DECISIVE = ("OPTIMAL", "FEASIBLE") @@ -105,6 +114,64 @@ def _load_program(path): return module +def _solve_repeats(): + """How many times to run each solve before averaging. small profile → 10 + (damps multi-worker variance), large → 1. OPENEVOLVE_SOLVE_REPEATS env + overrides.""" + env = os.environ.get("OPENEVOLVE_SOLVE_REPEATS") + if env: + try: + return max(1, int(env)) + except ValueError: + pass + return N_REPEATS_SMALL if _profile() == "small" else 1 + + +def _average_runs(runs): + """Collapse N run_cpsat dicts into one with mean elapsed_ms / stats / + objective. Repeated runs of the same params are near-identical except for + multi-worker timing jitter, so the mean is the stable estimate. A run that + surfaced invalid_param short-circuits (config error, not a timing sample). + result = modal label; timeout = any run timed out.""" + import collections + import statistics + + 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))] + 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 + + def _pick_local_baseline(lo_entry, workers): """Select the right local baseline entry for `workers`. @@ -183,13 +250,6 @@ def _filter_stage2(problems): return [p for p in problems if p["sha"] in keep] -def _filter_stage3(problems): - 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): if not _STAGE4_SAMPLE.exists(): return problems @@ -360,6 +420,8 @@ def _evaluate(program_path, problems, stage_name): for _b in _blocks: _core_pool.put(_b) + repeats = _solve_repeats() + def _solve(idx_p): idx, p = idx_p input_path = _RAW_DIR / p["input_file"] @@ -388,21 +450,18 @@ def _solve(idx_p): ) core = _core_pool.get() try: - r = run_cpsat(input_path, per_params, timeout_s, - python_bin=_PYTHON_BIN, cpu_core=core) + runs = [] + for _ in range(repeats): + rr = run_cpsat(input_path, per_params, timeout_s, + python_bin=_PYTHON_BIN, cpu_core=core) + runs.append(rr) + if "invalid_param" in rr: + break # config error — no point repeating + r = _average_runs(runs) finally: _core_pool.put(core) return idx, p, r, core, timeout_s - def _is_regression(p, r): - # cost mode: baseline OPTIMAL → variant must reach OPTIMAL or FEASIBLE. - # bailing to UNKNOWN/INFEASIBLE/MODEL_INVALID on an OPTIMAL baseline = regression. - if "invalid_param" in r: - return False - if p["baseline_result"] not in _DECISIVE: - return False - return r.get("result") not in _DECISIVE - def _invalid_err(r): return _err_result( {"error": f"invalid param: {r['invalid_param']}"}, @@ -411,24 +470,6 @@ def _invalid_err(r): "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 feasibility on a problem baseline reached optimal. " - "Revert params that disable a needed subsolver or shorten search.", - }, - ) - def _fmt_core(c): if c is None: return "-" @@ -437,8 +478,14 @@ def _fmt_core(c): return str(c) print(f" [{stage_name}] workers/solve={workers_per_solve} " + f"repeats={repeats} " f"core_blocks={[_fmt_core(b) for b in _blocks]}", flush=True) + # Non-feasible variants are NO LONGER aborted/zeroed — the variant's real + # measured time (≈ timeout when it ran the full budget) flows into the + # score, so a regression is penalized by its true slowdown + solved_rate + # drop, not by a 1e-6 sentinel. Only invalid_param (a config error, not a + # timing sample) still aborts the whole evaluation. by_idx = {} abort = None if n_parallel == 1: @@ -449,10 +496,6 @@ def _fmt_core(c): f"(core={_fmt_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}", flush=True) - return _regression_err(p, r) by_idx[idx] = (p, r) else: from concurrent.futures import ThreadPoolExecutor, as_completed @@ -468,10 +511,7 @@ def _fmt_core(c): f"(core={_fmt_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 " + print(f" [{stage_name}] invalid_param — cancelling pending " f"(in-flight workers will drain)", flush=True) for f in futures: f.cancel() @@ -479,7 +519,7 @@ def _fmt_core(c): 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) + return _invalid_err(r) results = [] for idx in range(len(problems)): @@ -495,6 +535,11 @@ def _fmt_core(c): rec["objective"] = r["objective"] results.append(rec) + # Cache raw per-problem results so the small cascade can merge stage1 + + # stage2 into one combined final score (see evaluate_stage3). Keyed by + # program_path + stage; overwritten each cascade so no stale leakage. + _SMALL_RESULTS_CACHE[(str(program_path), stage_name)] = results + metrics = score(results) metrics["stage"] = stage_name @@ -617,42 +662,53 @@ def evaluate_stage2(program_path): return _evaluate(program_path, _filter_stage2(_load_problems(w)), "stage2") +def _finalize_small(program_path): + """Small-profile final cascade slot. The cascade runs stage1 then stage2; + this merges their cached per-problem results into ONE combined score over + stage1 ∪ stage2. No stage3 (outliers live in the large profile) and no + stage4. Falls back to re-running a stage if its cache entry is absent.""" + key = str(program_path) + r1 = _SMALL_RESULTS_CACHE.get((key, "stage1")) + if r1 is None: + res = evaluate_stage1(program_path) + if not isinstance(res, EvaluationResult): + return res + r1 = _SMALL_RESULTS_CACHE.get((key, "stage1"), []) + r2 = _SMALL_RESULTS_CACHE.get((key, "stage2")) + if r2 is None: + res = evaluate_stage2(program_path) + if not isinstance(res, EvaluationResult): + return res + r2 = _SMALL_RESULTS_CACHE.get((key, "stage2"), []) + + combined = list(r1) + list(r2) + if not combined: + return _evaluate(program_path, [], "small_final") # empty pass-through + + metrics = score(combined) + metrics["stage"] = "small_final" + for k in _KEY_STATS: + metrics[f"total_{k}"] = float(sum(r["stats"].get(k, 0) for r in combined)) + artifacts = { + "stage": "small_final", + "summary": ( + f"combined stage1+stage2 ({len(combined)} problems) " + f"solved={metrics['solved']}/{metrics['total']} " + f"regressions={metrics['regressions']} " + f"geomean_dtime={metrics['geomean_speedup']:.3f} " + f"score={metrics['combined_score']:.3f}" + ), + } + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + def evaluate_stage3(program_path): if _profile() == "large": return _evaluate_large(program_path) - # openevolve cascade hardcodes 3 stages, so user-stage4 (broad runtime - # sample) is chained inside stage3 via the runtime cascade_threshold gate. - # - # Phase1/2 (W=1) policy: SKIP stage3 entirely. Stage3 holds outlier - # problems whose baseline is W=8-equivalent (raw-data was recorded with - # workers=8); running them at W=1 takes ≥10× longer with no signal — - # phase1/2 tune knobs that are irrelevant at W=1 anyway (subsolver mix, - # cuts). For W=1 phases, skip stage3 and go straight to stage4 as the - # final cascade step. - w = _peek_workers(program_path) - if w == 1: - problems4 = _filter_stage4(_load_problems(w)) - r4 = _evaluate(program_path, problems4, "stage4") - if isinstance(r4, EvaluationResult): - r4.artifacts["stage3_policy"] = ( - "skipped: phase has num_search_workers=1 — outlier stage3 " - "is W=8-only (see evaluator.evaluate_stage3 docstring)") - return r4 - - problems3 = _filter_stage3(_load_problems(w)) - 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(w)) - r4 = _evaluate(program_path, problems4, "stage4") - if not isinstance(r4, EvaluationResult): - return r4 - merged_metrics = {**r3.metrics, **r4.metrics} - merged_artifacts = {**r3.artifacts, **r4.artifacts} - return EvaluationResult(metrics=merged_metrics, artifacts=merged_artifacts) + # small profile: cascade is stage1 → stage2 only. This final slot merges + # cached stage1 + stage2 results into one combined score. stage3 (outliers) + # and stage4 (broad spread) are no longer part of the small cascade. + return _finalize_small(program_path) def evaluate_stage4(program_path): diff --git a/input/cpsat-bench/evolve/shared/score.py b/input/cpsat-bench/evolve/shared/score.py index 2041cc0e39..826aaa4881 100644 --- a/input/cpsat-bench/evolve/shared/score.py +++ b/input/cpsat-bench/evolve/shared/score.py @@ -154,8 +154,13 @@ def _score_cost(per_problem): ratios.append(time_r) wall_ratios.append(wall_r) else: - ratios.append(1e-6) - wall_ratios.append(1e-6) + # Non-decisive variant (UNKNOWN/INFEASIBLE on a decisive baseline). + # Use its REAL measured time_ratio instead of a 1e-6 sentinel — a + # variant that exhausted the timeout contributes its (slow) timeout + # ratio, not a fixed penalty. solved_rate (squared in combined) + # still drops, so a lost solve is penalized via that channel. + 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 diff --git a/input/cpsat-bench/problems.jsonl b/input/cpsat-bench/problems.jsonl index 37d873edf8..3d0f980fba 100644 --- a/input/cpsat-bench/problems.jsonl +++ b/input/cpsat-bench/problems.jsonl @@ -1,82 +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": 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"sufficient_assumptions_for_infeasibility": 0, "additional_solutions": 0}, "solution_info": "default_lp", "problem_sha256": "ff6697b1a9d57aeb43f770c91918c33c2b88b2ed07ceea158d351ecb9e4fe8ab", "applied_params_hash": "e3ddb5bb71ffa060136343b4797569f9ddd5d18e782107d83ebce533398612ff", "problem_filename": "ff6697b1a9d57aeb43f770c91918c33c2b88b2ed07ceea158d351ecb9e4fe8ab.cpsat.pb"} +{"solver": "cpsat", "path": "contracted", "features": {"num_variables": 4117, "num_bool": 2621, "num_int": 1496, "num_constraints": 52091, "constraint_breakdown": {"at_most_one": 244, "linear": 20481, "bool_or": 18365, "bool_and": 13001}}, "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": ["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 index daeba666cc..577e7b0dd8 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -203,8 +203,14 @@ if [ "$EXTRACT_ONLY" != "1" ]; then need_build=1 fi if [ "$need_build" = "1" ]; then - echo "[run_phase] sample json missing — running $BUILD_SAMPLES_SCRIPT first..." - python "$ROOT/$BUILD_SAMPLES_SCRIPT" + # small profile builds stage1+stage2 only (--small); large/default + # builds all four stages. Both emit stage1_large_sample.json. + build_args=() + if [ "$PROFILE" = "small" ]; then + build_args+=(--small) + fi + echo "[run_phase] sample json missing — running $BUILD_SAMPLES_SCRIPT ${build_args[*]}..." + python "$ROOT/$BUILD_SAMPLES_SCRIPT" "${build_args[@]+"${build_args[@]}"}" fi if [ -f "$ROOT/$REBASELINE_SCRIPT" ]; then From 0555709e1947eafd10f831f77b5c0309c7aad0bc Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 1 Jun 2026 09:54:00 +0900 Subject: [PATCH 37/42] [docs] update docs --- docs/openevolve-intro/index.html | 146 +++++++++++-------------- docs/openevolve-intro/slides.html | 176 ++++++++++++++---------------- 2 files changed, 146 insertions(+), 176 deletions(-) diff --git a/docs/openevolve-intro/index.html b/docs/openevolve-intro/index.html index f2054cbe58..34dc9a877a 100644 --- a/docs/openevolve-intro/index.html +++ b/docs/openevolve-intro/index.html @@ -709,7 +709,7 @@

    6. Z3 파라미터 최적화 — 실제 결과

    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 이득과 regression을 동시에 전달하는 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). + 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)

    @@ -720,128 +720,112 @@

    7.1 공통 실행 환경 (3 phase 동일)

    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(20) + 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: 큰 도약 (0.71 → 1.46)

    +

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

    - Phase1은 가장 큰 표면(search 전략 + subsolver 조합)을 단독으로 탐색한다. 초기 config는 탐색이 무거워 deterministic_time은 느리지만 branch/conflict 수는 적다(efficiency 2.25). + 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=0.7123  geomean_speedup=0.5436  efficiency=2.2518  (solved 10/10)
-winner : combined_score=1.4582  geomean_speedup=1.1786  efficiency=1.8948  (solved 20/20)
-         🌟 new best @ iter 1, 4, 31
    - -

    winner가 채택한 핵심 — feasibility 휴리스틱:

    - 
    GLOBAL_OVERRIDES = { "use_feasibility_pump": True }   # 작은/중간 문제 dtime 단축
-SIZE_BUCKETS[large] = { "use_feasibility_jump": True, "use_feasibility_pump": True }
-STAGE3_OVERRIDES = { "repair_hint": True, "hint_conflict_limit": 50 }
    + 
    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 17.9% 단축 (geomean_speedup 1.1786), stage4까지 통과해 20/20 OPTIMAL. 큰 탐색 공간이라 점수가 0.71 → 1.46으로 크게 도약. (LLM 생성 timeout 10회 — claude_code 3-retry 후 skip.) -
    -
    - 복선. winner가 켠 use_feasibility_jump/pump가 phase2 initial_program.py로 상속되며 곧바로 문제를 일으킨다. + 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 2 시작 — 상속이 깨졌다 (score 0.0000)

    +

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

    - Phase2(presolve/probing)는 phase1 winner를 상속한다. 그런데 초기 평가가 0점. phase1에서 켠 use_feasibility_jump=True가 num_search_workers=1 환경에서 사실상 유일 solver로 돌면서 한 문제에서 feasibility를 잃었다. + winner는 글로벌로 LP를 강하게 깔고, SIZE_BUCKETS로 크기별 정반대 강도를 적용한다:

    - 
    initial c69923ed: combined_score=0.0000  solved_rate=0.0000
-artifact.result_mismatch = {
-    "sha": "b5e191a8...", "baseline_result": "OPTIMAL", "got_result": "UNKNOWN"
-}
-artifact.suggestion = "Variant lost feasibility on a problem baseline reached optimal.
-    Revert params that disable a needed subsolver or shorten search."
    - -
    - 체이닝의 양날. phase 체이닝은 이득뿐 아니라 regression도 전달한다. 단 하나의 OPTIMAL→UNKNOWN이 solved_rate=0을 만들고, score 식의 solved_rate² 항이 combined_score를 0으로 떨군다. 하지만 evaluator가 돌려준 result_mismatch + suggestion artifact가 다음 iteration들의 단서가 된다. + 
    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 회복 — 안전하지만 느린 곡선

    +

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

    - 이후 iteration들은 "일단 다 풀리게" 만드는 보수적 변형으로 점진 회복한다. solved 10/10이지만 dtime은 baseline보다 느리다(speedup < 1). + Phase2(presolve/probing)는 phase1 winner를 상속한다. 이번엔 시작부터 깨끗 — 53/53 solved, regression 0.

    - 
    iter 1 (4a07c113): combined_score=0.8807  geomean_speedup=0.881   solved 10/10
-iter 3 (2fa91fef): combined_score=0.9771  geomean_speedup=0.901
-iter 9 (5e2691fb): combined_score=0.9905  geomean_speedup=0.946
    -
    - 국소 최적의 함정. regression은 사라졌지만(solved 10/10) 모두 geomean_speedup < 1.0 — baseline보다 dtime을 더 쓴다. LLM은 "안전한 0.9x"에 머물러 0점은 피했으나 phase1 수준(1.18)엔 못 미치고, stage1 gate를 못 넘어 stage2로도 진입하지 못한다. + 
    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 돌파 — iter 23, 근본 원인 진단 (1.4582)

    +

    7.5 Phase 2 통찰 — 더하기가 아니라 덜어내기

    - iter 23에서 LLM이 thinking 로그에 root cause를 적고 직접 고친다: + winning config는 phase1보다 더 단순하다. presolve 작업을 덜어내서 빨라졌다:

    -
    - "Phase1 winner가 use_feasibility_jump=True를 켠 듯하다. W=1에서는 이게 유일 solver로 돌아 LP-heavy 문제에서 optimality 증명 없이 FEASIBLE로 끝난다. 조기 종료를 유발하는 두 휴리스틱을 명시적으로 끈다." - 
    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 2, "symmetry_level": 1,
-    "use_feasibility_jump": False,   # ← root-cause fix
-    "use_feasibility_pump": False,
-    "stop_after_first_solution": False, "use_lns_only": False,
+    "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 = [
-    (50_000,  {probing2, cut_level1, add_mir_cuts, add_zero_half_cuts}),  # 작은 문제
-    (150_000, {probing2, cut_level1}),                                    # 중간
-    (inf,     {probing1, linearization_level1}),                         # 큰 LP-heavy
-]
    -
    - 돌파. stage1 gate 통과 → 전체 cascade(stage4, 20문제) 진입. combined_score=1.4582, geomean_speedup=1.1786 (+17.9%), efficiency=1.8948. phase1과 동일한 속도 수준을 더 안전한 config로 회복 — 즉 phase2의 본질은 "신규 이득"이 아니라 체이닝이 만든 regression을 진단·복구한 것. -
    - -

    7.6 Phase 2 실패들 — 좁은 표면의 대가

    +SIZE_BUCKETS[small <50k] = { "cp_model_probing_level": 0 } # probing 완전 off

    - presolve/probing 표면은 이미 잘 도는 baseline 위라 변형 여지가 좁다. 한 발만 잘못 디뎌도 OPTIMAL→UNKNOWN이 나고, 돌파 이후에도 다른 가지에서 같은 종류의 regression이 반복된다. + 흥미로운 점은 efficiency가 나빠졌는데도(1.09 → 0.91) 점수가 올랐다는 것. branch/conflict는 늘었지만 dtime이 크게 줄었기 때문이다:

    - 
    iter 25, 32: combined_score=0.0000
-             error="result regression on dd4063e9bc: baseline=OPTIMAL got=UNKNOWN"
-LLM 생성 timeout: iter 5,10,12,18,19,22,30,35  (claude_code 3-retry 후 skip)
    -
    - 그래도 수렴하는 이유. MAP-Elites + island가 0점 가지를 버리고, artifact의 result_mismatch가 LLM을 반복적으로 안전 영역으로 끌어와 iter 23 돌파를 가능케 했다. 점수만 줬다면 불가능한 인과적 디버깅 — artifact 피드백의 가치다. + 
    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).
    -

    7.7 Phase 3 — LP / cuts (W=8): 평탄한 표면

    +

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

    - Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠다. 시작부터 이미 baseline 수준(상속 config가 강함). + Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠다. 이미 강한 상속 config 위에서도 추가 이득을 짜낸다.

    - 
    initial c2e091f9: combined_score=0.9930  geomean_speedup=0.9930  (solved 10/10, W=8)
-winner  620d4019: combined_score=1.0225  geomean_speedup=0.9778  efficiency=1.1437
-         🌟 new best @ iter 3, 27
    -

    winner config (요약):

    - 
    GLOBAL: max_num_cuts=6000, root_lp_iterations=3500, symmetry_level=3,
-        interleave_search=True, share_glue_clauses=True, solution_pool_size=8
-SIZE_BUCKETS[small]: probing3 + linearization2 + cut_level2
-SIZE_BUCKETS[large]: root_lp_iterations=5000 + MIR/zero-half cuts
    + 
    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

    - 그런데 winner의 combined_score 1.0225는 speedup가 아니라 efficiency factor가 끌어올린 값이다: + iter 27에서 LLM이 num_violation_ls=1을 시도한다. dtime이 극적으로 줄지만 결과가 틀린다 — thinking 로그:

    - 
    combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
-              ≈   0.9778        × 1.1437^0.333
-              ≈   0.9778        × 1.046              ≈ 1.0225
    -
    - 한계 효용(diminishing returns). deterministic_time(0.978)은 오히려 baseline보다 2.2% 느리다. 줄어든 branch/conflict(efficiency 1.14)가 점수를 1 위로 들어올렸을 뿐, 실질 속도 개선이 아니다. 쉬운 이득은 phase1/2가 이미 가져갔고 LP/cuts 표면은 이 시점에 거의 평탄하다. score 숫자를 해석할 땐 어떤 항이 들어올렸는지 봐야 한다. +
    + "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 · W10.71231.45821.1786 (+17.9%)탐색 — 큰 도약
    2presolve/probing · W10.00001.45821.1786진단·복구 — root-cause fix
    3lp/cuts · W80.99301.02250.9778 (−2.2%)평탄 — 한계 효용
    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 이득과 regression을 함께 전달한다. phase2 시작 0점은 phase1이 켠 휴리스틱 탓 — artifact가 LLM의 root-cause 진단을 가능케 했다.
      2. -
    2. score 식 해석에 주의. phase3는 dtime<1인데도 efficiency factor가 combined_score를 1 위로 들어올린다. 숫자 하나만 보면 오해할 수 있다.
      3. -
    3. 모든 phase가 이득을 내진 않는다. phase3는 평탄 — 그게 정상이고, cascade는 "어디서 멈출지"를 보여준다.
      4. +
    4. 체이닝은 이득을 누적한다. 각 phase가 직전 best 위에서 추가로 짜냄 — phase1 LP/cuts → phase2 presolve → phase3 cut 미세조정.
      5. +
    5. 최적 방향이 표면마다 다르다. phase1은 LP/cuts를 추가, phase2는 presolve를 제거해 이득. LLM이 양방향 모두 탐색한다.
      6. +
    6. artifact가 안전을 보장한다. "빠르지만 UNKNOWN"인 변형을 점수가 아닌 결과 신호로 거부 (phase3 num_violation_ls). 점수만으로는 불가능한 인과적 디버깅.
    diff --git a/docs/openevolve-intro/slides.html b/docs/openevolve-intro/slides.html index b40a4cb522..7d8719497f 100644 --- a/docs/openevolve-intro/slides.html +++ b/docs/openevolve-intro/slides.html @@ -918,7 +918,7 @@

    공통 실행 환경 (3 phase 동일)

    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(20) + Cascade 평가stage1(10) → stage2(10) → [stage3 outlier] → stage4(64문제 · 53 비교가능) Phase 체이닝phaseN best → phase(N+1) initial_program.py로 상속 @@ -928,171 +928,157 @@

    공통 실행 환경 (3 phase 동일)

    -
    7.1 Phase1

    search / subsolvers — 큰 도약 (0.71 → 1.46)

    +
    7.1 Phase1

    search / subsolvers — LP·cuts로 +28.5%

    -

    Phase1은 가장 큰 표면(search 전략 + subsolver 조합)을 단독으로 탐색. 초기 config는 탐색이 무거워 dtime은 느리지만 branch/conflict는 적다.

    +

    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=0.7123  geomean_speedup=0.5436  efficiency=2.2518
-winner : combined_score=1.4582  geomean_speedup=1.1786  efficiency=1.8948  (solved 20/20)
-         🌟 new best @ iter 1, 4, 31
    - -

    winner가 채택한 핵심 — feasibility 휴리스틱:

    -
    GLOBAL_OVERRIDES = { "use_feasibility_pump": True }   # 작은/중간 문제 dtime 단축
-SIZE_BUCKETS[large] = { "use_feasibility_jump": True, "use_feasibility_pump": True }
-STAGE3_OVERRIDES = { "repair_hint": True, "hint_conflict_limit": 50 }
    +
    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 17.9% 단축 (geomean_speedup 1.1786), 20/20 OPTIMAL. 큰 탐색 공간 → 큰 점프. (LLM 생성 timeout 10회 — claude_code 3-retry 후 skip) + 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)
    -

    주의: winner가 켠 use_feasibility_jump/pump가 phase2로 상속되며 문제를 일으킨다 →

    7.1 Phase1
    21 / 35
    - +
    -
    7.2 Phase2 시작

    상속이 깨졌다 — score 0.0000

    +
    7.1 Phase1 config

    문제 크기별로 정반대 — layered tuning

    -

    Phase2(presolve/probing)는 phase1 winner를 상속. 그런데 초기 평가가 0점. phase1에서 켠 use_feasibility_jump=True가 W=1 단독 실행 시 한 문제에서 feasibility를 잃었다.

    +

    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
 
-
    initial c69923ed: combined_score=0.0000  solved_rate=0.0000
-artifact.result_mismatch = {
-    "sha": "b5e191a8...", "baseline_result": "OPTIMAL", "got_result": "UNKNOWN"
-}
-artifact.suggestion = "Variant lost feasibility on a problem baseline reached
-    optimal. Revert params that disable a needed subsolver or shorten search."
    
+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)
    -
    - 체이닝의 양날. phase 체이닝은 이득뿐 아니라 regression도 전달한다. 단 하나의 OPTIMAL→UNKNOWN이 solved_rate=0을 만들어 combined_score를 0으로 떨군다 (score 식의 solved_rate² 항). +
    + 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.
    -

    하지만 evaluator가 돌려준 result_mismatch + suggestion artifact가 다음 iteration들의 단서가 된다.

    -
    7.2 Phase2 시작
    22 / 35
    +
    7.1 Phase1 config
    22 / 35
    - +
    -
    7.3 Phase2 회복

    안전하지만 느린 회복

    +
    7.2 Phase2

    presolve — 깨끗한 상속, 단일 도약 (+51.7%)

    -

    이후 iteration들은 "일단 다 풀리게" 만드는 보수적 변형으로 점진 회복. solved 10/10이지만 dtime은 baseline보다 느림(speedup < 1).

    +

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

    -
    iter 1 (4a07c113): combined_score=0.8807  geomean_speedup=0.881   solved 10/10
-iter 3 (2fa91fef): combined_score=0.9771  geomean_speedup=0.901
-iter 9 (5e2691fb): combined_score=0.9905  geomean_speedup=0.946
    +
    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)
    -
    - 국소 최적의 함정. regression은 사라졌지만(solved 10/10) 모두 geomean_speedup < 1.0 — baseline보다 dtime을 더 쓴다. LLM은 "안전한 0.9x"에 머물러 0점은 피했지만 phase1 수준(1.18)엔 못 미침. stage1 gate를 못 넘어 stage2로도 못 간다. +
    + 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.
    -
    7.3 Phase2 회복
    23 / 35
    +
    7.2 Phase2
    23 / 35
    - +
    -
    7.4 Phase2 돌파

    iter 23 — 근본 원인 진단으로 돌파

    +
    7.3 Phase2 통찰

    더하기가 아니라 덜어내기

    -

    iter 23에서 LLM이 thinking 로그에 root cause를 적고 직접 고친다:

    -
    - "Phase1 winner가 use_feasibility_jump=True를 켠 듯. W=1에서는 이게 유일 solver로 돌아 LP-heavy 문제에서 optimality 증명 없이 FEASIBLE로 끝난다. 두 early-exit 휴리스틱을 명시적으로 끈다." -
    +

    winning config는 phase1보다 더 단순하다. presolve 작업을 덜어내서 빨라졌다:

    GLOBAL_OVERRIDES = {
-    "cp_model_probing_level": 2, "symmetry_level": 1,
-    "use_feasibility_jump": False,   # ← root-cause fix
-    "use_feasibility_pump": False,
-    "stop_after_first_solution": False, "use_lns_only": False,
+    "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=probing2+MIR/zero-half cuts, mid=probing2+cut1, large=probing1+lin1
    +SIZE_BUCKETS[small <50k] = { "cp_model_probing_level": 0 } # probing 완전 off +STAGE3_OVERRIDES = {} -
    - 돌파. stage1 gate 통과 → 전체 cascade(stage4, 20문제) 진입. combined_score=1.4582, geomean_speedup=1.1786 (+17.9%). phase1과 동일 수준을 더 안전한 config로 회복 — 체이닝이 만든 regression을 진단·복구한 것. +
    + 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.
    -
    7.4 Phase2 돌파
    24 / 35
    +
    7.3 Phase2 통찰
    24 / 35
    - +
    -
    7.5 Phase2 실패

    돌파 전후의 실패들

    +
    7.4 score 분해

    efficiency 희생, speed 취득

    -

    좁은 변형 공간에서 한 발만 잘못 디뎌도 OPTIMAL→UNKNOWN. 돌파 이후에도 다른 가지에서 같은 종류의 regression이 반복.

    +

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

    -
    iter 25, 32: combined_score=0.0000
-             error="result regression on dd4063e9bc: baseline=OPTIMAL got=UNKNOWN"
-LLM 생성 timeout: iter 5,10,12,18,19,22,30,35  (claude_code 3-retry 후 skip)
    +
    combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
+              ≈   1.5172        × 0.9119^0.333        × 1.0²
+              ≈   1.5172        × 0.969                       ≈ 1.4713
    -
    - 왜 0점이 자꾸 나오나. presolve/probing 표면은 이미 잘 도는 baseline 위라 변형 여지가 좁다. 그래도 MAP-Elites + island가 0점 가지를 버리고, artifact의 result_mismatch가 LLM을 안전 영역으로 끌어와 iter 23 돌파를 가능케 했다. +
    + 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)
    -
    7.5 Phase2 실패
    25 / 35
    +
    7.4 score 분해
    25 / 35
    - +
    -
    7.6 Phase3

    lp / cuts (W=8) — 평탄한 표면

    +
    7.5 Phase3

    lp / cuts (W=8) — +11.5% 추가

    -

    Phase3는 phase1+2 winner를 상속하고 8-worker로 LP/cuts 표면을 튠. 시작부터 이미 baseline 수준 (상속 config가 강함).

    +

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

    -
    initial c2e091f9: combined_score=0.9930  geomean_speedup=0.9930  (solved 10/10, W=8)
-winner  620d4019: combined_score=1.0225  geomean_speedup=0.9778  efficiency=1.1437
-         🌟 new best @ iter 3, 27
    +
    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)
    -

    winner config (요약):

    -
    GLOBAL: max_num_cuts=6000, root_lp_iterations=3500, symmetry_level=3,
-        interleave_search=True, share_glue_clauses=True, solution_pool_size=8
-SIZE_BUCKETS[small]: probing3 + linearization2 + cut_level2
-SIZE_BUCKETS[large]: root_lp_iterations=5000 + MIR/zero-half cuts
    +

    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. 8개 워커 portfolio에 무거운 LP/cuts를 더해도 geomean_speedup=0.9778 — dtime은 오히려 2.2% 느려짐. + 관찰. 8-worker portfolio 위에서도 deterministic_time 11.5% 추가 단축. (이 run은 iter 27에서 중단 — best 추출 전 best-so-far 기준)
    -
    7.6 Phase3
    26 / 35
    +
    7.5 Phase3
    26 / 35
    - +
    -
    7.7 Phase3 해석

    score > 1 인데 왜 dtime은 느린가

    +
    7.6 Phase3 안전장치

    "빠르지만 틀린" 변형을 artifact로 거부

    -

    winner의 combined_score 1.0225는 speedup가 아니라 efficiency factor가 끌어올린 값:

    - -
    combined_score = geomean_speedup × efficiency^STATS_WEIGHT × solved_rate²
-              ≈   0.9778        × 1.1437^0.333
-              ≈   0.9778        × 1.046              ≈ 1.0225
    - -

    즉 dtime(0.978)은 baseline보다 느린데, branch/conflict가 줄어든 efficiency(1.14)가 점수를 1 위로 들어올린 것.

    - -
    - 한계 효용(diminishing returns). 쉬운 이득은 phase1/2가 이미 가져갔다. LP/cuts 표면은 이 시점에 거의 평탄 — 점수 1.02는 "실질 속도 개선"이 아니다. score 숫자를 해석할 때 어떤 항이 들어올렸는지 봐야 함. +

    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."
    -

    artifact 피드백 + 분해 가능한 score = 점수만 봤을 때 놓칠 인과를 드러냄.

    +
    + 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. +
    -
    7.7 Phase3 해석
    27 / 35
    +
    7.6 Phase3 안전장치
    27 / 35
    -
    7.8 종합

    3-Phase가 보여준 동작 양상

    +
    7.7 종합

    3-Phase가 보여준 동작 양상

    - + - - - + + +
    Phase표면 / Winitialwinnergeomean_speedup양상
    Phase표면 / Winitialwinnergeomean_speedup (dtime)양상
    1search/subsolvers · W10.71231.45821.1786 (+17.9%)탐색 — 큰 도약
    2presolve/probing · W10.00001.45821.1786진단·복구 — root-cause fix
    3lp/cuts · W80.99301.02250.9778 (−2.2%)평탄 — 한계 효용
    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 이득과 regression을 함께 전달. phase2 시작 0점은 phase1이 켠 휴리스틱 탓 — artifact가 LLM의 root-cause 진단을 가능케 함.
      2. -
    2. score 식 해석 주의. phase3는 dtime<1인데 efficiency factor가 score를 1 위로 들어올림.
      3. -
    3. 모든 phase가 이득을 내진 않는다. phase3는 평탄 — cascade가 "어디서 멈출지"를 보여줌.
      4. +
    4. 체이닝은 이득을 누적. 각 phase가 직전 best 위에서 추가로 짜냄 — phase1 LP/cuts → phase2 presolve → phase3 cut 미세조정.
      5. +
    5. 최적 방향이 표면마다 다르다. phase1은 LP/cuts를 추가, phase2는 presolve를 제거해 이득. LLM이 양방향 모두 탐색.
      6. +
    6. artifact가 안전을 보장. "빠르지만 UNKNOWN"인 변형을 점수가 아닌 결과 신호로 거부 (phase3 num_violation_ls).
    -
    7.8 종합
    28 / 35
    +
    7.7 종합
    28 / 35
    From b9e60ef61882a334c8b17e3537a4025ce9a082b7 Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 1 Jun 2026 09:54:20 +0900 Subject: [PATCH 38/42] [fix] extract final solutions --- input/cpsat-bench/evolve/final_program.py | 305 ++++++++++++++++++ input/cpsat-bench/evolve/materialize_final.py | 224 +++++++++++++ .../evolve/phase4_unified/initial_program.py | 125 ++++--- 3 files changed, 607 insertions(+), 47 deletions(-) create mode 100644 input/cpsat-bench/evolve/final_program.py create mode 100644 input/cpsat-bench/evolve/materialize_final.py 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/materialize_final.py b/input/cpsat-bench/evolve/materialize_final.py new file mode 100644 index 0000000000..9a8a07ca60 --- /dev/null +++ b/input/cpsat-bench/evolve/materialize_final.py @@ -0,0 +1,224 @@ +""" +Materialize a standalone final_program.py from the phase5 winner plus all +inherited prior-phase winners. + +Inputs: + shared/baseline_params.py BASELINE dict + shared/phase4_best.json inherited GLOBAL_OVERRIDES (1+2+3 unified) + shared/phase4_buckets.json inherited SIZE_BUCKETS + shared/phase4_stage3.json inherited STAGE3_OVERRIDES + shared/has_objective_cache.json SHA set used by needs_objective gating + phase5_custom_subsolvers/openevolve_output/best/best_program.py + CUSTOM_SUBSOLVERS, STAGE3_CUSTOM_SUBSOLVERS (Python literals only) + +Output: + final_program.py — self-contained: no JSON loads, no relative imports. + Exposes BASELINE/GLOBAL_OVERRIDES/SIZE_BUCKETS/ + STAGE3_OVERRIDES/CUSTOM_SUBSOLVERS/ + STAGE3_CUSTOM_SUBSOLVERS/PHASE_LOCKED constants and + get_params(problem=None, stage=None) using the exact + phase5 merge logic. + +Usage: + python materialize_final.py [--output PATH] [--phase5-best PATH] +""" +import argparse +import importlib.util +import json +import pathlib +import pprint +import sys +import textwrap + +_HERE = pathlib.Path(__file__).resolve().parent +_SHARED = _HERE / "shared" +_PHASE5_BEST = _HERE / "phase5_custom_subsolvers/openevolve_output/best/best_program.py" +_DEFAULT_OUT = _HERE / "final_program.py" + + +def _load_phase5_specs(phase5_path): + """Import phase5 best_program just to read CUSTOM_SUBSOLVERS and + STAGE3_CUSTOM_SUBSOLVERS. Those are static literals so the snapshot's + broken _SHARED relative path does not matter — the JSON loads that fail + inside the module only affect _PHASE4 / _PHASE4_BUCKETS / _PHASE4_STAGE3, + which we re-read directly from shared/ here.""" + spec = importlib.util.spec_from_file_location("phase5_best", phase5_path) + module = importlib.util.module_from_spec(spec) + sys.path.insert(0, str(_SHARED)) + spec.loader.exec_module(module) + return module.CUSTOM_SUBSOLVERS, module.STAGE3_CUSTOM_SUBSOLVERS + + +def _format_buckets(buckets): + 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) + + +_TEMPLATE = '''\ +""" +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_rel} +""" + +BASELINE = {baseline} + +# Inherited from shared/phase4_best.json (union of phase1+2+3 winners + phase4 +# unified pass). +GLOBAL_OVERRIDES = {global_overrides} + +# Inherited from shared/phase4_buckets.json. Selected by num_constraints +# (first bucket whose upper > num_constraints wins). +SIZE_BUCKETS = {size_buckets} + +# 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 = {stage3_overrides} + +# Phase5 custom subsolvers (always-on, gated by needs_objective / +# min_constraints / max_constraints). +CUSTOM_SUBSOLVERS = {custom_subsolvers} + +# Phase5 stage3-only extra custom subsolvers (same dead-code caveat as +# STAGE3_OVERRIDES). +STAGE3_CUSTOM_SUBSOLVERS = {stage3_custom_subsolvers} + +# 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({objective_shas}) + +# 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 +''' + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) + ap.add_argument("--output", default=str(_DEFAULT_OUT), + help=f"output path (default: {_DEFAULT_OUT.relative_to(_HERE)})") + ap.add_argument("--phase5-best", default=str(_PHASE5_BEST), + help="phase5 best_program.py path") + args = ap.parse_args() + + sys.path.insert(0, str(_SHARED)) + from baseline_params import BASELINE # noqa: E402 + + phase4_best = json.loads((_SHARED / "phase4_best.json").read_text()) + phase4_buckets_raw = json.loads((_SHARED / "phase4_buckets.json").read_text()) + phase4_buckets = [(None if u is None else u, ov) for u, ov in phase4_buckets_raw] + phase4_stage3 = json.loads((_SHARED / "phase4_stage3.json").read_text()) + + obj_cache_path = _SHARED / "has_objective_cache.json" + objective_shas = set(json.loads(obj_cache_path.read_text()).get("with_objective") or []) + + custom_subsolvers, stage3_custom_subsolvers = _load_phase5_specs( + pathlib.Path(args.phase5_best) + ) + + rendered = _TEMPLATE.format( + phase5_rel=pathlib.Path(args.phase5_best).resolve().relative_to(_HERE), + baseline=pprint.pformat(dict(BASELINE), width=100, sort_dicts=True), + global_overrides=pprint.pformat(phase4_best, width=100, sort_dicts=True), + size_buckets=_format_buckets(phase4_buckets), + stage3_overrides=pprint.pformat(phase4_stage3, width=100, sort_dicts=True), + custom_subsolvers=pprint.pformat(custom_subsolvers, width=100, sort_dicts=True), + stage3_custom_subsolvers=pprint.pformat(stage3_custom_subsolvers, width=100, sort_dicts=True), + objective_shas=pprint.pformat(sorted(objective_shas), width=100), + ) + + out = pathlib.Path(args.output) + out.write_text(rendered) + print(f"wrote {out.relative_to(_HERE)} " + f"({len(BASELINE)} baseline + {len(phase4_best)} global + " + f"{len(phase4_buckets)} buckets + {len(phase4_stage3)} stage3 + " + f"{len(custom_subsolvers)} custom subsolvers + " + f"{len(stage3_custom_subsolvers)} stage3 custom subsolvers)") + + +if __name__ == "__main__": + main() diff --git a/input/cpsat-bench/evolve/phase4_unified/initial_program.py b/input/cpsat-bench/evolve/phase4_unified/initial_program.py index 17708d4ae1..eb1a90c693 100644 --- a/input/cpsat-bench/evolve/phase4_unified/initial_program.py +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -36,65 +36,96 @@ # EVOLVE-BLOCK-START # Auto-generated by prepare_phase_unified.py from union of prior phase winners. -GLOBAL_OVERRIDES = {'clause_cleanup_period': 20000, - 'cp_model_probing_level': 2, - 'cut_cleanup_target': 2000, +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': 15000, + 'clause_cleanup_target': 50000, + 'cp_model_probing_level': 1, 'cut_level': 1, - 'diversify_lns_params': True, + 'exploit_best_solution': True, 'interleave_search': True, - 'max_consecutive_inactive_count': 150, - 'max_num_cuts': 6000, + 'linearization_level': 1, + 'max_num_cuts': 3000, + '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': False, 'probing_num_combinations_limit': 40000, - 'root_lp_iterations': 3500, + 'root_lp_iterations': 2500, 'share_glue_clauses': True, - 'share_glue_clauses_dtime': 0.4, - 'solution_pool_size': 8, - 'stop_after_first_solution': False, - 'symmetry_level': 3, - 'use_feasibility_jump': False, - 'use_feasibility_pump': False, - 'use_lns_only': False} + 'share_glue_clauses_dtime': 0.5, + 'symmetry_level': 1} SIZE_BUCKETS = [ - (50000, {'add_mir_cuts': True, - 'add_zero_half_cuts': True, - 'cp_model_probing_level': 3, - 'cut_level': 2, - 'linearization_level': 2, - 'max_num_cuts': 8000, - 'merge_at_most_one_work_limit': 500000000.0, - 'presolve_inclusion_work_limit': 300000000, - 'use_feasibility_jump': False, - 'use_feasibility_pump': True}), - (150000, {'cp_model_probing_level': 2, 'cut_level': 1, 'use_feasibility_jump': False}), - (float('inf'), {'add_mir_cuts': True, - 'add_zero_half_cuts': True, - 'cp_model_probing_level': 1, - 'cut_level': 2, - 'linearization_level': 2, - 'max_num_cuts': 8000, - 'root_lp_iterations': 5000, - 'use_feasibility_jump': True, - 'use_feasibility_pump': True}), + (50000, {'cp_model_probing_level': 3, + 'cut_level': 0, + 'interleave_search': False, + 'linearization_level': 1, + 'max_num_cuts': 0, + 'merge_at_most_one_work_limit': 1000000, + 'presolve_inclusion_work_limit': 50000000, + 'presolve_use_bva': False, + 'probing_num_combinations_limit': 5000, + 'root_lp_iterations': 500, + 'search_branching': 0, + 'symmetry_level': 2}), + (150000, {'add_clique_cuts': False, + 'add_mir_cuts': True, + 'add_zero_half_cuts': False, + 'cp_model_probing_level': 2, + 'cut_cleanup_target': 1500, + 'cut_level': 1, + 'exploit_relaxation_solution': True, + 'linearization_level': 1, + 'max_num_cuts': 4000, + 'root_lp_iterations': 3000, + '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}), ] -STAGE3_OVERRIDES = {'add_mir_cuts': True, - 'add_zero_half_cuts': True, +STAGE3_OVERRIDES = {'clause_cleanup_lbd_bound': 8, + 'clause_cleanup_ordering': 1, + 'clause_cleanup_period': 22000, + 'clause_cleanup_target': 150000, 'cp_model_probing_level': 3, - 'cut_level': 2, - 'extra_subsolvers': ['lb_tree_search'], - 'hint_conflict_limit': 50, - 'inprocessing_dtime_ratio': 0.25, - 'linearization_level': 2, - 'max_num_cuts': 10000, - 'num_violation_ls': 3, - 'optimize_with_lb_tree_search': True, - 'presolve_bve_threshold': 1000, + 'cut_active_count_decay': 0.9, + 'cut_level': 1, + 'diversify_lns_params': True, + 'exploit_all_precedences': True, + 'extra_subsolvers': ['core', 'quick_restart', 'reduced_costs', 'lb_tree_search'], + 'inprocessing_dtime_ratio': 0.4, + 'inprocessing_minimization_dtime': 2.0, + 'inprocessing_probing_dtime': 2.0, + 'linearization_level': 1, + 'max_cut_rounds_at_level_zero': 3, + 'max_num_cuts': 4000, + 'merge_at_most_one_work_limit': 1000000000, + 'merge_no_overlap_work_limit': 2000000000000.0, + 'polish_lp_solution': True, + 'presolve_inclusion_work_limit': 1000000000, + 'probing_num_combinations_limit': 80000, 'repair_hint': True, - 'root_lp_iterations': 6000, - 'symmetry_level': 2} + 'root_lp_iterations': 8000, + 'solution_pool_size': 5, + 'symmetry_level': 3, + 'use_feasibility_jump': True, + 'use_feasibility_pump': True, + 'use_strong_propagation_in_disjunctive': True} # EVOLVE-BLOCK-END From 7173f9e026ccf8879d06e9fa7049d9b6f049c243 Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 1 Jun 2026 12:51:18 +0900 Subject: [PATCH 39/42] [refactor] unify cpsat + z3 pipelines into shared _lib platform MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lift per-bench evaluator / sampler / rebaseline / scoring / phase extraction / unified-phase materialization into reusable _lib modules. Per-bench surface shrinks to four hand-edited files (config.yaml, params.json, adapter.py, _solve_worker.py) plus phase initial_program.py modules. All orchestration commands route via `python -m _lib. `; no per-bench wrapper scripts. New _lib modules: bench_paths, params_catalog, averaging, scorer, sampler, rebaseline, self_test, evaluator_core, evaluator_entry, final_verify, finalize. Extended extract_best + prepare_phase + load_bench_config to support CLI invocation by bench name. Per-bench changes: - shared/ subdirs removed entirely (worker moved to evolve/ root). - baseline_params.py / score.py / evaluator.py / runtime.py / _runner.py / build_samples.py / rebaseline_local.py / extract_best.py / prepare_phase*.py / final_verify.py wrappers deleted (logic now in _lib). - New adapter.py exposes solver-specific constants (DECISIVE_RESULTS, KEY_STATS, STATUS_FIELD, WORKERS_KEY, SCORE_MODE, STATS_WEIGHTS, get_problem_size). - New params.json: rich catalog (defaults + locked + grouped params with type/range/desc) replaces BASELINE/LOCKED dicts. - config.yaml gains bench.clustering, bench.evaluation, bench.adapter / params_catalog / worker_path / unified_dict_name. - z3 extras (validate_keys.py, probe_cli_keys.py, _probe_parallel.py, verify_stage1_baseline.py) and cpsat extras (benchmark_final.py, materialize_final.py) removed. - Phase initial_program.py modules: - imports BASELINE from params_catalog - EVOLVE-BLOCK reset to empty (clean slate) - robust bench-root resolution via OPENEVOLVE_BENCH_ROOT env + walk-up fallback (works after openevolve copies program to /tmp) run_phase.sh: invokes _lib modules directly, exports OPENEVOLVE_BENCH_ROOT, passes _lib/evaluator_entry.py as evaluator, auto-runs _lib.finalize after the last phase to emit /evolve/final_program.py (self-contained — all _load_prev*() calls inlined as literal dicts so no cache/ dependency at runtime). Behavior unified across solvers: - 10-run averaging is the standard (configurable via bench.evaluation.repeats). - Clustering driven by bench.clustering (kmeans / quintile / thresholds); z3 clusters by features.num_hard_constraints, cpsat by features.num_constraints. - Score mode pluggable: `cost` (cpsat: dtime + cost_ratio) or `speedup` (z3: wall-clock). - SIZE_BUCKETS / STAGE3_OVERRIDES are opt-in via bench.evaluation.enable_size_buckets / enable_outlier_stage. - LLM param validation runs against the catalog before subprocess spawn — unknown / range-violating keys surface as invalid_param without a solver run. Generated stage samples + local_baseline now live in /evolve/cache/ (safe to delete; regenerated on first run). Co-Authored-By: Claude Opus 4.7 (1M context) --- input/README.md | 242 ++-- input/_lib/averaging.py | 52 + input/_lib/bench_paths.py | 116 ++ input/_lib/evaluator_core.py | 476 +++++++ input/_lib/evaluator_entry.py | 29 + input/_lib/extract_best.py | 75 +- input/_lib/final_verify.py | 98 ++ input/_lib/finalize.py | 191 +++ input/_lib/load_bench_config.py | 25 +- input/_lib/params_catalog.py | 216 +++ input/_lib/prepare_phase.py | 80 +- input/_lib/rebaseline.py | 290 ++++ input/_lib/sampler.py | 285 ++++ input/_lib/scorer.py | 255 ++++ input/_lib/self_test.py | 128 ++ input/cpsat-bench/evolve/README.md | 402 ++---- ...cpsat_solve_worker.py => _solve_worker.py} | 0 input/cpsat-bench/evolve/adapter.py | 38 + input/cpsat-bench/evolve/benchmark_final.py | 439 ------ input/cpsat-bench/evolve/build_samples.py | 607 -------- .../evolve/cache/stage1_sample.json | 79 + .../evolve/cache/stage2_sample.json | 79 + .../evolve/cache/stage3_sample.json | 44 + .../evolve/cache/stage4_sample.json | 149 ++ input/cpsat-bench/evolve/config.yaml | 50 +- input/cpsat-bench/evolve/extract_best.py | 27 - input/cpsat-bench/evolve/final_verify.py | 334 ----- input/cpsat-bench/evolve/materialize_final.py | 224 --- input/cpsat-bench/evolve/params.json | 235 +++ .../evolve/phase1_search/initial_program.py | 91 +- .../evolve/phase2_presolve/initial_program.py | 117 +- .../evolve/phase3_lp_cuts/initial_program.py | 131 +- .../evolve/phase4_unified/initial_program.py | 148 +- .../initial_program.py | 173 +-- .../evolve/prepare_phase_unified.py | 27 - input/cpsat-bench/evolve/rebaseline_local.py | 424 ------ .../evolve/shared/baseline_params.py | 149 -- .../evolve/shared/cpsat_params_reference.md | 353 ----- .../cpsat-bench/evolve/shared/cpsat_runner.py | 24 - input/cpsat-bench/evolve/shared/evaluator.py | 725 ---------- input/cpsat-bench/evolve/shared/runtime.py | 26 - input/cpsat-bench/evolve/shared/score.py | 222 --- input/run_phase.sh | 297 +--- input/z3-bench/evolve/OPENEVOLVE_INTRO.md | 8 +- input/z3-bench/evolve/README.md | 349 ++--- input/z3-bench/evolve/_probe_parallel.py | 74 - .../_z3_solve_worker.py => _solve_worker.py} | 0 input/z3-bench/evolve/adapter.py | 33 + input/z3-bench/evolve/build_samples.py | 272 ---- .../z3-bench/evolve/cache/stage1_sample.json | 44 + .../z3-bench/evolve/cache/stage2_sample.json | 44 + .../z3-bench/evolve/cache/stage3_sample.json | 44 + .../z3-bench/evolve/cache/stage4_sample.json | 149 ++ input/z3-bench/evolve/config.yaml | 35 +- input/z3-bench/evolve/extract_best.py | 23 - input/z3-bench/evolve/final_verify.py | 216 --- input/z3-bench/evolve/params.json | 194 +++ .../evolve/phase1_opt_sls/initial_program.py | 77 +- .../evolve/phase2_sat/initial_program.py | 189 +-- .../evolve/phase3_smt/initial_program.py | 177 +-- .../evolve/phase4_unified/initial_program.py | 264 +--- input/z3-bench/evolve/prepare_phase4.py | 21 - input/z3-bench/evolve/probe_cli_keys.py | 105 -- input/z3-bench/evolve/rebaseline_local.py | 191 --- .../z3-bench/evolve/shared/baseline_params.py | 164 --- input/z3-bench/evolve/shared/evaluator.py | 443 ------ input/z3-bench/evolve/shared/runtime.py | 25 - input/z3-bench/evolve/shared/score.py | 137 -- .../z3-bench/evolve/shared/stage1_sample.json | 48 - .../z3-bench/evolve/shared/stage2_sample.json | 48 - .../z3-bench/evolve/shared/stage3_sample.json | 48 - .../z3-bench/evolve/shared/stage4_sample.json | 168 --- input/z3-bench/evolve/shared/z3_runner.py | 27 - .../z3-bench/evolve/shared/z3_valid_keys.json | 1266 ----------------- input/z3-bench/evolve/validate_keys.py | 160 --- .../z3-bench/evolve/verify_stage1_baseline.py | 123 -- 76 files changed, 4288 insertions(+), 9050 deletions(-) create mode 100644 input/_lib/averaging.py create mode 100644 input/_lib/bench_paths.py create mode 100644 input/_lib/evaluator_core.py create mode 100644 input/_lib/evaluator_entry.py create mode 100644 input/_lib/final_verify.py create mode 100644 input/_lib/finalize.py create mode 100644 input/_lib/params_catalog.py create mode 100644 input/_lib/rebaseline.py create mode 100644 input/_lib/sampler.py create mode 100644 input/_lib/scorer.py create mode 100644 input/_lib/self_test.py rename input/cpsat-bench/evolve/{shared/_cpsat_solve_worker.py => _solve_worker.py} (100%) create mode 100644 input/cpsat-bench/evolve/adapter.py delete mode 100644 input/cpsat-bench/evolve/benchmark_final.py delete mode 100644 input/cpsat-bench/evolve/build_samples.py create mode 100644 input/cpsat-bench/evolve/cache/stage1_sample.json create mode 100644 input/cpsat-bench/evolve/cache/stage2_sample.json create mode 100644 input/cpsat-bench/evolve/cache/stage3_sample.json create mode 100644 input/cpsat-bench/evolve/cache/stage4_sample.json delete mode 100644 input/cpsat-bench/evolve/extract_best.py delete mode 100644 input/cpsat-bench/evolve/final_verify.py delete mode 100644 input/cpsat-bench/evolve/materialize_final.py create mode 100644 input/cpsat-bench/evolve/params.json delete mode 100644 input/cpsat-bench/evolve/prepare_phase_unified.py delete mode 100644 input/cpsat-bench/evolve/rebaseline_local.py delete mode 100644 input/cpsat-bench/evolve/shared/baseline_params.py delete mode 100644 input/cpsat-bench/evolve/shared/cpsat_params_reference.md delete mode 100644 input/cpsat-bench/evolve/shared/cpsat_runner.py delete mode 100644 input/cpsat-bench/evolve/shared/evaluator.py delete mode 100644 input/cpsat-bench/evolve/shared/runtime.py delete mode 100644 input/cpsat-bench/evolve/shared/score.py delete mode 100644 input/z3-bench/evolve/_probe_parallel.py rename input/z3-bench/evolve/{shared/_z3_solve_worker.py => _solve_worker.py} (100%) create mode 100644 input/z3-bench/evolve/adapter.py delete mode 100644 input/z3-bench/evolve/build_samples.py create mode 100644 input/z3-bench/evolve/cache/stage1_sample.json create mode 100644 input/z3-bench/evolve/cache/stage2_sample.json create mode 100644 input/z3-bench/evolve/cache/stage3_sample.json create mode 100644 input/z3-bench/evolve/cache/stage4_sample.json delete mode 100644 input/z3-bench/evolve/extract_best.py delete mode 100644 input/z3-bench/evolve/final_verify.py create mode 100644 input/z3-bench/evolve/params.json delete mode 100644 input/z3-bench/evolve/prepare_phase4.py delete mode 100644 input/z3-bench/evolve/probe_cli_keys.py delete mode 100644 input/z3-bench/evolve/rebaseline_local.py delete mode 100644 input/z3-bench/evolve/shared/baseline_params.py delete mode 100644 input/z3-bench/evolve/shared/evaluator.py delete mode 100644 input/z3-bench/evolve/shared/runtime.py delete mode 100644 input/z3-bench/evolve/shared/score.py delete mode 100644 input/z3-bench/evolve/shared/stage1_sample.json delete mode 100644 input/z3-bench/evolve/shared/stage2_sample.json delete mode 100644 input/z3-bench/evolve/shared/stage3_sample.json delete mode 100644 input/z3-bench/evolve/shared/stage4_sample.json delete mode 100644 input/z3-bench/evolve/shared/z3_runner.py delete mode 100644 input/z3-bench/evolve/shared/z3_valid_keys.json delete mode 100644 input/z3-bench/evolve/validate_keys.py delete mode 100644 input/z3-bench/evolve/verify_stage1_baseline.py diff --git a/input/README.md b/input/README.md index ca56bcbf47..e2db77cefa 100644 --- a/input/README.md +++ b/input/README.md @@ -1,120 +1,174 @@ -# input/ — Benchmark Pipelines + Shared Lib +# input/ — Solver-Parameter Tuning Pipelines Each `/` directory holds one OpenEvolve solver-parameter-tuning -pipeline. Common scaffolding lives under `_lib/` and is wired into each -bench via thin per-bench wrappers (keeps bench code <100 LOC for the -boilerplate parts). +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 # single entry: ./run_phase.sh [] [flags] -├── _lib/ # shared library (importable as `_lib.*`) -│ ├── runtime.py # parallel_solvers / cascade_threshold / core_range -│ ├── subprocess_runner.py # run_solver(worker, problem, params, timeout, ...) -│ ├── extract_best.py # main(root, shared, phase_dirs) — CLI core -│ ├── prepare_phase.py # main(root, shared, prior_phases, unified_file) -│ └── load_bench_config.py # parse `bench:` section of config.yaml → bash exports -├── z3-bench/ -│ ├── raw-data/ # .smt2 + ____seed0.meta.jsonl +├── 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 # openevolve + `bench:` (phases / scripts / solver check) -│ └── ... # baseline_params, evaluator, phases, etc. -└── cpsat-bench/ - ├── raw-data/ # .cpsat.pb + ____seed0.meta.jsonl +│ ├── 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 # same - └── ... + ├── config.yaml + ├── params.json + ├── adapter.py + ├── _solve_worker.py # subprocess entry (z3 Python binding) + ├── phase{1..4}_*/initial_program.py + └── cache/ ``` -## _lib/ — what's shared - -| module | purpose | per-bench wrapper | -|---|---|---| -| `_lib/runtime.py` | parse `OPENEVOLVE_PARALLEL_SOLVERS`, `OPENEVOLVE_CORE_RANGE`, `cascade_thresholds` from env + config.yaml | `/evolve/shared/runtime.py` (binds config path, re-exports) | -| `_lib/subprocess_runner.py` | spawn worker subprocess, taskset pin, timeout grace, parse stdout JSON | `/evolve/shared/_runner.py` (binds WORKER path, names `run_`) | -| `_lib/extract_best.py` | argparse + load best_program.py + write `phaseN_best.json` | `/evolve/extract_best.py` (passes `PHASE_DIRS` dict) | -| `_lib/prepare_phase.py` | merge `phaseN_best.json` files, rewrite unified phase's EVOLVE-BLOCK | `/evolve/prepare_phase_unified.py` (passes `PRIOR_PHASES` list) | -| `input/run_phase.sh` | single user-facing entry: `./run_phase.sh [] [--pin] [--extract-only] [--iterations]`. Phase optional → runs ALL phases sequentially. Reads `bench:` section of `/evolve/config.yaml` via `_lib/load_bench_config.py`. | `/evolve/config.yaml` — `bench:` key with `phases[].dir/iters`, `unified_prepare_script`, `solver_check_cmd`, `solver_install_hint` | - -## Per-bench files NOT shared - -Stay per-bench because each is solver- or dataset-specific: - -- `shared/baseline_params.py` — `BASELINE` + `LOCKED` dicts (recorded params) -- `shared/__solve_worker.py` — solver Python binding glue -- `shared/score.py` — score mode (`decision` / `cost` / `time-only`) -- `shared/evaluator.py` — cascade scaffold + decisive-result set + key stats -- `build_samples.py` — meta.jsonl field paths -- `rebaseline_local.py` — local baseline measurement -- `phase{N}_/initial_program.py` — phase namespace + initial overrides -- `config.yaml` — bench knobs + LLM system message - -## Wiring pattern (per-bench wrapper) - -Each wrapper: -1. Computes `_INPUT_DIR` (relative to its own file). -2. Inserts it into `sys.path` so `from _lib import ...` works. -3. Calls / re-exports the shared function with bench-specific args. - -Example — `/evolve/shared/runtime.py`: - -```python -import pathlib, sys -_HERE = pathlib.Path(__file__).resolve().parent -_CONFIG_YAML = _HERE.parent / "config.yaml" -_INPUT_DIR = _HERE.parents[2] -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) -from _lib import runtime as _rt - -core_range = _rt.core_range -def parallel_solvers(default=1): return _rt.parallel_solvers(_CONFIG_YAML, default=default) -def cascade_threshold(i, d): return _rt.cascade_threshold(_CONFIG_YAML, i, d) -``` - -Same pattern for `_runner.py`, `extract_best.py`, `prepare_phase_unified.py`. - -## Adding a new bench +## Per-bench surface (the only hand-edited files) -1. Make `input//raw-data/` and populate it (e.g. `load_script.sh`). -2. Scaffold `/evolve/` using the [openevolve-pipeline skill](../.claude/skills/openevolve-pipeline/SKILL.md). -3. For each shared module, copy a per-bench wrapper from `z3-bench/` or - `cpsat-bench/` and adjust the bench-specific bits (worker path, phase - dirs, config path). +| 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 (work in both benches) +## Environment knobs | variable | use | |---|---| -| `OPENEVOLVE_PARALLEL_SOLVERS` | concurrent solver subprocesses per stage (cores 1..N) | -| `OPENEVOLVE_CORE_RANGE` | explicit taskset core range `N-M` (overrides PARALLEL_SOLVERS for pinning; concurrency = range size) | +| `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 in score.py (0 disables) | -| `OPENEVOLVE_COST_WEIGHT` | (cost-mode only) exponent on cost_ratio | -| `OPENEVOLVE_PYTHON_BIN` | python used by solver worker subprocess | -| `SKIP_REBASELINE` | skip per-host baseline remeasurement (reuse `local_baseline.json`) | +| `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 -# (cpsat-bench example — swap to z3-bench by name) -bash input/cpsat-bench/raw-data/load_script.sh # populate raw-data -python input/cpsat-bench/evolve/build_samples.py # stage{1..4}_sample.json -python input/cpsat-bench/evolve/shared/baseline_params.py # sanity self-test - -# Run all phases (1..N) with one command: +# 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) -# Or step phase-by-phase: -./input/run_phase.sh cpsat-bench 1 --pin 2-7 +./input/run_phase.sh cpsat-bench 1 --pin 2-7 # phase 1 only ./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 +# ... -# Final verify on the unified winner (fresh LOCAL baseline per problem): -python input/cpsat-bench/evolve/final_verify.py \ - input/cpsat-bench/evolve/phase4_unified/openevolve_output/best/best_program.py -# (writes final_verify.json next to best_program.py) +# 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/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..cbb0103f25 --- /dev/null +++ b/input/_lib/bench_paths.py @@ -0,0 +1,116 @@ +""" +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 cache_dir(bench_root): + return pathlib.Path(bench_root).resolve() / "cache" + + +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..2755e7e025 --- /dev/null +++ b/input/_lib/evaluator_core.py @@ -0,0 +1,476 @@ +""" +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")) + 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 index 5b324723b1..e622001b95 100644 --- a/input/_lib/extract_best.py +++ b/input/_lib/extract_best.py @@ -1,13 +1,14 @@ """ -Generic per-phase winner extractor. Per-bench `extract_best.py` wrappers -provide PHASE_DIRS and call `main(root, shared, phase_dirs)`. +Per-phase winner extractor. -After phase N completes, load get_phase_overrides() from its best_program.py -(default: openevolve_output/best/best_program.py) and write -shared/phaseN_best.json. The next phase's initial_program.py picks it up. +CLI: `python -m _lib.extract_best [--from-checkpoints]` ---from-checkpoints scans openevolve_output/checkpoints/checkpoint_*/ and -picks the program with the highest combined_score (best_program_info.json). +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 @@ -15,6 +16,8 @@ import pathlib import sys +from _lib import bench_paths + def _pick_from_checkpoints(phase_dir): ckpt_root = phase_dir / "openevolve_output" / "checkpoints" @@ -55,24 +58,52 @@ def _pick_from_checkpoints(phase_dir): return best_py -def main(root, shared, phase_dirs, argv=None): - """ - root — bench evolve dir (pathlib.Path) - shared — bench evolve/shared dir (pathlib.Path) - phase_dirs — {phase_int: "phaseN_"} (excludes the final/unified phase) +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. """ - choices = sorted(phase_dirs.keys()) - ap = argparse.ArgumentParser( - description="Extract get_phase_overrides() from phase's best_program.py.") - ap.add_argument("phase", type=int, choices=choices, 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) + 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 args.from_checkpoints: + if from_checkpoints: best_py = _pick_from_checkpoints(phase_dir) else: best_py = phase_dir / "openevolve_output" / "best" / "best_program.py" @@ -160,3 +191,7 @@ def main(root, shared, phase_dirs, argv=None): 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..fbe1dd0bfe --- /dev/null +++ b/input/_lib/finalize.py @@ -0,0 +1,191 @@ +""" +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 + + out = bench_root / "final_program.py" + 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_program.py ({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 index 961ddafea7..794b5c4de2 100644 --- a/input/_lib/load_bench_config.py +++ b/input/_lib/load_bench_config.py @@ -5,29 +5,26 @@ Usage: eval "$(python _lib/load_bench_config.py /evolve/config.yaml)" -Schema: +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; null/omitted => fall back to max_iterations + iters: 60 # optional; falls back to max_iterations - dir: phase2_y ... - unified_prepare_script: prepare_phase_unified.py # optional - unified_prepare_before_dir: phase4_unified # optional; fire prep - # before this dir - # (default: last phase) + 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 - rebaseline_script: rebaseline_local.py # optional override - extract_best_script: extract_best.py # optional override - build_samples_script: build_samples.py # optional override Output (stdout): export PHASE_DIRS='phase1_x phase2_y ...' export PHASE_ITERS='60 80 40' # space-separated; missing iters -> empty slot - export UNIFIED_PREPARE_SCRIPT='prepare_phase_unified.py' - export SOLVER_CHECK_CMD='python -c "..."' - ... + 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. @@ -96,13 +93,9 @@ def main(): if any_iter: _emit("PHASE_ITERS", " ".join(iters)) - _emit("UNIFIED_PREPARE_SCRIPT", bench.get("unified_prepare_script")) _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")) - _emit("REBASELINE_SCRIPT", bench.get("rebaseline_script")) - _emit("EXTRACT_BEST_SCRIPT", bench.get("extract_best_script")) - _emit("BUILD_SAMPLES_SCRIPT", bench.get("build_samples_script")) if __name__ == "__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 index 98a8a23496..700c23b862 100644 --- a/input/_lib/prepare_phase.py +++ b/input/_lib/prepare_phase.py @@ -1,28 +1,30 @@ """ -Materialize a unified phase's initial_program.py with the union of prior-phase -winners (from shared/phaseN_best.json + optional phaseN_buckets.json + -phaseN_stage3.json). - -Per-bench `prepare_phase_unified.py` wrappers call -`main(root, shared, prior_phases, unified_file)` after all prior phases -have completed (and extract_best.py ran for each). - -Rewrites only the EVOLVE-BLOCK section of the unified phase's initial_program.py. - -Output format inside EVOLVE-BLOCK (cpsat-bench layout): - GLOBAL_OVERRIDES = {...} # union of phaseN_best.json - SIZE_BUCKETS = [(upper, {...})] # merged per-bucket overrides - STAGE3_OVERRIDES = {...} # union of phaseN_stage3.json - -If a bench doesn't ship the bucket/stage3 files, only GLOBAL_OVERRIDES is -written (legacy single-dict layout) — controlled by `dict_name` parameter. +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, @@ -78,17 +80,37 @@ def _format_buckets_literal(buckets): 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"): - """ - root — bench evolve dir (pathlib.Path) - shared — bench evolve/shared dir (pathlib.Path) - prior_phases — list of phase ints to merge (e.g. [1, 2, 3]) - unified_file — path to the unified phase's initial_program.py - dict_name — variable name for the merged global dict. Default - "UNIFIED_OVERRIDES" (legacy). cpsat-bench unified phase - uses "GLOBAL_OVERRIDES"; pass dict_name="GLOBAL_OVERRIDES" - from the wrapper. - """ + """Direct invocation (also used by main_cli).""" unified_file = pathlib.Path(unified_file) merged = {} @@ -157,3 +179,7 @@ def main(root, shared, prior_phases, unified_file, dict_name="UNIFIED_OVERRIDES" 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/sampler.py b/input/_lib/sampler.py new file mode 100644 index 0000000000..6a08c638d3 --- /dev/null +++ b/input/_lib/sampler.py @@ -0,0 +1,285 @@ +""" +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 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") + + 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") + + 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(f"wrote {sample_path.name} ({len(picks)} from clusters " + f"{cluster_ids})") + + +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..6256b9ab49 --- /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(p["baseline_ms"] / 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 p["baseline_ms"] / 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 = p["baseline_ms"] / 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/cpsat-bench/evolve/README.md b/input/cpsat-bench/evolve/README.md index 881cd9b9e6..0b78469826 100644 --- a/input/cpsat-bench/evolve/README.md +++ b/input/cpsat-bench/evolve/README.md @@ -1,309 +1,205 @@ -# cpsat-bench Parameter Tuning via OpenEvolve +# cpsat-bench — OR-tools CP-SAT 파라미터 튜닝 -Tune OR-tools CP-SAT (`ortools.sat.python.cp_model`) parameters by -evolutionary search on the `input/cpsat-bench/raw-data` dataset (85 OPTIMAL -instances stored as binary `CpModelProto`). Goal: minimize wall-clock vs -baseline while preserving feasibility / objective. +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. -> OpenEvolve concept primer: see `input/z3-bench/evolve/OPENEVOLVE_INTRO.md` — -> z3-bench is the canonical reference impl; the pattern here is identical -> (cost-mode scoring instead of decision-mode is the only difference). +플랫폼 전반 구조는 [`input/README.md`](../../README.md) 참고. 본 문서는 +cpsat-bench 고유 사항만 다룬다. -## Layout +## 디렉토리 구조 ``` input/cpsat-bench/ -├── raw-data/ # flat layout (mirrors z3-bench) -│ ├── load_script.sh # gitlab pkg 71 downloader -│ ├── .cpsat.pb # binary CpModelProto -│ └── ____seed0.meta.jsonl -├── problems.jsonl # built by build_samples.py +├── raw-data/ # .cpsat.pb + meta.jsonl +├── problems.jsonl # baseline 실행 기록 (855 rows) └── evolve/ - ├── README.md # this file - ├── config.yaml # OpenEvolve config + custom knobs - ├── run_phase.sh # phase runner (chmod +x) - ├── build_samples.py # stage{1..4} sample selection - ├── extract_best.py # per-phase winner extraction (1..4) - ├── prepare_phase_unified.py # materializes phase4 EVOLVE-BLOCK - ├── rebaseline_local.py # measure baseline on local box - ├── shared/ - │ ├── baseline_params.py # BASELINE + LOCKED - │ ├── score.py # cost mode - │ ├── runtime.py # config.yaml passthrough - │ ├── evaluator.py # cascade stages - │ ├── cpsat_runner.py # subprocess solver invoker - │ ├── _cpsat_solve_worker.py - │ ├── stage{1..4}_sample.json # small profile (cascade staging) - │ ├── stage1_large_sample.json # large profile (single hardest outlier) - │ └── local_baseline.json # built by rebaseline_local.py - ├── phase{1..4}_/ - │ └── initial_program.py # EVOLVE-BLOCK (top-level param tuning) - └── phase5_custom_subsolvers/ - └── initial_program.py # EVOLVE-BLOCK (adds custom subsolvers) + ├── 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) ``` -## Evaluation flow (cascade) +## 평가 흐름 -The cascade differs by profile — see [Profiles](#profiles-small-vs-large). -The **small** profile (default) is: +`config.yaml` `bench.evaluation`: -``` -LLM-mutated initial_program.py - ↓ -evaluator.py: - 1. get_params(problem=None, stage=...) → global dict (workers/locked) - 2. LOCKED-violation check (random_seed, num_search_workers, timeout_sec) - 3. stage1 (10 problems, runtime c1+c2 cluster centers): - get_params(problem=p, stage="stage1") per problem - each solve repeated 10× and averaged (dtime); invalid_param → early 0 - score → gate (cascade_thresholds[0]) → stage2 - 4. stage2 (10 problems, runtime c3+c4 cluster centers): same - 5. stage3 slot = _finalize_small(): merge cached stage1 + stage2 results - into ONE combined final score. No outlier stage3, no broad stage4. -``` +| 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. -Non-feasible variants are NOT aborted — their real (slow) timeout ratio plus -a `solved_rate` drop is the penalty. Scoring uses `deterministic_time`. +## Clustering (config.yaml `bench.clustering`) -> The **large** profile bypasses staging entirely: every cascade entry point -> evaluates a single hardest-outlier set once (cache-backed). The default -> `build_samples.py` (no `--small`) still emits a 4-stage layout -> (stage3=outliers, stage4=broad) for ad-hoc analysis / `evaluate_stage4`, -> but the small cascade ignores stage3/4. +| 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 | -### Per-problem param resolution +`python -m _lib.sampler cpsat-bench`로 `cache/stage{1..4}_sample.json` 생성. -Each phase's `initial_program.py` exposes three evolution surfaces inside its -EVOLVE-BLOCK: +## Phase별 surface -| Surface | Applied when | Purpose | +각 phase `initial_program.py`의 EVOLVE-BLOCK에는 세 surface: + +| Surface | 적용 조건 | 용도 | |---|---|---| -| `GLOBAL_OVERRIDES` | every problem | baseline params for the phase | -| `SIZE_BUCKETS` | `num_constraints` matches the bucket's upper bound | trade off cuts / probing / subsolver mix by problem size (small <50k / mid 50–150k / large ≥150k) | -| `STAGE3_OVERRIDES` | `stage == "stage3"` AND `problem.is_outlier` | tune outlier-specific knobs without regressing normal problems | +| `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 | -Apply order inside `get_params()`: -`BASELINE → prior_phase_globals → GLOBAL_OVERRIDES → SIZE_BUCKETS match → -STAGE3_OVERRIDES (gated) → PHASE_LOCKED`. +`get_params(problem, stage)` 적용 순서: +`BASELINE → GLOBAL_OVERRIDES → SIZE_BUCKETS 매치 → STAGE3_OVERRIDES (gated) +→ PHASE_LOCKED`. -`is_outlier` is set from `shared/outliers.json`, generated by `build_samples.py` -from `Statistics/outliers_top.csv`. Reference for valid CP-SAT param names: -`shared/cpsat_params_reference.md` (275 fields, grouped by category). +`size` 값은 `adapter.get_problem_size(features)` (= `num_constraints`)에서 +나옴. ## Phase 5: custom subsolvers -Phases 1–4 tune **top-level** CP-SAT parameters. Phase 5 instead adds **custom -subsolvers** to the portfolio, leaving the inherited top-level config (phase4 -winner) untouched. - -Why a separate mechanism: a top-level param applies to *every* subsolver, -including the LNS workers. An expensive propagation technique enabled top-level -also fires inside LNS, making it useless; an objective-requiring config on an -objective-less model makes CP-SAT drop most/all subsolvers. Isolating the knob -in a single extra subsolver bounds the downside — if it doesn't help only that -one worker is slow; if it helps, that worker shares solutions / variable bounds -with the rest of the portfolio. +Phase 1-4가 top-level 파라미터를 튜닝하는 반면, Phase 5는 portfolio에 +**custom subsolver를 추가**한다 (inherited phase4 top-level config는 건드리지 +않음). -Evolve surface (`phase5_custom_subsolvers/initial_program.py` EVOLVE-BLOCK): +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. -| Surface | Applied when | Purpose | -|---|---|---| -| `CUSTOM_SUBSOLVERS` | every problem | portfolio additions for all problems | -| `STAGE3_CUSTOM_SUBSOLVERS` | `stage == "stage3"` AND `problem.is_outlier` | extra subsolvers for long-tail outliers | +Phase 5 EVOLVE-BLOCK: -Each entry is a dict: +| Surface | 적용 | +|---|---| +| `CUSTOM_SUBSOLVERS` | 모든 문제 | +| `STAGE3_CUSTOM_SUBSOLVERS` | stage3 outlier 전용 | +각 entry: ```python { - "name": "max_lp_heavy", # required, unique - "params": {"linearization_level": 2, "add_mir_cuts": True}, - "needs_objective": False, # optional; skip on objective-less models - "min_constraints": 50000, # optional inclusive lower gate - "max_constraints": None, # optional exclusive upper gate + "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, } ``` -`get_params()` turns eligible entries into `subsolver_params` (a named -`SatParameters` set per subsolver) plus `extra_subsolvers` names, on top of the -inherited phase4 config. `needs_objective` entries are dropped when the model -has no objective (`shared/has_objective_cache.json`), avoiding portfolio -collapse. Locks: `random_seed=0`, `num_search_workers=8`, -`interleave_search=True` (the cross-worker sharing that makes an isolated -subsolver pay off requires it). - -The worker (`shared/_cpsat_solve_worker.py`) builds params on a standalone -`sat_parameters_pb2.SatParameters` proto and assigns it wholesale, so nested -`subsolver_params` messages apply on recent ortools (9.15+) where in-place -mutation of repeated messages is unsupported. Invalid subsolver fields surface -as `invalid_param: subsolver_params.`. - -Phase 5 is the terminal phase (no extract step). Phase 4 is therefore no longer -terminal: `run_phase.sh` materializes its EVOLVE-BLOCK before it runs (config -key `unified_prepare_before_dir`) and extracts `phase4_best.json` after, which -phase 5 inherits. - -## Quick start - -```bash -# 0) populate raw-data from gitlab package 71 (first time only) -cd input/cpsat-bench/raw-data && bash load_script.sh && cd - +`_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. -# 1) build sample files (re-run any time raw-data changes) -python input/cpsat-bench/evolve/build_samples.py - -# 2) sanity-check: does the runner reproduce baseline timings? -python input/cpsat-bench/evolve/shared/baseline_params.py - -# 3) optional: measure baseline on local box (captures objective_value -# for cost-mode scoring — first run picks it up automatically too) -python input/cpsat-bench/evolve/rebaseline_local.py - -# 4) run phases sequentially (small profile = historical run) -./input/run_phase.sh cpsat-bench 1 -./input/run_phase.sh cpsat-bench 2 -./input/run_phase.sh cpsat-bench 3 -./input/run_phase.sh cpsat-bench 4 # unified refinement (top-level params) -./input/run_phase.sh cpsat-bench 5 # custom subsolvers (inherits phase4_best) -# or all phases at once: -./input/run_phase.sh cpsat-bench -``` +Locked: `random_seed=0`, `num_search_workers=8`, `interleave_search=True` +(cross-worker sharing 전제). -After each non-final phase (1–4), `run_phase.sh` calls `extract_best.py` to -write `shared/phaseN_best.json`, which the next phase's `initial_program.py` -loads. Phase 5 is terminal (no extract). Running phase 5 standalone requires -`shared/phase4_best.json` — produced by phase 4, or regenerate with -`./input/run_phase.sh cpsat-bench 4 --extract-only`. +Phase 5는 terminal — extract 단계 없음. Phase 4가 `unified_prepare_before_dir` +target이라 `python -m _lib.prepare_phase cpsat-bench`로 미리 머터리얼됨. -## Profiles (`small` vs `large`) - -`run_phase.sh --profile ` switches between two independent -tuning tracks: - -| | small (default) | large | -|---|---|---| -| sample files | `stage1/2_sample.json` (stage3/4 empty) | `stage1_large_sample.json` (single sample) | -| phase1/2 workers | 1 | 8 | -| phase3/4 workers | 8 | 8 | -| cascade | stage1 → stage2 → **combined final** (no stage3/4) | single eval on all stages (cache-backed) | -| solve repeats | **10× per problem, averaged** | 1× | -| problem set | clustered fast (c1+c2) + mid (c3+c4) | top-`STAGE1_LARGE_N` outlier(s) sorted by `STAGE1_LARGE_SORT_BY` | -| output dir | `openevolve_output/` | `openevolve_output_large/` | - -Both profiles use **deterministic_time** as the primary score metric (wall is -diagnostic only) and treat a **non-feasible variant's real measured time** as -its ratio — a timed-out solve contributes its slow timeout ratio + a -`solved_rate` drop, never a fixed `1e-6` sentinel and never an abort. - -Profile sets `OPENEVOLVE_PROFILE` env var that: - -- `evaluator.py` reads to pick sample files and route the cascade: - - **small** → `evaluate_stage1` (stage1), `evaluate_stage2` (stage2), - `evaluate_stage3` = `_finalize_small()` which merges the cached stage1 + - stage2 per-problem results into one combined score (no stage3/4). Each - solve runs `N_REPEATS_SMALL` (=10) times and averages dtime/wall/counters - to damp multi-worker jitter (`OPENEVOLVE_SOLVE_REPEATS` overrides). - - **large** → every entry point dispatches to a single `_evaluate_large()` - over the outlier set (cache-backed, 1× solve). -- `rebaseline_local.py` reads to rebaseline only the active profile's sample set. -- `phase{1,2}_*/initial_program.py` reads to override `PHASE_LOCKED` - `num_search_workers=8` (default 1). - -Build samples per profile: +## Quick start ```bash -python build_samples.py # default: all 4 stages (stage3=outliers, stage4=broad) -python build_samples.py --small # small: stage1+stage2 only, stage3/4 empty -python build_samples.py --optimize-only # restrict pool to objective-bearing problems -``` - -`run_phase.sh --profile small` auto-passes `--small` to the build step. -Adjust `STAGE1_LARGE_N` / `STAGE1_LARGE_SORT_BY` in `build_samples.py` to -widen or re-rank the large outlier set. +# 1. raw-data 채우기 (최초 1회) +cd input/cpsat-bench/raw-data && bash load_script.sh && cd - -```bash -# large profile (all phases W=8, single hardest outlier) -./input/run_phase.sh cpsat-bench --profile large -./input/run_phase.sh cpsat-bench 1 --profile large # single phase +# 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 ``` -`local_baseline.json` is shared across profiles (keyed by worker count); -running both profiles reuses each other's measurements where worker count -matches. +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`. -> Note: `extract_best.py` writes a single `phaseN_best.json` per phase -> regardless of profile. Running both profiles back-to-back overwrites the -> previous profile's best file. Rename/move it manually if you want to keep -> both. +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. -## Resume from checkpoint +## Worker count 정책 -```bash -cd input/cpsat-bench/evolve/phase2_presolve/ -python /app/openevolve-run.py \ - initial_program.py \ - ../shared/evaluator.py \ - --config ../config.yaml \ - --checkpoint openevolve_output/checkpoints/checkpoint_50 \ - --iterations 100 -``` - -## Environment variables - -| variable | default | use | +| Phase | `num_search_workers` | 이유 | |---|---|---| -| `CLAUDE_CODE_OAUTH_TOKEN` / `OPENAI_API_KEY` | — | LLM credential (claude_code provider) | -| `OPENEVOLVE_PARALLEL_SOLVERS` | 1 | concurrent solver subprocesses per stage (taskset-pinned on Linux to cores 1..N). Each CP-SAT uses num_search_workers=8 internally — size accordingly. | -| `OPENEVOLVE_CORE_RANGE` | unset | explicit taskset core range `N-M` (overrides PARALLEL_SOLVERS for pinning; concurrency = range size). e.g. `2-7` → 6 workers on cores 2..7. `run_phase.sh --pin 2-7` is sugar for this. | -| `OPENEVOLVE_MAX_PROBLEMS` | unlimited | cap stage problem count (testing) | -| `OPENEVOLVE_STATS_WEIGHT` | 0.333 | exponent on efficiency factor (0 disables) | -| `OPENEVOLVE_COST_WEIGHT` | 1.0 | exponent on cost_ratio in cost-mode score (0 disables cost factor) | -| `OPENEVOLVE_PYTHON_BIN` | `sys.executable` | python used by solver worker | -| `OPENEVOLVE_PROFILE` | `small` | tuning track: `small` (cascade staging) or `large` (single outlier eval). Set automatically by `run_phase.sh --profile`. Drives sample file selection in `evaluator.py` / `rebaseline_local.py` and worker count override in phase1/2 `initial_program.py`. | -| `SKIP_REBASELINE` | 0 | skip per-host baseline remeasurement (reuse existing local_baseline.json) | +| 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 | -## CPU pinning (Linux) +`OPENEVOLVE_PROFILE=large`로 phase 1/2도 W=8 운영 가능 (outlier 튜닝 트랙). -For stable wall-clock measurement, isolate cores via -`scripts/host-isolate-cores.sh start` (from the openevolve repo root) and -run the docker container with `./docker-run.sh dev -s cpsat --pin 1-6`. -The runner already pins each worker subprocess with `taskset -c `. - -## Score formula (cost mode) +## Score 공식 (cost mode) ``` -combined_score = geomean( (b_obj/v_obj)^COST_W * time_ratio ) +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 (fallback when dtime missing) +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** (CP-SAT's hardware-independent work measure) is the - primary time metric. Wall-clock varies with CPU load / NUMA / thermal - state — using dtime removes that noise from the score so genuine knob - improvements are visible. `geomean_speedup` in the metrics dict is the - dtime ratio; `geomean_wall_speedup` is reported alongside as a - diagnostic. Override with `OPENEVOLVE_TIME_METRIC=wall` if needed. -- Fallback to wall when either side lacks `deterministic_time` in `stats` - (e.g. older local_baseline.json from before this change — rerun - `rebaseline_local.py` to refresh). -- All 85 baselines are OPTIMAL, so if the variant also reaches OPTIMAL, the - cost ratio collapses to 1.0 and score reduces to geomean(time speedup). -- Variants that bail to FEASIBLE-but-worse-objective are penalized via the - cost ratio; variants that bail to UNKNOWN/INFEASIBLE contribute 1e-6 to - the geomean (~60× penalty across stage1's 10 problems). -- Efficiency factor multiplies based on `num_conflicts` (weight 2.0) and - `num_branches` (weight 1.5) ratios vs baseline. +- `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`, `num_search_workers` (per-phase: 1 in phase1/2, 8 in -phase3/4/5). Phase 5 additionally locks `interleave_search=True` (required for -the cross-worker sharing a custom subsolver depends on). +`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 -Modifying any locked key in `get_params()` returns `combined_score=0` plus a -`locked_violated` artifact identifying the offending key. Per-problem -`get_params(problem, stage)` calls have locked keys and `num_search_workers` -re-pinned defensively by the evaluator after the call returns, so SIZE_BUCKETS -/ STAGE3_OVERRIDES cannot override them even by accident. +- 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/shared/_cpsat_solve_worker.py b/input/cpsat-bench/evolve/_solve_worker.py similarity index 100% rename from input/cpsat-bench/evolve/shared/_cpsat_solve_worker.py rename to input/cpsat-bench/evolve/_solve_worker.py 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/benchmark_final.py b/input/cpsat-bench/evolve/benchmark_final.py deleted file mode 100644 index dfadb990f5..0000000000 --- a/input/cpsat-bench/evolve/benchmark_final.py +++ /dev/null @@ -1,439 +0,0 @@ -""" -Repeated-trial benchmark of BASELINE vs final-best on the main test sample. - -For each problem in the sample, run BASELINE N times AND the optimized -program's params N times (default 20 each, back-to-back on the same core -to share warm-cache / system-noise envelope). Aggregate mean / median / -stddev / min / max per case and report speedup of variant vs baseline. - -Parallelism comes from config.yaml `parallel_solvers` (env -OPENEVOLVE_PARALLEL_SOLVERS overrides) or OPENEVOLVE_CORE_RANGE. Each -problem is pinned to one core; the 40 runs for that problem stay serial -on that core. Different problems run concurrently across cores. - -Usage: - python benchmark_final.py [--iters N] [--timeout S] - -Outputs (next to ): - benchmark_final.json summary metrics - benchmark_final_iterations.csv long-form: per-run row - benchmark_final_summary.csv per-problem aggregates - -Sample selection (same as final_verify.py): - 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. - 2. Fall back to ALL problems in problems.jsonl. -""" -import argparse -import csv -import importlib.util -import inspect -import json -import pathlib -import statistics -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed - -_HERE = pathlib.Path(__file__).resolve().parent -sys.path.insert(0, str(_HERE / "shared")) - -from baseline_params import BASELINE, LOCKED # noqa: E402 -from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" -_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" - -_DEFAULT_ITERS = 20 -_DEFAULT_TIMEOUT_S = 300 -_DECISIVE = ("OPTIMAL", "FEASIBLE") - - -def _load_outlier_shas(): - if not _OUTLIERS_JSON.exists(): - return set() - try: - d = json.loads(_OUTLIERS_JSON.read_text()) - except (json.JSONDecodeError, OSError): - return set() - return set(d.get("outliers") or {}) - - -def _supports_kwargs(fn, *kwargs): - 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(name in params_ for name in kwargs) - - -def _resolve_params(fn, problem, stage_name): - kwargs = {} - if _supports_kwargs(fn, "problem"): - kwargs["problem"] = problem - if _supports_kwargs(fn, "stage"): - kwargs["stage"] = stage_name - return fn(**kwargs) if kwargs else fn() - - -def _load_program(program_path): - spec = importlib.util.spec_from_file_location("program", program_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - if not hasattr(module, "get_params"): - print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) - sys.exit(2) - return module - - -def _load_problem_index(): - outliers = _load_outlier_shas() - idx = {} - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - sha = d["problem_sha256"] - features = d.get("features") or {} - idx[sha] = { - "sha": sha, - "problem_filename": d["problem_filename"], - "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), - "raw_result": (d.get("cpsat_status") or {}).get("result"), - "num_variables": int(features.get("num_variables") or 0), - "num_constraints": int(features.get("num_constraints") or 0), - "num_bool": int(features.get("num_bool") or 0), - "num_int": int(features.get("num_int") or 0), - "is_outlier": sha in outliers, - } - return idx - - -def _resolve_sample(idx): - if _FINAL_SAMPLE.exists(): - shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) - source = f"shared/final_sample.json ({len(shas)} SHAs)" - else: - shas = list(idx.keys()) - source = f"problems.jsonl (full {len(shas)})" - metas = [] - for sha in shas: - meta = idx.get(sha) - if meta is None: - print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) - sys.exit(2) - pb = _RAW_DIR / meta["problem_filename"] - if not pb.exists(): - print(f"ERROR: missing {pb}", file=sys.stderr) - sys.exit(2) - metas.append((meta, pb)) - return metas, source - - -def _aggregate(runs): - """runs = list of dicts {elapsed_ms, result, objective?, timeout, stats?}. - Returns aggregate stats over only the decisive runs (others as 'failed'). - Tracks both wall (elapsed_ms) and deterministic_time aggregates.""" - decisive = [r for r in runs if r["result"] in _DECISIVE] - ms_list = [r["elapsed_ms"] for r in decisive] - dt_list = [(r.get("stats") or {}).get("deterministic_time") - for r in decisive] - dt_list = [d for d in dt_list if d is not None and d > 0] - obj_list = [r["objective"] for r in decisive - if r.get("objective") is not None] - agg = { - "n_total": len(runs), - "n_decisive": len(decisive), - "n_failed": len(runs) - len(decisive), - } - if ms_list: - agg["mean_ms"] = statistics.mean(ms_list) - agg["median_ms"] = statistics.median(ms_list) - agg["min_ms"] = min(ms_list) - agg["max_ms"] = max(ms_list) - agg["stddev_ms"] = statistics.stdev(ms_list) if len(ms_list) > 1 else 0.0 - else: - agg.update({"mean_ms": 0, "median_ms": 0, "min_ms": 0, "max_ms": 0, "stddev_ms": 0.0}) - if dt_list: - agg["mean_dt"] = statistics.mean(dt_list) - agg["median_dt"] = statistics.median(dt_list) - agg["stddev_dt"] = statistics.stdev(dt_list) if len(dt_list) > 1 else 0.0 - agg["n_dt"] = len(dt_list) - else: - agg.update({"mean_dt": 0, "median_dt": 0, "stddev_dt": 0.0, "n_dt": 0}) - if obj_list: - agg["mean_obj"] = statistics.mean(obj_list) - agg["min_obj"] = min(obj_list) - agg["max_obj"] = max(obj_list) - return agg - - -def _run_repeat(problem_path, params, n, timeout_s, core, tag): - runs = [] - for i in range(n): - r = run_cpsat(problem_path, params, timeout_s, cpu_core=core) - runs.append({ - "iter": i + 1, - "elapsed_ms": int(r.get("elapsed_ms", 0)), - "result": r.get("result", "Unknown"), - "objective": r.get("objective"), - "timeout": bool(r.get("timeout")), - "invalid_param": r.get("invalid_param"), - "error": r.get("error"), - "stats": r.get("stats") or {}, - }) - return runs - - -def main(): - ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - ap.add_argument("program", help="path to best_program.py (must expose get_params())") - ap.add_argument("--iters", type=int, default=_DEFAULT_ITERS, - help=f"runs per (problem, variant) pair (default {_DEFAULT_ITERS})") - ap.add_argument("--timeout", type=int, default=_DEFAULT_TIMEOUT_S, - help=f"per-run timeout in seconds (default {_DEFAULT_TIMEOUT_S})") - args = ap.parse_args() - - program_path = pathlib.Path(args.program).resolve() - if not program_path.exists(): - print(f"ERROR: {program_path} not found", file=sys.stderr) - return 2 - - program = _load_program(program_path) - # No-arg path resolves the "global" params used for worker/block sizing, - # locked-key validation, and summary diff stats. Per-problem variants - # (SIZE_BUCKETS + STAGE3_OVERRIDES) are resolved inside _worker below. - variant_params = _resolve_params(program.get_params, None, "final") - _phase_locked = getattr(program, "PHASE_LOCKED", None) - _lock = _phase_locked if isinstance(_phase_locked, dict) else LOCKED - violations = {k: variant_params.get(k) for k in _lock - if variant_params.get(k) != _lock[k]} - if violations: - print(f"ERROR: locked params violated: {violations}", file=sys.stderr) - return 2 - - idx = _load_problem_index() - metas, source = _resolve_sample(idx) - - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=1) + 1)) - workers_per_solve = int(variant_params.get("num_search_workers", 1) or 1) - blocks = alloc_core_blocks(cores, workers_per_solve) - if not blocks: - blocks = [list(cores)] if cores else [None] - n_parallel = min(len(blocks), len(metas)) - blocks = blocks[:n_parallel] - baseline_cores = [(b[0] if isinstance(b, (list, tuple)) and b else b) for b in blocks] - - n_iters = args.iters - timeout_s = args.timeout - - n_total_runs = len(metas) * n_iters * 2 - print(f"benchmark final: {program_path}") - print(f" sample : {source}") - print(f" iters per case : {n_iters} baseline + {n_iters} variant = {n_iters * 2}") - print(f" total runs : {len(metas)} problems × {n_iters * 2} = {n_total_runs}") - print(f" per-run timeout : {timeout_s}s") - print(f" parallel solvers: {n_parallel} variant_workers={workers_per_solve} blocks={blocks}") - print(f" params diff : {sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} keys differ from BASELINE") - print() - - def _worker(idx_meta): - i, meta, pb = idx_meta - slot = i % n_parallel - v_core = blocks[slot] - b_core = baseline_cores[slot] - # Per-problem param resolution. Locked + worker count re-pinned to - # match `variant_params` so core blocks stay consistent. - per_params = _resolve_params(program.get_params, meta, "final") - for k, v in _lock.items(): - per_params[k] = v - if "num_search_workers" in variant_params: - per_params["num_search_workers"] = variant_params["num_search_workers"] - t0 = time.monotonic() - baseline_runs = _run_repeat(pb, BASELINE, n_iters, timeout_s, b_core, "baseline") - variant_runs = _run_repeat(pb, per_params, n_iters, timeout_s, v_core, "variant") - dt = time.monotonic() - t0 - return i, meta, baseline_runs, variant_runs, v_core, dt - - tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] - t_start = time.monotonic() - completed = [] - if n_parallel == 1: - for t in tasks: - completed.append(_worker(t)) - i, meta, b_runs, v_runs, core, dt = completed[-1] - b_agg = _aggregate(b_runs) - v_agg = _aggregate(v_runs) - sp_w = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 - sp_d = (b_agg["mean_dt"] / max(v_agg["mean_dt"], 1)) if v_agg["mean_dt"] else 0 - sp_main = sp_d if sp_d else sp_w - print(f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} core={core} " - f"base_mean={b_agg['mean_ms']:>7.0f}ms (n_ok={b_agg['n_decisive']}/{n_iters}) " - f"variant_mean={v_agg['mean_ms']:>7.0f}ms (n_ok={v_agg['n_decisive']}/{n_iters}) " - f"speedup={sp_main:.2f}x[{'dt' if sp_d else 'wall'}] wall={sp_w:.2f}x (took {dt:.1f}s)", - flush=True) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_worker, t) for t in tasks] - for fut in as_completed(futures): - completed.append(fut.result()) - i, meta, b_runs, v_runs, core, dt = completed[-1] - b_agg = _aggregate(b_runs) - v_agg = _aggregate(v_runs) - sp = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 - print(f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} core={core} " - f"base_mean={b_agg['mean_ms']:>7.0f}ms (n_ok={b_agg['n_decisive']}/{n_iters}) " - f"variant_mean={v_agg['mean_ms']:>7.0f}ms (n_ok={v_agg['n_decisive']}/{n_iters}) " - f"speedup={sp:.2f}x (took {dt:.1f}s)", - flush=True) - completed.sort(key=lambda x: x[0]) - wall = time.monotonic() - t_start - - # ----- write CSVs ----- - out_dir = program_path.parent - iter_csv = out_dir / "benchmark_final_iterations.csv" - summary_csv = out_dir / "benchmark_final_summary.csv" - json_path = out_dir / "benchmark_final.json" - - with open(iter_csv, "w", newline="") as f: - w = csv.writer(f) - w.writerow(["sha", "variant", "iter", "elapsed_ms", "result", - "objective", "timeout", "invalid_param", "error"]) - for _, meta, b_runs, v_runs, _, _ in completed: - sha = meta["sha"] - for r in b_runs: - w.writerow([sha, "baseline", r["iter"], r["elapsed_ms"], r["result"], - r["objective"] if r["objective"] is not None else "", - int(r["timeout"]), - r["invalid_param"] or "", - (r["error"] or "")[:200]]) - for r in v_runs: - w.writerow([sha, "variant", r["iter"], r["elapsed_ms"], r["result"], - r["objective"] if r["objective"] is not None else "", - int(r["timeout"]), - r["invalid_param"] or "", - (r["error"] or "")[:200]]) - - summary_rows = [] - for _, meta, b_runs, v_runs, _, _ in completed: - b_agg = _aggregate(b_runs) - v_agg = _aggregate(v_runs) - mean_sp_w = (b_agg["mean_ms"] / max(v_agg["mean_ms"], 1)) if v_agg["mean_ms"] else 0 - med_sp_w = (b_agg["median_ms"] / max(v_agg["median_ms"], 1)) if v_agg["median_ms"] else 0 - mean_sp_d = (b_agg["mean_dt"] / max(v_agg["mean_dt"], 1)) if v_agg["mean_dt"] else 0 - med_sp_d = (b_agg["median_dt"] / max(v_agg["median_dt"], 1)) if v_agg["median_dt"] else 0 - summary_rows.append({ - "sha": meta["sha"], - "n_iters": n_iters, - "baseline_n_decisive": b_agg["n_decisive"], - "baseline_mean_ms": round(b_agg["mean_ms"], 1), - "baseline_median_ms": round(b_agg["median_ms"], 1), - "baseline_min_ms": round(b_agg["min_ms"], 1), - "baseline_max_ms": round(b_agg["max_ms"], 1), - "baseline_stddev_ms": round(b_agg["stddev_ms"], 1), - "baseline_mean_dt": round(b_agg["mean_dt"], 3), - "baseline_median_dt": round(b_agg["median_dt"], 3), - "baseline_n_dt": b_agg["n_dt"], - "variant_n_decisive": v_agg["n_decisive"], - "variant_mean_ms": round(v_agg["mean_ms"], 1), - "variant_median_ms": round(v_agg["median_ms"], 1), - "variant_min_ms": round(v_agg["min_ms"], 1), - "variant_max_ms": round(v_agg["max_ms"], 1), - "variant_stddev_ms": round(v_agg["stddev_ms"], 1), - "variant_mean_dt": round(v_agg["mean_dt"], 3), - "variant_median_dt": round(v_agg["median_dt"], 3), - "variant_n_dt": v_agg["n_dt"], - "mean_speedup": round(mean_sp_w, 3), # wall (legacy) - "median_speedup": round(med_sp_w, 3), # wall (legacy) - "mean_dt_speedup": round(mean_sp_d, 3), # deterministic (primary) - "median_dt_speedup": round(med_sp_d, 3), - }) - - with open(summary_csv, "w", newline="") as f: - w = csv.DictWriter(f, fieldnames=list(summary_rows[0].keys())) - w.writeheader() - w.writerows(summary_rows) - - # ----- overall aggregate ----- - # speedup geomean over problems with both means valid. Compute both wall - # and deterministic-time geomeans; dt is preferred when populated for both - # baseline + variant on every (problem, iter) pair. - import math - valid = [r for r in summary_rows - if r["baseline_mean_ms"] > 0 and r["variant_mean_ms"] > 0 - and r["baseline_n_decisive"] > 0 and r["variant_n_decisive"] > 0] - valid_dt = [r for r in valid - if r["mean_dt_speedup"] > 0 and r["median_dt_speedup"] > 0] - if valid: - geo_mean = math.exp(sum(math.log(r["mean_speedup"]) for r in valid) / len(valid)) - geo_med = math.exp(sum(math.log(r["median_speedup"]) for r in valid) / len(valid)) - arith_mean = sum(r["mean_speedup"] for r in valid) / len(valid) - else: - geo_mean = geo_med = arith_mean = 0.0 - if valid_dt: - geo_mean_dt = math.exp(sum(math.log(r["mean_dt_speedup"]) - for r in valid_dt) / len(valid_dt)) - geo_med_dt = math.exp(sum(math.log(r["median_dt_speedup"]) - for r in valid_dt) / len(valid_dt)) - else: - geo_mean_dt = geo_med_dt = 0.0 - - n_baseline_failed = sum(r["baseline_n_decisive"] < n_iters for r in summary_rows) - n_variant_failed = sum(r["variant_n_decisive"] < n_iters for r in summary_rows) - n_baseline_all_failed = sum(r["baseline_n_decisive"] == 0 for r in summary_rows) - n_variant_all_failed = sum(r["variant_n_decisive"] == 0 for r in summary_rows) - - json_path.write_text(json.dumps({ - "program": str(program_path), - "sample_source": source, - "iters_per_case": n_iters, - "timeout_s": timeout_s, - "n_parallel": n_parallel, - "cores": cores, - "wall_clock_s": round(wall, 1), - "n_problems": len(metas), - "summary": { - "geomean_dt_speedup_mean": round(geo_mean_dt, 3), # primary - "geomean_dt_speedup_median": round(geo_med_dt, 3), - "geomean_wall_speedup_mean": round(geo_mean, 3), # diagnostic - "geomean_wall_speedup_median": round(geo_med, 3), - "arith_mean_wall_speedup": round(arith_mean, 3), - "n_problems_with_valid_speedup": len(valid), - "n_problems_with_valid_dt_speedup": len(valid_dt), - "n_baseline_partial_fail": n_baseline_failed, - "n_baseline_total_fail": n_baseline_all_failed, - "n_variant_partial_fail": n_variant_failed, - "n_variant_total_fail": n_variant_all_failed, - }, - "per_problem": summary_rows, - }, indent=2) + "\n") - - print() - print("== overall (mean-of-means across problems) ==") - print(f" problems with valid speedup : wall={len(valid)}/{len(metas)} dt={len(valid_dt)}/{len(metas)}") - print(f" geomean dt-speedup (mean) : {geo_mean_dt:.3f}x (primary, hardware-independent)") - print(f" geomean dt-speedup (median) : {geo_med_dt:.3f}x") - print(f" geomean wall-speedup (mean) : {geo_mean:.3f}x (diagnostic)") - print(f" geomean wall-speedup (med) : {geo_med:.3f}x") - print(f" arith mean wall-speedup : {arith_mean:.3f}x") - print(f" baseline partial-fail cases : {n_baseline_failed} (≥1 of {n_iters} runs non-decisive)") - print(f" baseline total-fail cases : {n_baseline_all_failed} (all {n_iters} runs non-decisive)") - print(f" variant partial-fail cases : {n_variant_failed}") - print(f" variant total-fail cases : {n_variant_all_failed}") - print(f" wall-clock : {wall:.1f}s") - print() - print(f" wrote {iter_csv}") - print(f" wrote {summary_csv}") - print(f" wrote {json_path}") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/input/cpsat-bench/evolve/build_samples.py b/input/cpsat-bench/evolve/build_samples.py deleted file mode 100644 index 978be19d3c..0000000000 --- a/input/cpsat-bench/evolve/build_samples.py +++ /dev/null @@ -1,607 +0,0 @@ -""" -Build problems.jsonl + stage1/2/3/4 sample files from raw-data/. - -Layout (flat, mirroring z3-bench): - raw-data/.cpsat.pb binary CpModelProto - raw-data/____seed0.meta.jsonl one-line JSON with - problem_sha256, problem_filename, cpsat_applied_params, - cpsat_status (result, elapsed_ms), cpsat_response_stats, ... - -problems.jsonl is the full aggregate; sample selection applies a runtime cap -(MAX_BASELINE_MS) and a Tukey IQR outlier filter (k=3.0) so stage quintile -boundaries don't get distorted by long-tail monsters. - -Stages (decisive = OPTIMAL or FEASIBLE; this dataset is all OPTIMAL): - Runtime is clustered into N_BUCKETS via 1D k-means (Lloyd's). Clusters - are ordered by ascending centroid (c1=fastest, c5=slowest), then merged. - - stage1 (5) center pick from clusters c1+c2 (fast group) — default-param sanity - stage2 (5) center pick from clusters c3+c4 (mid group) — default-param sanity - stage3 (5) outliers from Statistics/outliers_top.csv — per-problem tune target - (top residual, capped at STAGE3_MAX_BASELINE_MS — higher than - MAX_BASELINE_MS so genuinely slow outliers enter the tune set; - still bounded so a single evolve iteration finishes) - stage4 (20) quintile-spread broad sample, dedup vs stage1-3 - -Stage3 sample also writes shared/outliers.json (sha -> {residual, baseline_ms, -n_cons, n_vars}) so the evaluator can mark `is_outlier` on problem records and -phase initial_program.py can branch STAGE3_OVERRIDES on it. -""" -import argparse -import csv -import json -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_BENCH = _HERE.parent -_RAW = _BENCH / "raw-data" -_PROBLEMS = _BENCH / "problems.jsonl" -_STAGE1 = _HERE / "shared" / "stage1_sample.json" -_STAGE2 = _HERE / "shared" / "stage2_sample.json" -_STAGE3 = _HERE / "shared" / "stage3_sample.json" -_STAGE4 = _HERE / "shared" / "stage4_sample.json" -_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" - -# Large-profile sample (selected via OPENEVOLVE_PROFILE=large). Single file -# only — large profile bypasses cascade staging entirely; evaluator dispatches -# every stage entry point to a single outlier-set evaluation. File name keeps -# the stage1_large prefix so legacy callers (and evaluator's path resolver) -# continue to find it. -_STAGE1_LARGE = _HERE / "shared" / "stage1_large_sample.json" - -# outliers_top.csv lives under cpsat-bench/Statistics/ (or its rename "1/"). -_OUTLIERS_CSV_CANDIDATES = [ - _BENCH / "Statistics" / "outliers_top.csv", - _BENCH / "1" / "outliers_top.csv", -] - -STAGE1_N = 10 -STAGE2_N = 10 -STAGE3_N = 5 -STAGE4_N = 20 -N_BUCKETS = 5 -# Large profile: pick from outliers_top.csv. -# STAGE1_LARGE_N how many outliers in the sample -# STAGE1_LARGE_SORT_BY "elapsed_ms" → slowest first (large-runtime tuning); -# "residual" → most out-of-line vs predicted size. -# Make sure evaluator.timeout in config.yaml covers worst-case -# baseline_ms * 1.3 (= variant timeout). -STAGE1_LARGE_N = 1 -STAGE1_LARGE_SORT_BY = "elapsed_ms" -# Global cap for stage1/2/4 sample pool. Anything slower than 2 min skews -# the quintile clustering. -MAX_BASELINE_MS = 120_000 -# Stage3 (outlier-only) gets a higher cap so genuinely slow outliers can -# enter the tune set. 25 min ≈ what one stage3 problem can chew through -# under W=8 (raw-data times are W=8-equivalent). Variant timeout = -# baseline_ms * 1.3 in evaluator. -STAGE3_MAX_BASELINE_MS = 1_500_000 -# Stage3 target-based pick — outliers in outliers_top.csv span 4 orders of -# magnitude (180ms .. 5.7M ms). Picking by residual alone produces a sample -# with wild runtime variance, which makes per-iteration scoring noisy. Instead -# pick STAGE3_N outliers whose baseline elapsed_ms is closest to -# STAGE3_TARGET_MS, dropping any whose ratio to target exceeds -# STAGE3_TARGET_TOL_RATIO (e.g. target=20000 tol=5.0 → accepts 4000..100000ms). -STAGE3_TARGET_MS = 500_000 -STAGE3_TARGET_TOL_RATIO = 500.0 -OUTLIER_IQR_K = 3.0 - -STAGE1_STRATEGY = "center" -STAGE2_STRATEGY = "center" -STAGE3_STRATEGY = "target_ms" # pick outliers nearest STAGE3_TARGET_MS -STAGE4_STRATEGY = "spread" - -_DECISIVE_RESULTS = {"OPTIMAL", "FEASIBLE"} - - -def _scan_raw(): - """Glob raw-data/*.meta.jsonl (one-line JSON per problem). The meta - already contains problem_sha256 + problem_filename; no derivation needed.""" - rows = [] - for path in sorted(_RAW.glob("*.meta.jsonl")): - with open(path) as f: - line = f.readline().strip() - if not line: - continue - d = json.loads(line) - rows.append(d) - return rows - - -def _has_objective_set(): - """Scan raw-data/*.cpsat.pb once and return the set of SHAs whose - CpModelProto carries an objective (either `objective` or - `floating_point_objective`). Cached to shared/has_objective_cache.json so - re-runs skip the parse cost.""" - cache_path = _HERE / "shared" / "has_objective_cache.json" - pb_files = sorted(_RAW.glob("*.cpsat.pb")) - pb_count = len(pb_files) - - if cache_path.exists(): - try: - cache = json.loads(cache_path.read_text()) - if cache.get("pb_count") == pb_count: - return set(cache.get("with_objective") or []) - except (json.JSONDecodeError, OSError): - pass - - try: - from ortools.sat import cp_model_pb2 - except ImportError as e: - print(f"warning: ortools missing; cannot filter by objective: {e}", - file=sys.stderr) - return None - - with_obj = [] - for p in pb_files: - m = cp_model_pb2.CpModelProto() - m.ParseFromString(p.read_bytes()) - if m.HasField("objective") or m.HasField("floating_point_objective"): - # Strip ".cpsat.pb" suffix to recover bare SHA (matches - # problem_sha256 in meta.jsonl). pathlib's .stem only drops one - # extension, leaving ".cpsat". - with_obj.append(p.name[:-len(".cpsat.pb")]) - cache_path.write_text(json.dumps( - {"pb_count": pb_count, "with_objective": with_obj}, indent=2) + "\n") - print(f"objective scan: {len(with_obj)}/{pb_count} problems have objective" - f" (cached at {cache_path.relative_to(_BENCH.parent)})") - return set(with_obj) - - -def _runtime_key(d): - return (d.get("cpsat_status") or {}).get("elapsed_ms", 0) - - -def _result_key(d): - return (d.get("cpsat_status") or {}).get("result") - - -def _id_key(d): - return d["problem_sha256"] - - -def _drop_runtime_outliers(rows, k=OUTLIER_IQR_K): - ms_sorted = sorted(_runtime_key(d) for d in rows if _runtime_key(d) > 0) - n = len(ms_sorted) - if n < 4: - return list(rows), [] - q1 = ms_sorted[n // 4] - q3 = ms_sorted[(3 * n) // 4] - iqr = q3 - q1 - lo, hi = q1 - k * iqr, q3 + k * iqr - kept, dropped = [], [] - for d in rows: - ms = _runtime_key(d) - if ms <= 0 or lo <= ms <= hi: - kept.append(d) - else: - dropped.append(d) - return kept, dropped - - -def _pick(strategy, sorted_rows, n_pick): - if strategy == "center": - return _center_pick(sorted_rows, n_pick) - if strategy == "spread": - return _quintile_spread(sorted_rows, n_pick, N_BUCKETS) - if strategy == "outliers": - # stage3 uses _pick_outliers() directly; this branch should not fire. - raise ValueError("'outliers' strategy requires _pick_outliers(), not _pick()") - raise ValueError(f"unknown sample strategy: {strategy!r}") - - -def _find_outliers_csv(): - for p in _OUTLIERS_CSV_CANDIDATES: - if p.exists(): - return p - return None - - -def _load_outliers_top(csv_path): - """Read outliers_top.csv -> ordered list of {sha, residual, elapsed_ms, - n_vars, n_cons}, sorted by descending residual (already the file's order).""" - rows = [] - with open(csv_path) as f: - reader = csv.DictReader(f) - for r in reader: - try: - rows.append({ - "sha": r["_sha"], - "residual": float(r.get("_residual") or 0.0), - "elapsed_ms": float(r.get("_elapsed_ms") or 0.0), - "n_vars": int(float(r.get("_num_variables") or 0)), - "n_cons": int(float(r.get("_num_constraints") or 0)), - }) - except (KeyError, ValueError) as e: - print(f"warning: skipping malformed outlier row: {e}", - file=sys.stderr) - continue - rows.sort(key=lambda d: -d["residual"]) - return rows - - -def _pick_outliers(rows_by_sha, csv_path, all_rows=None, n_pick=None, - target_ms=None, tol_ratio=None): - """Target-ms pick for stage3: prefer outliers_top.csv entries whose - baseline elapsed_ms is closest to target_ms; if fewer than n_pick survive - the tol_ratio filter, backfill from the full problems pool (non-outlier - problems also near target_ms). - - Returns (picks, csv_rows). picks sorted by ascending |elapsed_ms-target_ms|. - csv_rows is the full csv (for outliers.json metadata). - - Returns (None, []) when csv missing — caller falls back to slow-cluster - center pick.""" - if csv_path is None: - print("warning: outliers_top.csv not found — falling back to " - "slow-cluster center pick for stage3", file=sys.stderr) - return None, [] - - if n_pick is None: - n_pick = STAGE3_N - if target_ms is None: - target_ms = STAGE3_TARGET_MS - if tol_ratio is None: - tol_ratio = STAGE3_TARGET_TOL_RATIO - - csv_rows = _load_outliers_top(csv_path) - - def _within_tol(ms): - if ms <= 0 or ms > STAGE3_MAX_BASELINE_MS: - return False - return max(ms / target_ms, target_ms / ms) <= tol_ratio - - # Pass 1: outliers within tol. - outlier_eligible = [c for c in csv_rows - if c["sha"] in rows_by_sha and _within_tol(c["elapsed_ms"])] - outlier_eligible.sort(key=lambda c: abs(c["elapsed_ms"] - target_ms)) - outlier_pick_csv = outlier_eligible[:n_pick] - picks = [rows_by_sha[c["sha"]] for c in outlier_pick_csv] - used_shas = {c["sha"] for c in outlier_pick_csv} - - # Pass 2: backfill from full problems pool near target_ms (non-outliers). - backfill_picks = [] - if len(picks) < n_pick and all_rows is not None: - decisive = (d for d in all_rows - if _id_key(d) not in used_shas - and _within_tol(_runtime_key(d)) - and _result_key(d) in _DECISIVE_RESULTS) - backfill_pool = sorted(decisive, - key=lambda d: abs(_runtime_key(d) - target_ms)) - need = n_pick - len(picks) - backfill_picks = backfill_pool[:need] - picks.extend(backfill_picks) - - print(f"outliers stage3: from {csv_path.relative_to(_BENCH.parent)}, " - f"target={target_ms}ms tol={tol_ratio}x cap={STAGE3_MAX_BASELINE_MS}ms " - f"→ {len(picks)}/{n_pick} " - f"(outliers={len(outlier_pick_csv)}, backfill={len(backfill_picks)})") - for c in outlier_pick_csv: - ratio = max(c["elapsed_ms"] / target_ms, target_ms / c["elapsed_ms"]) - print(f" outlier {c['sha'][:12]} {int(c['elapsed_ms']):>10}ms " - f"Δ={int(c['elapsed_ms'] - target_ms):+d}ms " - f"ratio={ratio:.2f}x residual={c['residual']:.3f}") - for d in backfill_picks: - ms = _runtime_key(d) - ratio = max(ms / target_ms, target_ms / ms) - print(f" backfill {_id_key(d)[:12]} {int(ms):>10}ms " - f"Δ={int(ms - target_ms):+d}ms ratio={ratio:.2f}x") - if len(picks) < n_pick: - print(f" warning: only {len(picks)} candidates within tol={tol_ratio}x " - f"of {target_ms}ms — widen STAGE3_TARGET_TOL_RATIO or change " - f"STAGE3_TARGET_MS to fill stage3") - - return picks, csv_rows - - -def _write_outliers_json(picks, csv_all): - """Write shared/outliers.json: {sha: {residual, elapsed_ms, n_vars, n_cons}} - for every entry in outliers_top.csv (not just the stage3 picks). The - evaluator uses this map to set `is_outlier` on problem records.""" - by_sha = {c["sha"]: { - "residual": c["residual"], - "elapsed_ms": c["elapsed_ms"], - "n_vars": c["n_vars"], - "n_cons": c["n_cons"], - } for c in (csv_all or [])} - stage3_shas = [_id_key(d) for d in picks] - payload = { - "stage3_sample": stage3_shas, - "outliers": by_sha, - } - _OUTLIERS_JSON.write_text(json.dumps(payload, indent=2) + "\n") - print(f"wrote {_OUTLIERS_JSON.relative_to(_BENCH.parent)} " - f"({len(by_sha)} outliers, stage3_sample={len(stage3_shas)})") - - -def _center_pick(sorted_rows, n_pick): - total = len(sorted_rows) - if total == 0 or n_pick <= 0: - return [] - if total <= n_pick: - return list(sorted_rows) - start = (total - n_pick) // 2 - return sorted_rows[start:start + n_pick] - - -def _cluster_runtime(sorted_rows, k, max_iter=100): - """1D Lloyd's k-means on runtime. Input must be runtime-sorted ascending. - Returns list of k buckets (lists of rows), ordered by ascending centroid.""" - n = len(sorted_rows) - if k <= 0: - return [] - if n == 0: - return [[] for _ in range(k)] - if n <= k: - buckets = [[r] for r in sorted_rows] - buckets += [[] for _ in range(k - n)] - return buckets - - values = [_runtime_key(d) for d in sorted_rows] - # init centroids at k-quantile midpoints over the sorted values. - centroids = [values[((2 * i + 1) * n) // (2 * k)] for i in range(k)] - labels = [0] * n - - for _ in range(max_iter): - changed = False - for i, v in enumerate(values): - 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 i, v in enumerate(values): - sums[labels[i]] += v - counts[labels[i]] += 1 - for c in range(k): - if counts[c] > 0: - centroids[c] = sums[c] / counts[c] - - order = sorted(range(k), key=lambda c: centroids[c]) - rank = {old: new for new, old in enumerate(order)} - buckets = [[] for _ in range(k)] - for i, lbl in enumerate(labels): - buckets[rank[lbl]].append(sorted_rows[i]) - return buckets - - -def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): - total = len(sorted_rows) - if total == 0 or n_pick <= 0: - return [] - if total <= n_pick: - return list(sorted_rows) - per_bucket = n_pick // n_buckets - remainder = n_pick % n_buckets - picked = [] - for b in range(n_buckets): - lo = (b * total) // n_buckets - hi = ((b + 1) * total) // n_buckets - bucket = sorted_rows[lo:hi] - if not bucket: - continue - k = per_bucket + (1 if b < remainder else 0) - if k <= 0: - continue - if k == 1: - picked.append(bucket[len(bucket) // 2]) - else: - for j in range(k): - idx = round(j * (len(bucket) - 1) / (k - 1)) - picked.append(bucket[idx]) - return picked - - -def _summary(d): - return { - "sha": _id_key(d)[:12], - "baseline_result": _result_key(d), - "baseline_ms": _runtime_key(d), - } - - -def _write_sample(path, picks, label, criteria): - path.write_text( - json.dumps( - { - "selection": f"{len(picks)} {criteria}", - "source": str(_PROBLEMS.relative_to(_BENCH.parent)), - "sha256": [_id_key(d) for d in picks], - "summary": [_summary(d) for d in picks], - }, - indent=2, - ) - + "\n" - ) - print(f"wrote {path.relative_to(_BENCH.parent)} ({len(picks)} {label})") - - -def main(): - ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - ap.add_argument( - "--optimize-only", - action="store_true", - help="Filter the sample pool to problems whose CpModelProto carries " - "an objective (drops pure-feasibility models). Default off — " - "keep all problems.", - ) - ap.add_argument( - "--small", - action="store_true", - help="small-profile build: generate stage1 + stage2 only and leave " - "stage3 / stage4 empty (small cascade runs stage1+stage2). " - "Default off — generate all four stages.", - ) - args = ap.parse_args() - - rows = _scan_raw() - if not rows: - raise SystemExit(f"no *.meta.jsonl found under {_RAW}") - print(f"scanned {len(rows)} problems") - - if args.optimize_only: - obj_shas = _has_objective_set() - if obj_shas is None: - print("warning: --optimize-only requested but ortools unavailable " - "— keeping all problems", file=sys.stderr) - else: - before = len(rows) - rows = [d for d in rows if _id_key(d) in obj_shas] - print(f"objective filter: kept {len(rows)}/{before} problems " - f"(dropped feasibility-only)") - if not rows: - raise SystemExit("no problems with objective — nothing to sample") - else: - print("objective filter: OFF (use --optimize-only to drop " - "feasibility-only problems)") - - with open(_PROBLEMS, "w") as f: - for d in rows: - f.write(json.dumps(d) + "\n") - print(f"wrote {_PROBLEMS.relative_to(_BENCH.parent)} ({len(rows)} entries)") - - candidates = [d for d in rows if _runtime_key(d) <= MAX_BASELINE_MS] - print(f"sample pool: {len(candidates)} (skipped {len(rows) - len(candidates)} " - f"with baseline_ms > {MAX_BASELINE_MS}ms)") - - candidates, outliers = _drop_runtime_outliers(candidates) - if outliers: - print(f"dropped {len(outliers)} runtime outliers (Tukey IQR k={OUTLIER_IQR_K}):") - for d in sorted(outliers, key=_runtime_key): - print(f" {_id_key(d)[:12]} {int(_runtime_key(d)):>7}ms {_result_key(d)}") - - decided_by_rt = sorted( - (d for d in candidates if _result_key(d) in _DECISIVE_RESULTS), - key=_runtime_key, - ) - n_decided = len(decided_by_rt) - - clusters = _cluster_runtime(decided_by_rt, N_BUCKETS) - pool_c12 = clusters[0] + clusters[1] - pool_c34 = clusters[2] + clusters[3] - pool_c5 = clusters[4] - - def _bucket_range(b): - if not b: - return "empty" - return f"{int(_runtime_key(b[0]))}-{int(_runtime_key(b[-1]))}ms" - - print(f"decisive-result runtime pool: {n_decided} | clusters: " + - " | ".join(f"c{i+1}({len(b)},{_bucket_range(b)})" - for i, b in enumerate(clusters))) - print(f"stage pools: c1+2={len(pool_c12)} | c3+4={len(pool_c34)} | " - f"c5={len(pool_c5)}") - - s1 = _pick(STAGE1_STRATEGY, pool_c12, STAGE1_N) - s2 = _pick(STAGE2_STRATEGY, pool_c34, STAGE2_N) - - # outliers_top.csv feeds outliers.json (is_outlier flag + large-profile - # pick source) and, when generating all stages, the stage3 sample. - rows_by_sha = {_id_key(d): d for d in rows} - outliers_csv = _find_outliers_csv() - s3_outliers, csv_all = _pick_outliers(rows_by_sha, outliers_csv, - all_rows=rows) \ - if outliers_csv else (None, []) - - if args.small: - # small-profile build: stage1 + stage2 only. stage3 (outliers → large - # profile) and stage4 (broad spread) left empty; the small cascade in - # evaluator merges stage1+stage2. - _write_outliers_json([], csv_all) - _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") - _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") - _write_sample(_STAGE3, [], "stage3", "EMPTY — --small build (stage1+stage2 cascade)") - _write_sample(_STAGE4, [], "stage4", "EMPTY — --small build (stage1+stage2 cascade)") - printed_stages = (("stage1", s1), ("stage2", s2)) - else: - # Default build: all four stages. - if s3_outliers: - s3 = s3_outliers - stage3_criteria = ( - f"outliers from {outliers_csv.name} nearest " - f"{STAGE3_TARGET_MS}ms (tol={STAGE3_TARGET_TOL_RATIO}x, " - f"cap={STAGE3_MAX_BASELINE_MS}ms; target {STAGE3_N})" - ) - else: - s3 = _center_pick(pool_c5, STAGE3_N) - stage3_criteria = ("FALLBACK: decisive runtime cluster c5 " - "(outliers_top.csv unavailable or empty)") - - _write_outliers_json(s3 if s3_outliers else [], csv_all) - - used = {_id_key(d) for d in (s1 + s2 + s3)} - broad = sorted( - (d for d in candidates if _id_key(d) not in used), - key=_runtime_key, - ) - s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) - - _write_sample(_STAGE1, s1, "stage1", "decisive runtime clusters c1+c2 (fast group)") - _write_sample(_STAGE2, s2, "stage2", "decisive runtime clusters c3+c4 (mid group)") - _write_sample(_STAGE3, s3, "stage3", stage3_criteria) - _write_sample(_STAGE4, s4, "stage4", "broad runtime spread, dedup vs stage1-3") - printed_stages = (("stage1", s1), ("stage2", s2), ("stage3", s3), ("stage4", s4)) - - for label, picks in printed_stages: - print(f"\n{label}:") - for d in picks: - print(f" {_id_key(d)[:12]} " - f"{str(_result_key(d)):<10} " - f"{int(_runtime_key(d)):>7}ms") - - # ---- large profile sample (OPENEVOLVE_PROFILE=large) ---- - # Single sample file with STAGE1_LARGE_N outliers, sorted by - # STAGE1_LARGE_SORT_BY ("elapsed_ms" → slowest baseline first; "residual" - # → highest log10 deviation first). evaluator dispatches every cascade - # stage entry point to one outlier-set evaluation, so no stage2/3/4 split. - if csv_all: - eligible = [c for c in csv_all if c["sha"] in rows_by_sha] - if STAGE1_LARGE_SORT_BY == "elapsed_ms": - eligible.sort(key=lambda c: -c["elapsed_ms"]) - elif STAGE1_LARGE_SORT_BY == "residual": - eligible.sort(key=lambda c: -c["residual"]) - else: - raise ValueError( - f"unknown STAGE1_LARGE_SORT_BY: {STAGE1_LARGE_SORT_BY!r} " - f"(expected 'elapsed_ms' or 'residual')" - ) - large_picks = eligible[:STAGE1_LARGE_N] - large_rows = [rows_by_sha[c["sha"]] for c in large_picks] - else: - large_picks = [] - large_rows = [] - _write_sample( - _STAGE1_LARGE, large_rows, "stage1_large", - f"top-{STAGE1_LARGE_N} outlier(s) from outliers_top.csv " - f"sorted by {STAGE1_LARGE_SORT_BY} (W=8 tuning set)", - ) - - # Clean up stale empty stage{2,3,4}_large files from older builds — they - # would be picked up by evaluator's path resolver and produce stale empty - # pass-throughs, masking real outlier scores. - for stale in ( - _HERE / "shared" / "stage2_large_sample.json", - _HERE / "shared" / "stage3_large_sample.json", - _HERE / "shared" / "stage4_large_sample.json", - ): - if stale.exists(): - stale.unlink() - print(f"removed stale {stale.relative_to(_BENCH.parent)}") - - print(f"\nstage1_large ({len(large_rows)} outliers):") - for d in large_rows: - print(f" {_id_key(d)[:12]} " - f"{str(_result_key(d)):<10} " - f"{int(_runtime_key(d)):>7}ms") - - -if __name__ == "__main__": - main() diff --git a/input/cpsat-bench/evolve/cache/stage1_sample.json b/input/cpsat-bench/evolve/cache/stage1_sample.json new file mode 100644 index 0000000000..344e6096a8 --- /dev/null +++ b/input/cpsat-bench/evolve/cache/stage1_sample.json @@ -0,0 +1,79 @@ +{ + "selection": "10 from 352 candidates", + "criteria": "kmeans-clusters=[0, 1] feature=features.num_constraints spread=quintile", + "source": "cpsat-bench/problems.jsonl", + "sha256": [ + "5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5", + "d7e09c73e7c387ce90ed94c06031780e15353ec9e09ac3a8d2ce6cb1af0dc6f9", + "2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137", + "cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4", + "cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4", + "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", + "41f6ecf9ca6462dcb7ca49e028b2970175579c0250058e0430e979a04435bf09", + "7a78d4951c1a8b67cebf1dd62ec358ef792f9fd6000319b75cda30b8a24775e6", + "6396211e6842d6969cce80194013fff987eb3e308c435eccd942b5007dec4639", + "ba0fe698c28be555ec428f05daa2aa901ecf9694c6c23043b76a91a7a847b111" + ], + "summary": [ + { + "sha": "5976726485c2", + "baseline_result": "OPTIMAL", + "baseline_ms": 105.102, + "features.num_constraints": 7361 + }, + { + "sha": "d7e09c73e7c3", + "baseline_result": "OPTIMAL", + "baseline_ms": 688.182, + "features.num_constraints": 20658 + }, + { + "sha": "2c283b1f086e", + "baseline_result": "OPTIMAL", + "baseline_ms": 1361.71, + "features.num_constraints": 20886 + }, + { + "sha": "cda37747aa2a", + "baseline_result": "OPTIMAL", + "baseline_ms": 1928.9, + "features.num_constraints": 15385 + }, + { + "sha": "cda37747aa2a", + "baseline_result": "OPTIMAL", + "baseline_ms": 2872.95, + "features.num_constraints": 15385 + }, + { + "sha": "303a6733c9ec", + "baseline_result": "OPTIMAL", + "baseline_ms": 3530.81, + "features.num_constraints": 20766 + }, + { + "sha": "41f6ecf9ca64", + "baseline_result": "OPTIMAL", + "baseline_ms": 5144.56, + "features.num_constraints": 21983 + }, + { + "sha": "7a78d4951c1a", + "baseline_result": "OPTIMAL", + "baseline_ms": 7019.67, + "features.num_constraints": 30546 + }, + { + "sha": "6396211e6842", + "baseline_result": "OPTIMAL", + "baseline_ms": 8973.46, + "features.num_constraints": 31882 + }, + { + "sha": "ba0fe698c28b", + "baseline_result": "OPTIMAL", + "baseline_ms": 25129, + "features.num_constraints": 15565 + } + ] +} diff --git a/input/cpsat-bench/evolve/cache/stage2_sample.json b/input/cpsat-bench/evolve/cache/stage2_sample.json new file mode 100644 index 0000000000..bf1bd8e7a4 --- /dev/null +++ b/input/cpsat-bench/evolve/cache/stage2_sample.json @@ -0,0 +1,79 @@ +{ + "selection": "10 from 332 candidates", + "criteria": "kmeans-clusters=[2, 3] feature=features.num_constraints spread=quintile", + "source": "cpsat-bench/problems.jsonl", + "sha256": [ + "4c230e355482c3652bd60bf83cf4ad77382cdb0b9abe08c27007683545e8f071", + "9c3c55cd54e6074a7e2732ccfaac2e90698141e7e9fb30c946657a21fb186061", + "6ecbb5bc3a035ad7b51348fac58112aa35296e0e6ae9405119a1296fb9384045", + "e5c9e667fbee60ca8a589904e1a7afc30c81394efd58e388a9f63470fa4ea406", + "b13bb2432694ba5fe9768abc03525698986ba13ffe70a7112b90bd7fbad02aec", + "f7041abae337b502aa03618b1c0f40cc693acb9c0bd48efe21b41c42a7849bef", + "f77cdd0764d8aa490133369275f4d62d71f47133f6102d90caa9a799c69ddc63", + "f5931bc8ee19786a4ade31e56bdc19d415c4dbfa42da07785765944fe6748326", + "a46c87c567e8ea0a6fa70198b83d8523a69f38448aeafbb35a956a845987993a", + "58d58a76371ef7762bf96386ce5d2677d1bfc7d7df745d40f4a02bd30006662d" + ], + "summary": [ + { + "sha": "4c230e355482", + "baseline_result": "OPTIMAL", + "baseline_ms": 3021.82, + "features.num_constraints": 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"features.num_constraints": 61665 + }, + { + "sha": "38e83ff25869", + "baseline_result": "OPTIMAL", + "baseline_ms": 118894, + "features.num_constraints": 135485 + } + ] +} diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index f1bd22660a..e7820b47d8 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -17,18 +17,50 @@ bench: iters: 40 - dir: phase5_custom_subsolvers iters: 40 - unified_prepare_script: prepare_phase_unified.py - # The unified-prepare script materializes phase4_unified from phase{1,2,3} - # winners. phase4 is no longer the last phase (phase5 is), so the prep must - # fire before phase4 specifically, not "before the last phase". When unset, - # run_phase.sh falls back to the legacy "before last phase" trigger. + + # 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" - # optional script-name overrides (defaults shown): - # rebaseline_script: rebaseline_local.py - # extract_best_script: extract_best.py - # build_samples_script: build_samples.py + + # === 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: kmeans # kmeans | quintile | thresholds + feature: features.num_constraints + n_clusters: 5 + max_baseline_ms: 120000 # drop outliers > 120s from sample pool + 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 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. diff --git a/input/cpsat-bench/evolve/extract_best.py b/input/cpsat-bench/evolve/extract_best.py deleted file mode 100644 index 033b8c9056..0000000000 --- a/input/cpsat-bench/evolve/extract_best.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Thin wrapper: calls _lib.extract_best.main with cpsat-bench phase map. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_SHARED = _HERE / "shared" -_INPUT_DIR = _HERE.parents[1] - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.extract_best import main # noqa: E402 - -PHASE_DIRS = { - 1: "phase1_search", - 2: "phase2_presolve", - 3: "phase3_lp_cuts", - # phase4 is no longer terminal (phase5 follows it), so it gets extracted to - # phase4_best.json / phase4_buckets.json / phase4_stage3.json for phase5 to - # inherit. phase5 (custom subsolvers) is the terminal phase — no extract. - 4: "phase4_unified", -} - -if __name__ == "__main__": - main(_HERE, _SHARED, PHASE_DIRS) diff --git a/input/cpsat-bench/evolve/final_verify.py b/input/cpsat-bench/evolve/final_verify.py deleted file mode 100644 index 1c38b071f2..0000000000 --- a/input/cpsat-bench/evolve/final_verify.py +++ /dev/null @@ -1,334 +0,0 @@ -""" -Final verification for cpsat-bench: on a final-test sample, measure LOCAL -baseline elapsed_ms (+ objective_value) and then run the optimized program. -Report per-problem cost_ratio + time speedup using the fresh local baseline -(not the raw-data baseline recorded on a different machine). - -Usage: - python final_verify.py - -Example: - python input/cpsat-bench/evolve/final_verify.py \\ - input/cpsat-bench/evolve/phase4_unified/openevolve_output/best/best_program.py - -Sample selection (in priority order): - 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. - Hand-edit or generate this to pin a specific subset for verification. - 2. Fall back to ALL problems in problems.jsonl. - -Order of operations: - for each problem p in final sample: - run BASELINE on p → record base_ms_local, base_obj_local - run params on p → record variant_ms, variant_obj - cost_ratio = base_obj_local / variant_obj (cost mode, minimize) - speedup = base_ms_local / variant_ms (when status decisive) - -Baseline + variant are run back-to-back per problem so they share the same -warm cache / system noise. Concurrency = config parallel_solvers (taskset -pinned via OPENEVOLVE_CORE_RANGE or 1..N). -""" -import importlib.util -import inspect -import json -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 / "shared")) - -from baseline_params import BASELINE, LOCKED # noqa: E402 -from score import score # noqa: E402 -from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" -_OUTLIERS_JSON = _HERE / "shared" / "outliers.json" - -TIMEOUT_S = 300 -_DECISIVE = ("OPTIMAL", "FEASIBLE") - - -def _load_outlier_shas(): - if not _OUTLIERS_JSON.exists(): - return set() - try: - d = json.loads(_OUTLIERS_JSON.read_text()) - except (json.JSONDecodeError, OSError): - return set() - return set(d.get("outliers") or {}) - - -def _supports_kwargs(fn, *kwargs): - 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(name in params_ for name in kwargs) - - -def _resolve_params(fn, problem, stage_name): - kwargs = {} - if _supports_kwargs(fn, "problem"): - kwargs["problem"] = problem - if _supports_kwargs(fn, "stage"): - kwargs["stage"] = stage_name - return fn(**kwargs) if kwargs else fn() - - -def _load_program(program_path): - spec = importlib.util.spec_from_file_location("program", program_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - if not hasattr(module, "get_params"): - print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) - sys.exit(2) - return module - - -def _load_problem_index(): - outliers = _load_outlier_shas() - idx = {} - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - sha = d["problem_sha256"] - features = d.get("features") or {} - idx[sha] = { - "sha": sha, - "problem_filename": d["problem_filename"], - "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), - "raw_result": (d.get("cpsat_status") or {}).get("result"), - "num_variables": int(features.get("num_variables") or 0), - "num_constraints": int(features.get("num_constraints") or 0), - "num_bool": int(features.get("num_bool") or 0), - "num_int": int(features.get("num_int") or 0), - "is_outlier": sha in outliers, - } - return idx - - -def _resolve_sample(idx): - if _FINAL_SAMPLE.exists(): - shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) - source = f"shared/final_sample.json ({len(shas)} SHAs)" - else: - shas = list(idx.keys()) - source = f"problems.jsonl (full {len(shas)})" - metas = [] - for sha in shas: - meta = idx.get(sha) - if meta is None: - print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) - sys.exit(2) - pb = _RAW_DIR / meta["problem_filename"] - if not pb.exists(): - print(f"ERROR: missing {pb}", file=sys.stderr) - sys.exit(2) - metas.append((meta, pb)) - return metas, source - - -def main(): - if len(sys.argv) != 2: - print(__doc__, file=sys.stderr) - return 2 - - program_path = pathlib.Path(sys.argv[1]).resolve() - if not program_path.exists(): - print(f"ERROR: {program_path} not found", file=sys.stderr) - return 2 - - program = _load_program(program_path) - # final_verify treats stage as "final"; per-problem resolution still - # applies SIZE_BUCKETS but skips STAGE3_OVERRIDES (which is stage3-gated). - variant_params = _resolve_params(program.get_params, None, "final") - # Use the program's PHASE_LOCKED if it exposes one (later phases typically - # lock num_search_workers higher than baseline); fall back to global LOCKED. - _phase_locked = getattr(program, "PHASE_LOCKED", None) - _lock = _phase_locked if isinstance(_phase_locked, dict) else LOCKED - violations = {k: variant_params.get(k) for k in _lock - if variant_params.get(k) != _lock[k]} - if violations: - print(f"ERROR: locked params violated: {violations}", file=sys.stderr) - return 2 - - idx = _load_problem_index() - metas, source = _resolve_sample(idx) - - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=1) + 1)) - workers_per_solve = int(variant_params.get("num_search_workers", 1) or 1) - blocks = alloc_core_blocks(cores, workers_per_solve) - if not blocks: - blocks = [list(cores)] if cores else [None] - n_parallel = min(len(blocks), len(metas)) - blocks = blocks[:n_parallel] - # Baseline run still uses workers=1 (single core). Give it the first core - # of each block so it's pinned within the same NUMA neighborhood as the - # variant for fair comparison. - baseline_cores = [(b[0] if isinstance(b, (list, tuple)) and b else b) for b in blocks] - - print(f"final verify: {program_path}") - print(f" sample : {source}") - print(f" params : {len(variant_params)} keys, " - f"{sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} differ from BASELINE") - print(f" parallel solvers : {n_parallel} variant_workers={workers_per_solve} blocks={blocks}") - print(f" per-problem timeout : {TIMEOUT_S}s × 2 (baseline + variant)") - print() - - def _measure(idx_meta): - i, meta, pb = idx_meta - slot = i % n_parallel if n_parallel > 0 else 0 - v_core = blocks[slot] if n_parallel > 0 else None - b_core = baseline_cores[slot] if n_parallel > 0 else None - # Per-problem param resolution. PHASE_LOCKED keys re-pinned defensively. - per_params = _resolve_params(program.get_params, meta, "final") - for k, v in _lock.items(): - per_params[k] = v - if "num_search_workers" in variant_params: - per_params["num_search_workers"] = variant_params["num_search_workers"] - b = run_cpsat(pb, BASELINE, TIMEOUT_S, cpu_core=b_core) - v = run_cpsat(pb, per_params, TIMEOUT_S, cpu_core=v_core) - return i, meta, b, v - - tasks = [(i, meta, pb) for i, (meta, pb) in enumerate(metas)] - t_start = time.monotonic() - completed = [] - if n_parallel == 1: - for t in tasks: - completed.append(_measure(t)) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_measure, t) for t in tasks] - for fut in as_completed(futures): - completed.append(fut.result()) - completed.sort(key=lambda x: x[0]) - elapsed = time.monotonic() - t_start - - results = [] - for i, meta, b, v in completed: - base_ms_local = int(b.get("elapsed_ms", 0)) - base_result = b.get("result", "Unknown") - base_obj = b.get("objective") - var_ms = int(v.get("elapsed_ms", 0)) - var_result = v.get("result", "Unknown") - var_obj = v.get("objective") - var_invalid = v.get("invalid_param") - b_dt = (b.get("stats") or {}).get("deterministic_time") - v_dt = (v.get("stats") or {}).get("deterministic_time") - - wall_speedup = base_ms_local / max(var_ms, 1) - if b_dt and v_dt and b_dt > 0 and v_dt > 0: - dtime_speedup = float(b_dt) / float(v_dt) - speedup = dtime_speedup - speedup_src = "dt" - else: - speedup = wall_speedup - speedup_src = "wall" - if var_invalid: - flag = f" INVALID_PARAM={var_invalid}" - speedup = 0.0 - cost_ratio = 0.0 - elif base_result not in _DECISIVE and var_result not in _DECISIVE: - # Both failed to reach decisive status — uncomparable, NOT a regression. - # score.py skips this from geomean. - flag = f" BOTH_NONDECISIVE ({base_result}=base, {var_result}=variant) — skipped from score" - cost_ratio = 0.0 - elif base_result in _DECISIVE and var_result not in _DECISIVE: - # Baseline solved, variant didn't → real regression. - flag = f" REGRESSION (base={base_result} variant={var_result})" - speedup = 0.0 - cost_ratio = 0.0 - elif base_result not in _DECISIVE and var_result in _DECISIVE: - # Variant solved a problem baseline couldn't — bonus, but cost mode - # can't ratio it. Show but don't score. - flag = f" VARIANT_WIN_UNCOMPARABLE (base={base_result})" - cost_ratio = 0.0 - else: - if base_obj is not None and var_obj is not None: - cost_ratio = (float(base_obj) + 1e-9) / (float(var_obj) + 1e-9) - else: - cost_ratio = 1.0 - flag = "" - - print( - f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} " - f"base_local={base_result:<10}/{base_ms_local:>7}ms" - f"{('/obj=' + format(base_obj, '.3g')) if base_obj is not None else ''} " - f"variant={var_result:<10}/{var_ms:>7}ms" - f"{('/obj=' + format(var_obj, '.3g')) if var_obj is not None else ''} " - f"speedup={speedup:.2f}x[{speedup_src}] " - f"wall={wall_speedup:.2f}x cost_ratio={cost_ratio:.3f}{flag}", - flush=True, - ) - results.append({ - "sha": meta["sha"], - "input_file": meta["problem_filename"], - "baseline_ms": base_ms_local, - "baseline_result": base_result, - "baseline_objective": base_obj, - "result": var_result, - "elapsed_ms": var_ms, - "objective": var_obj, - "timeout": bool(v.get("timeout")), - "raw_baseline_ms": meta["raw_ms"], - "stats": v.get("stats") or {}, - "baseline_stats": b.get("stats") or {}, - }) - - metrics = score(results) - print() - print("== summary (cost-mode, vs fresh LOCAL baseline) ==") - print(f" total problems : {metrics['total']}") - print(f" comparable : {metrics['comparable']} " - f"(baseline reached decisive status; geomean computed over this subset)") - print(f" uncomparable : {metrics['uncomparable']} " - f"(baseline non-decisive; skipped from score)") - print(f" solved : {metrics['solved']}/{metrics['comparable']}") - print(f" regressions : {metrics['regressions']} " - f"(baseline OK, variant failed)") - print(f" geomean (cost×dtime): {metrics['geomean_speedup']:.3f} " - f"(dtime_used={metrics.get('dtime_used', 0)}/" - f"{metrics.get('dtime_used', 0) + metrics.get('dtime_fallback', 0)})") - print(f" geomean (cost×wall) : {metrics.get('geomean_wall_speedup', 0.0):.3f} " - f"(diagnostic)") - print(f" solved_rate : {metrics['solved_rate']:.3f} (over comparable)") - print(f" efficiency : {metrics.get('efficiency', 1.0):.3f}") - print(f" combined_score : {metrics['combined_score']:.3f}") - print(f" wall-clock : {elapsed:.1f}s") - - out_path = program_path.parent / "final_verify.json" - out_path.write_text(json.dumps({ - "program": str(program_path), - "sample_source": source, - "metrics": metrics, - "per_problem": [ - { - "sha": r["sha"][:12], - "base_result": r["baseline_result"], - "got_result": r["result"], - "base_local_ms": r["baseline_ms"], - "variant_ms": r["elapsed_ms"], - "base_obj": r["baseline_objective"], - "variant_obj": r["objective"], - "raw_baseline_ms": r["raw_baseline_ms"], - "timeout": r["timeout"], - } - for r in results - ], - }, indent=2) + "\n") - print(f" wrote {out_path}") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/input/cpsat-bench/evolve/materialize_final.py b/input/cpsat-bench/evolve/materialize_final.py deleted file mode 100644 index 9a8a07ca60..0000000000 --- a/input/cpsat-bench/evolve/materialize_final.py +++ /dev/null @@ -1,224 +0,0 @@ -""" -Materialize a standalone final_program.py from the phase5 winner plus all -inherited prior-phase winners. - -Inputs: - shared/baseline_params.py BASELINE dict - shared/phase4_best.json inherited GLOBAL_OVERRIDES (1+2+3 unified) - shared/phase4_buckets.json inherited SIZE_BUCKETS - shared/phase4_stage3.json inherited STAGE3_OVERRIDES - shared/has_objective_cache.json SHA set used by needs_objective gating - phase5_custom_subsolvers/openevolve_output/best/best_program.py - CUSTOM_SUBSOLVERS, STAGE3_CUSTOM_SUBSOLVERS (Python literals only) - -Output: - final_program.py — self-contained: no JSON loads, no relative imports. - Exposes BASELINE/GLOBAL_OVERRIDES/SIZE_BUCKETS/ - STAGE3_OVERRIDES/CUSTOM_SUBSOLVERS/ - STAGE3_CUSTOM_SUBSOLVERS/PHASE_LOCKED constants and - get_params(problem=None, stage=None) using the exact - phase5 merge logic. - -Usage: - python materialize_final.py [--output PATH] [--phase5-best PATH] -""" -import argparse -import importlib.util -import json -import pathlib -import pprint -import sys -import textwrap - -_HERE = pathlib.Path(__file__).resolve().parent -_SHARED = _HERE / "shared" -_PHASE5_BEST = _HERE / "phase5_custom_subsolvers/openevolve_output/best/best_program.py" -_DEFAULT_OUT = _HERE / "final_program.py" - - -def _load_phase5_specs(phase5_path): - """Import phase5 best_program just to read CUSTOM_SUBSOLVERS and - STAGE3_CUSTOM_SUBSOLVERS. Those are static literals so the snapshot's - broken _SHARED relative path does not matter — the JSON loads that fail - inside the module only affect _PHASE4 / _PHASE4_BUCKETS / _PHASE4_STAGE3, - which we re-read directly from shared/ here.""" - spec = importlib.util.spec_from_file_location("phase5_best", phase5_path) - module = importlib.util.module_from_spec(spec) - sys.path.insert(0, str(_SHARED)) - spec.loader.exec_module(module) - return module.CUSTOM_SUBSOLVERS, module.STAGE3_CUSTOM_SUBSOLVERS - - -def _format_buckets(buckets): - 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) - - -_TEMPLATE = '''\ -""" -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_rel} -""" - -BASELINE = {baseline} - -# Inherited from shared/phase4_best.json (union of phase1+2+3 winners + phase4 -# unified pass). -GLOBAL_OVERRIDES = {global_overrides} - -# Inherited from shared/phase4_buckets.json. Selected by num_constraints -# (first bucket whose upper > num_constraints wins). -SIZE_BUCKETS = {size_buckets} - -# 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 = {stage3_overrides} - -# Phase5 custom subsolvers (always-on, gated by needs_objective / -# min_constraints / max_constraints). -CUSTOM_SUBSOLVERS = {custom_subsolvers} - -# Phase5 stage3-only extra custom subsolvers (same dead-code caveat as -# STAGE3_OVERRIDES). -STAGE3_CUSTOM_SUBSOLVERS = {stage3_custom_subsolvers} - -# 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({objective_shas}) - -# 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 -''' - - -def main(): - ap = argparse.ArgumentParser(description=__doc__.splitlines()[1]) - ap.add_argument("--output", default=str(_DEFAULT_OUT), - help=f"output path (default: {_DEFAULT_OUT.relative_to(_HERE)})") - ap.add_argument("--phase5-best", default=str(_PHASE5_BEST), - help="phase5 best_program.py path") - args = ap.parse_args() - - sys.path.insert(0, str(_SHARED)) - from baseline_params import BASELINE # noqa: E402 - - phase4_best = json.loads((_SHARED / "phase4_best.json").read_text()) - phase4_buckets_raw = json.loads((_SHARED / "phase4_buckets.json").read_text()) - phase4_buckets = [(None if u is None else u, ov) for u, ov in phase4_buckets_raw] - phase4_stage3 = json.loads((_SHARED / "phase4_stage3.json").read_text()) - - obj_cache_path = _SHARED / "has_objective_cache.json" - objective_shas = set(json.loads(obj_cache_path.read_text()).get("with_objective") or []) - - custom_subsolvers, stage3_custom_subsolvers = _load_phase5_specs( - pathlib.Path(args.phase5_best) - ) - - rendered = _TEMPLATE.format( - phase5_rel=pathlib.Path(args.phase5_best).resolve().relative_to(_HERE), - baseline=pprint.pformat(dict(BASELINE), width=100, sort_dicts=True), - global_overrides=pprint.pformat(phase4_best, width=100, sort_dicts=True), - size_buckets=_format_buckets(phase4_buckets), - stage3_overrides=pprint.pformat(phase4_stage3, width=100, sort_dicts=True), - custom_subsolvers=pprint.pformat(custom_subsolvers, width=100, sort_dicts=True), - stage3_custom_subsolvers=pprint.pformat(stage3_custom_subsolvers, width=100, sort_dicts=True), - objective_shas=pprint.pformat(sorted(objective_shas), width=100), - ) - - out = pathlib.Path(args.output) - out.write_text(rendered) - print(f"wrote {out.relative_to(_HERE)} " - f"({len(BASELINE)} baseline + {len(phase4_best)} global + " - f"{len(phase4_buckets)} buckets + {len(phase4_stage3)} stage3 + " - f"{len(custom_subsolvers)} custom subsolvers + " - f"{len(stage3_custom_subsolvers)} stage3 custom subsolvers)") - - -if __name__ == "__main__": - main() 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 index d950e72dd0..6532b0706a 100644 --- a/input/cpsat-bench/evolve/phase1_search/initial_program.py +++ b/input/cpsat-bench/evolve/phase1_search/initial_program.py @@ -1,29 +1,13 @@ """ Phase 1: tune CP-SAT search / subsolver knobs. -Reference: shared/cpsat_params_reference.md - (full proto field listing — consult before adding keys; invalid names - surface as `invalid_param` and zero the score.) - -Three evolution surfaces live in this file's EVOLVE-BLOCK: - GLOBAL_OVERRIDES — applied to every problem - SIZE_BUCKETS — applied conditionally on `num_constraints` - (small / medium / large; first match wins). - The dataset spans ~7k–246k constraints; thresholds - below split it ~evenly. Adjust freely. - STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND the problem - is in the outlier set (Statistics/outliers_top.csv). - Tune knobs that help long-tail / hard outliers - without regressing fast/mid problems. - -Targeted namespace for phase 1 (search/subsolvers): - 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. This phase pins num_search_workers=1 so other -knobs are evaluated without multi-thread / multi-subsolver noise. Phase 3 -raises the worker count to explore subsolver-mix effects. +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. @@ -32,14 +16,31 @@ import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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 -from baseline_params import BASELINE # noqa: E402 +BASELINE = params_catalog.load_for_bench(_BENCH).defaults -# OPENEVOLVE_PROFILE=large → run this phase at W=8 (outlier tuning track). -# Default (small) keeps the historical W=1 search-only sweep. +# 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 = { @@ -49,34 +50,19 @@ # EVOLVE-BLOCK-START -GLOBAL_OVERRIDES = { - "extra_subsolvers": ["default_lp", "no_lp"], - "ignore_subsolvers": ["max_lp"], - "interleave_search": True, - "use_feasibility_jump": False, - "use_feasibility_pump": False, -} - -# (max_num_constraints_exclusive, override_dict). First match wins; the final -# entry must use float("inf") as the sentinel for "no upper bound". +GLOBAL_OVERRIDES = {} SIZE_BUCKETS = [ - (50_000, {}), # small problems — keep GLOBAL_OVERRIDES intact - (150_000, {}), # medium - (float("inf"), {}), # large + (50_000, {}), + (150_000, {}), + (float("inf"), {}), ] - -# Applied ONLY when stage == "stage3" AND problem["is_outlier"] is True. -# Outliers are pre-identified in Statistics/outliers_top.csv (residual log10 -# slowdown vs the runtime ~ vars^a * cons^b regression). Their search budget -# is dominated by conflict learning + LP iter; use this dict to add aggressive -# settings that would hurt simple problems but pay off on hard ones. STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def _bucket_override(num_constraints): +def _bucket_override(size): for upper, override in SIZE_BUCKETS: - if num_constraints < upper: + if size < upper: return override return {} @@ -85,21 +71,18 @@ 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))) + 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) # re-enforce phase lock last + p.update(PHASE_LOCKED) return p def get_phase_overrides(): - """Used by extract_best.py — returns ONLY this phase's evolved GLOBAL dict. - SIZE_BUCKETS / STAGE3_OVERRIDES are extracted via the helpers below.""" return dict(GLOBAL_OVERRIDES) def get_phase_size_buckets(): - """Returns list[(upper_exclusive, override_dict)] for chaining to next phase.""" return [(u, dict(d)) for u, d in SIZE_BUCKETS] diff --git a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py index 904978ccbd..fa3c47b36f 100644 --- a/input/cpsat-bench/evolve/phase2_presolve/initial_program.py +++ b/input/cpsat-bench/evolve/phase2_presolve/initial_program.py @@ -1,44 +1,44 @@ """ -Phase 2: tune CP-SAT presolve / probing knobs. - -Reference: shared/cpsat_params_reference.md (presolve section, lines ~132-162; -LP/cuts section may also be relevant for probing-LP interactions.) - -Three evolution surfaces in this file's EVOLVE-BLOCK: - GLOBAL_OVERRIDES — applied to every problem - SIZE_BUCKETS — applied conditionally on `num_constraints` - (large problems often want LIGHTER presolve to avoid - spending budget on substitution work that doesn't pay - off; small problems can afford full probing.) - STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND outlier. - Outliers have heavy presolve-time / search-time - imbalance — extra knobs here let you trade presolve - depth vs search depth without hurting normal cases. - -Targeted namespace: - cp_model_probing_level, cp_model_presolve, symmetry_level, - presolve_use_bva, presolve_bve_threshold, presolve_substitution_level, - max_presolve_iterations, presolve_probing_deterministic_time_limit, - find_big_linear_overlap, infer_all_diffs, mip_presolve_level. - -Inherits phase1 winners from shared/phase1_*.json. Like phase1, workers stays -at 1 — presolve effects must be measured without multi-worker search masking -them. Phase 3 raises workers. - -Do NOT modify locked keys (random_seed, num_search_workers). +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 json import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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 baseline_params import BASELINE # noqa: E402 +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults -# OPENEVOLVE_PROFILE=large → run this phase at W=8 (outlier tuning track). _LARGE_PROFILE = (os.environ.get("OPENEVOLVE_PROFILE", "small").strip().lower() == "large") PHASE_LOCKED = { @@ -47,69 +47,32 @@ } -def _load_prev_dict(name): - p = _SHARED / name - if p.exists(): - return json.loads(p.read_text()) - return {} - - -def _load_prev_buckets(name): - """Returns list[(upper, override_dict)] or None if file missing. - - File schema: [[upper_or_null, override_dict], ...] (JSON cannot encode inf, - so the writer stores null for the float('inf') sentinel).""" - p = _SHARED / name - if not p.exists(): - return None - raw = json.loads(p.read_text()) - out = [] - for upper, override in raw: - out.append((float("inf") if upper is None else upper, override)) - return out - - -_PHASE1 = _load_prev_dict("phase1_best.json") -_PHASE1_BUCKETS = _load_prev_buckets("phase1_buckets.json") -_PHASE1_STAGE3 = _load_prev_dict("phase1_stage3.json") - - # EVOLVE-BLOCK-START -GLOBAL_OVERRIDES = { - "cp_model_probing_level": 1, -} - +GLOBAL_OVERRIDES = {} SIZE_BUCKETS = [ (50_000, {}), (150_000, {}), (float("inf"), {}), ] - STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def _merge_bucket(num_constraints): - """Merge phase1 bucket + phase2 bucket. Phase2 wins on conflicts.""" - out = {} - for buckets in (_PHASE1_BUCKETS or [], SIZE_BUCKETS): - for upper, override in buckets: - if num_constraints < upper: - out.update(override) - break - return out +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(_PHASE1) p.update(GLOBAL_OVERRIDES) if problem is not None: - p.update(_merge_bucket(int(problem.get("num_constraints") or 0))) + p.update(_bucket_override(int(problem.get("size") or 0))) if stage == "stage3" and problem.get("is_outlier"): - p.update(_PHASE1_STAGE3) p.update(STAGE3_OVERRIDES) - p.update(PHASE_LOCKED) # phase1 may have stored workers; re-pin to 1 + p.update(PHASE_LOCKED) return p diff --git a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py index b14ed1f539..bc4221768a 100644 --- a/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py +++ b/input/cpsat-bench/evolve/phase3_lp_cuts/initial_program.py @@ -1,123 +1,76 @@ """ -Phase 3: tune CP-SAT LP / cuts / MIP-bridge AND subsolver-mix knobs. - -Reference: shared/cpsat_params_reference.md (sections "LP/cuts" lines -~164-198, "MIP bridge" ~200-214, "parallel/workers" ~10-32.) - -Worker count is RAISED to PHASE3_WORKERS (default 8) for this phase: many -subsolvers (max_lp, no_lp, core, quick_restart, reduced_costs, lb_tree_search, -probing_search, …) only activate when num_search_workers is large enough. -Phase 3's job is to find the best subsolver combination + LP/cuts tuning that -works at parallel-search scale. - -Three evolution surfaces (see EVOLVE-BLOCK): - GLOBAL_OVERRIDES — applied to every problem - SIZE_BUCKETS — applied conditionally on `num_constraints`. - LARGE problems (≥150k constraints) often suffer from - LP iteration explosion; smaller max_num_cuts, lower - cut_level, dropping `max_lp` from subsolvers can pay - off here. SMALL problems can afford richer LP work. - STAGE3_OVERRIDES — applied ONLY when stage == "stage3" AND outlier. - Outliers spend ~30x more LP iterations than baseline - median (see Statistics/outliers_report.txt). Use this - to ship outlier-only LP/cut tuning. - -Targeted namespace: - max_num_cuts, cut_level, linearization_level, - mip_max_bound, mip_var_scaling, mip_check_precision, mip_drop_tolerance, - extra_subsolvers, ignore_subsolvers, diversify_lns_params, repair_hint, - add_*_cuts toggles, root_lp_iterations, exploit_*_lp_solution. - -Inherits phase1+phase2 winners (+ their size_buckets / stage3_overrides if -present). num_search_workers is re-pinned to PHASE3_WORKERS at the end of -get_params() — phases 1/2 ran at workers=1 so their wins still need to be -re-validated at workers=PHASE3_WORKERS here. - -Do NOT modify locked keys (random_seed, num_search_workers). +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 json +import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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) + ) + -from baseline_params import BASELINE # noqa: E402 +_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 -PHASE3_WORKERS = 8 PHASE_LOCKED = { "random_seed": 0, - "num_search_workers": PHASE3_WORKERS, + "num_search_workers": 8, } -def _load_prev_dict(name): - p = _SHARED / name - if p.exists(): - return json.loads(p.read_text()) - return {} - - -def _load_prev_buckets(name): - p = _SHARED / 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] - - -_PHASE1 = _load_prev_dict("phase1_best.json") -_PHASE2 = _load_prev_dict("phase2_best.json") -_PHASE1_BUCKETS = _load_prev_buckets("phase1_buckets.json") -_PHASE2_BUCKETS = _load_prev_buckets("phase2_buckets.json") -_PHASE1_STAGE3 = _load_prev_dict("phase1_stage3.json") -_PHASE2_STAGE3 = _load_prev_dict("phase2_stage3.json") - - # EVOLVE-BLOCK-START -GLOBAL_OVERRIDES = { - "max_num_cuts": 3000, - "cut_level": 1, - "mip_max_bound": 1e+07, - "mip_var_scaling": 1, - "mip_check_precision": 1e-06, - "mip_drop_tolerance": 1e-07, -} - +GLOBAL_OVERRIDES = {} SIZE_BUCKETS = [ (50_000, {}), (150_000, {}), (float("inf"), {}), ] - STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def _merge_bucket(num_constraints): - out = {} - for buckets in (_PHASE1_BUCKETS or [], _PHASE2_BUCKETS or [], SIZE_BUCKETS): - for upper, override in buckets: - if num_constraints < upper: - out.update(override) - break - return out +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(_PHASE1) - p.update(_PHASE2) p.update(GLOBAL_OVERRIDES) if problem is not None: - p.update(_merge_bucket(int(problem.get("num_constraints") or 0))) + p.update(_bucket_override(int(problem.get("size") or 0))) if stage == "stage3" and problem.get("is_outlier"): - p.update(_PHASE1_STAGE3) - p.update(_PHASE2_STAGE3) p.update(STAGE3_OVERRIDES) - p.update(PHASE_LOCKED) # pin workers=PHASE3_WORKERS for this phase + p.update(PHASE_LOCKED) return p @@ -130,4 +83,4 @@ def get_phase_size_buckets(): def get_phase_stage3_overrides(): - return dict(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 index eb1a90c693..27bb187313 100644 --- a/input/cpsat-bench/evolve/phase4_unified/initial_program.py +++ b/input/cpsat-bench/evolve/phase4_unified/initial_program.py @@ -1,137 +1,61 @@ """ -Phase 4: unified refinement. +Phase 4: unified refinement at W=8. -Reference: shared/cpsat_params_reference.md (any section may be tuned here.) - -EVOLVE-BLOCK below is auto-materialized by prepare_phase_unified.py from the -union of phase{1,2,3}_best.json winners (GLOBAL_OVERRIDES), plus the merged -SIZE_BUCKETS and STAGE3_OVERRIDES from those phases. LLM may then tune all -three surfaces jointly: - - GLOBAL_OVERRIDES — every-problem params - SIZE_BUCKETS — per-constraint-count overrides - STAGE3_OVERRIDES — outlier-only overrides (stage3 cascade) - -num_search_workers stays at PHASE4_WORKERS (= phase3 setting) so unified -tuning happens at the same parallel-search scale as phase3. +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 -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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 baseline_params import BASELINE # noqa: E402 +from _lib import params_catalog # noqa: E402 +BASELINE = params_catalog.load_for_bench(_BENCH).defaults -PHASE4_WORKERS = 8 PHASE_LOCKED = { "random_seed": 0, - "num_search_workers": PHASE4_WORKERS, + "num_search_workers": 8, } # EVOLVE-BLOCK-START -# Auto-generated by prepare_phase_unified.py from union of prior phase winners. -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': 15000, - 'clause_cleanup_target': 50000, - 'cp_model_probing_level': 1, - 'cut_level': 1, - 'exploit_best_solution': True, - 'interleave_search': True, - 'linearization_level': 1, - 'max_num_cuts': 3000, - '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': False, - 'probing_num_combinations_limit': 40000, - 'root_lp_iterations': 2500, - 'share_glue_clauses': True, - 'share_glue_clauses_dtime': 0.5, - 'symmetry_level': 1} +GLOBAL_OVERRIDES = {} SIZE_BUCKETS = [ - (50000, {'cp_model_probing_level': 3, - 'cut_level': 0, - 'interleave_search': False, - 'linearization_level': 1, - 'max_num_cuts': 0, - 'merge_at_most_one_work_limit': 1000000, - 'presolve_inclusion_work_limit': 50000000, - 'presolve_use_bva': False, - 'probing_num_combinations_limit': 5000, - 'root_lp_iterations': 500, - 'search_branching': 0, - 'symmetry_level': 2}), - (150000, {'add_clique_cuts': False, - 'add_mir_cuts': True, - 'add_zero_half_cuts': False, - 'cp_model_probing_level': 2, - 'cut_cleanup_target': 1500, - 'cut_level': 1, - 'exploit_relaxation_solution': True, - 'linearization_level': 1, - 'max_num_cuts': 4000, - 'root_lp_iterations': 3000, - '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}), + (50_000, {}), + (150_000, {}), + (float("inf"), {}), ] -STAGE3_OVERRIDES = {'clause_cleanup_lbd_bound': 8, - 'clause_cleanup_ordering': 1, - 'clause_cleanup_period': 22000, - 'clause_cleanup_target': 150000, - 'cp_model_probing_level': 3, - 'cut_active_count_decay': 0.9, - 'cut_level': 1, - 'diversify_lns_params': True, - 'exploit_all_precedences': True, - 'extra_subsolvers': ['core', 'quick_restart', 'reduced_costs', 'lb_tree_search'], - 'inprocessing_dtime_ratio': 0.4, - 'inprocessing_minimization_dtime': 2.0, - 'inprocessing_probing_dtime': 2.0, - 'linearization_level': 1, - 'max_cut_rounds_at_level_zero': 3, - 'max_num_cuts': 4000, - 'merge_at_most_one_work_limit': 1000000000, - 'merge_no_overlap_work_limit': 2000000000000.0, - 'polish_lp_solution': True, - 'presolve_inclusion_work_limit': 1000000000, - 'probing_num_combinations_limit': 80000, - 'repair_hint': True, - 'root_lp_iterations': 8000, - 'solution_pool_size': 5, - 'symmetry_level': 3, - 'use_feasibility_jump': True, - 'use_feasibility_pump': True, - 'use_strong_propagation_in_disjunctive': True} +STAGE3_OVERRIDES = {} # EVOLVE-BLOCK-END -def _bucket_override(num_constraints): +def _bucket_override(size): for upper, override in SIZE_BUCKETS: - if num_constraints < upper: + if size < upper: return override return {} @@ -140,7 +64,7 @@ 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))) + 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) @@ -156,4 +80,4 @@ def get_phase_size_buckets(): def get_phase_stage3_overrides(): - return dict(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 index 2d7358d03f..e9dd451cdc 100644 --- a/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py +++ b/input/cpsat-bench/evolve/phase5_custom_subsolvers/initial_program.py @@ -1,72 +1,59 @@ """ -Phase 5: add CUSTOM SUBSOLVERS to the CP-SAT portfolio. +Phase 5: add CUSTOM SUBSOLVERS to the CP-SAT portfolio at W=8. -Reference: shared/cpsat_params_reference.md - (any SatParameters field may be set INSIDE a custom subsolver's `params`; - invalid names surface as `invalid_param: subsolver_params.` and - zero the score.) +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. -WHY a dedicated phase for this ------------------------------- -CP-SAT solves a model with a PORTFOLIO of subsolvers, each with a slightly -different configuration (e.g. different linearization_level). You may add your -OWN subsolver with a specific configuration via `subsolver_params` (a named -SatParameters set) referenced from `extra_subsolvers`. +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. -CRITICAL — do NOT tune top-level parameters here. Top-level params apply to -EVERY subsolver, including the LNS workers. A very expensive propagation -technique enabled top-level would also fire inside LNS workers and make LNS so -slow it becomes useless, unbalancing the whole portfolio. You can also create a -default subsolver incompatible with the model: e.g. an objective-requiring -config on an objective-less model makes CP-SAT drop most/all subsolvers from -the portfolio, leaving the solver inefficient or non-functional. - -The safe pattern is a SINGLE extra subsolver carrying the expensive technique: - - if the technique does NOT help → only that one worker is slow; the rest of - the portfolio is untouched. - - if it DOES help → that worker shares its solutions and variable bounds with - the others, lifting whole-portfolio performance. - -This is exactly the packing-problem case: a high-cost propagation can speed up -search dramatically when isolated to one worker, but would cripple search if -forced on everyone. - -Inheritance ------------ -Inherits the phase4 unified winner (shared/phase4_best.json + phase4_buckets + -phase4_stage3) as the top-level configuration and does NOT modify it. Phase 5 -ONLY appends custom subsolvers on top. num_search_workers is pinned to -PHASE5_WORKERS (8) so the added subsolvers actually get worker slots; portfolio -sharing requires interleave_search, which is locked True. - -Evolve surface (EVOLVE-BLOCK) ------------------------------ +EVOLVE-BLOCK surface: CUSTOM_SUBSOLVERS — applied to every problem - STAGE3_CUSTOM_SUBSOLVERS — added ONLY for stage3 outliers (long-tail / hard) + STAGE3_CUSTOM_SUBSOLVERS — added ONLY for stage3 outliers -Each entry is a dict: +Spec dict: { - "name": "unique_subsolver_name", # required, unique string - "params": { ...SatParameters fields }, # the isolated configuration - "needs_objective": False, # optional; if True, skipped on - # objective-less models (avoids - # CP-SAT dropping the portfolio) - "min_constraints": 0, # optional inclusive lower gate - "max_constraints": None, # optional exclusive upper gate + "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 } -Only put the expensive/experimental knob(s) in `params`. Keep it to one -technique per subsolver so its effect is attributable. Do NOT modify locked keys (random_seed, num_search_workers, interleave_search). """ import json +import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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 -from baseline_params import BASELINE # noqa: E402 +BASELINE = params_catalog.load_for_bench(_BENCH).defaults +_CACHE = _BENCH / "cache" PHASE5_WORKERS = 8 @@ -74,94 +61,52 @@ PHASE_LOCKED = { "random_seed": 0, "num_search_workers": PHASE5_WORKERS, - # Portfolio determinism + the cross-worker sharing that makes an isolated - # custom subsolver pay off (it broadcasts solutions / variable bounds). "interleave_search": True, } def _load_prev_dict(name): - p = _SHARED / name + p = _CACHE / name if p.exists(): return json.loads(p.read_text()) return {} def _load_prev_buckets(name): - p = _SHARED / 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] -def _load_objective_shas(): - """SHAs whose model carries an objective (build_samples cache). Used to skip - objective-requiring custom subsolvers on feasibility-only models.""" - p = _SHARED / "has_objective_cache.json" - if not p.exists(): - return None # unknown → treat every problem as having an objective - try: - data = json.loads(p.read_text()) - return set(data.get("with_objective") or []) - except (json.JSONDecodeError, OSError): - return None - - -# Inherit the consolidated phase4 unified winner as the top-level config. _PHASE4 = _load_prev_dict("phase4_best.json") _PHASE4_BUCKETS = _load_prev_buckets("phase4_buckets.json") _PHASE4_STAGE3 = _load_prev_dict("phase4_stage3.json") -_OBJECTIVE_SHAS = _load_objective_shas() # EVOLVE-BLOCK-START -# Custom subsolvers added to the portfolio. Start empty; the LLM introduces -# one isolated expensive technique at a time. See module docstring for schema. -# Example (commented — DO NOT enable blindly): -# { -# "name": "max_lp_heavy", -# "params": {"linearization_level": 2, "add_mir_cuts": True, -# "max_num_cuts": 12000, "cut_level": 2}, -# "min_constraints": 50000, -# }, CUSTOM_SUBSOLVERS = [] - -# Added on top of CUSTOM_SUBSOLVERS only for stage3 outliers. STAGE3_CUSTOM_SUBSOLVERS = [] # EVOLVE-BLOCK-END -def _problem_has_objective(problem): - if problem is None: - return True - if _OBJECTIVE_SHAS is None: - return True - sha = problem.get("sha") - if sha is None: - # fall back to the recorded baseline objective when sha is absent - 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 + size = int(problem.get("size") or 0) if problem else 0 lo = spec.get("min_constraints") - if lo is not None and nc < lo: + if lo is not None and size < lo: return False hi = spec.get("max_constraints") - if hi is not None and nc >= hi: + if hi is not None and size >= hi: return False return True -def _bucket_override(num_constraints): +def _bucket_override(size): out = {} for buckets in (_PHASE4_BUCKETS or [],): for upper, override in buckets: - if num_constraints < upper: + if size < upper: out.update(override) break return out @@ -175,22 +120,16 @@ def _collect_specs(problem, stage): def _apply_custom_subsolvers(p, specs): - """Append custom subsolvers to the portfolio WITHOUT touching top-level - params. Each spec → one entry in subsolver_params + one name in - extra_subsolvers (deduped against any inherited names).""" if not specs: return sub_entries = [] new_names = [] for spec in specs: - name = spec["name"] - entry = {"name": name} + entry = {"name": spec["name"]} entry.update(spec.get("params") or {}) sub_entries.append(entry) - new_names.append(name) - + 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: @@ -200,13 +139,17 @@ def _apply_custom_subsolvers(p, specs): def get_params(problem=None, stage=None): p = dict(BASELINE) - p.update(_PHASE4) # inherited top-level config — left untouched below + p.update(_PHASE4) if problem is not None: - p.update(_bucket_override(int(problem.get("num_constraints") or 0))) + 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) # re-enforce phase lock last + 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/evolve/prepare_phase_unified.py b/input/cpsat-bench/evolve/prepare_phase_unified.py deleted file mode 100644 index a6ae80f94f..0000000000 --- a/input/cpsat-bench/evolve/prepare_phase_unified.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Thin wrapper: calls _lib.prepare_phase.main with cpsat-bench phase config. -Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json -plus phase{1,2,3}_buckets.json / phase{1,2,3}_stage3.json when present. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_SHARED = _HERE / "shared" -_INPUT_DIR = _HERE.parents[1] -_UNIFIED_FILE = _HERE / "phase4_unified" / "initial_program.py" - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.prepare_phase import main # noqa: E402 - -PRIOR_PHASES = [1, 2, 3] - -if __name__ == "__main__": - # cpsat-bench unified phase exposes GLOBAL_OVERRIDES (not the legacy - # UNIFIED_OVERRIDES) so prepare_phase writes that name. SIZE_BUCKETS and - # STAGE3_OVERRIDES are appended when prior phases produced bucket/stage3 - # extracts. - main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE, - dict_name="GLOBAL_OVERRIDES") diff --git a/input/cpsat-bench/evolve/rebaseline_local.py b/input/cpsat-bench/evolve/rebaseline_local.py deleted file mode 100644 index 07e8849e48..0000000000 --- a/input/cpsat-bench/evolve/rebaseline_local.py +++ /dev/null @@ -1,424 +0,0 @@ -""" -Init-phase rebaseline: measure BASELINE on the union of -stage{1,2,3,4}_sample.json (on the local host, with current CP-SAT version) -and write shared/local_baseline.json. Captures elapsed_ms, stats, AND -objective_value — the last is critical for cost-mode scoring (variant cost -needs a baseline_obj to ratio against). - -MULTI-WORKER BASELINES (2026-05 revision): - Each phase's initial_program.py pins num_search_workers via PHASE_LOCKED. - Comparing a workers=8 variant against a workers=1 baseline conflates - param-tuning gain with multi-thread parallelism gain. To keep speedup - honest, we capture one baseline PER unique worker count discovered across - the phase files, and store them under `by_workers` in the output. - - Auto-discovery: walks sibling phase*_*/initial_program.py and unions every - PHASE_LOCKED["num_search_workers"]. Override with --workers 1,8 if needed. - -STAGE3 OUTLIER POLICY (2026-05 revision): - Stage3 holds outlier problems with W=8-equivalent baselines from raw-data, - some > 1500s. Measuring those at W=1 would either timeout or take ~10× - longer with no value (evaluator skips stage3 for W=1 phases anyway — see - evaluate_stage3). To save rebaseline wall-clock, this script EXCLUDES stage3 - shas from the W=1 task list and measures stage3 only at W>=2. - -Output schema (shared/local_baseline.json): - { - "": { - "raw_result": "OPTIMAL", - "raw_elapsed_ms": 4321, - "by_workers": { - "1": {"elapsed_ms": ..., "result": ..., "stats": {...}, - "objective": ..., "matches_raw": true}, - "8": {"elapsed_ms": ..., "result": ..., "stats": {...}, - "objective": ..., "matches_raw": true} - } - } - } - -Wall-clock varies by hardware / ortools version. raw-data timings were -recorded elsewhere; evaluator overlays this local file so per-problem -timeout = baseline_ms * 1.3 and speedup = local_baseline_ms / variant_ms -are calibrated for this box AND for the variant's worker count. - -Per-problem timeout = REBASELINE_TIMEOUT_S (1 hr safety floor). Never cut a -baseline run short — a truncated baseline poisons every variant comparison. - -Concurrency = floor(len(core_pool) / W) per worker count. W=1 fills the -pool fully; W=8 typically runs sequentially on small hosts. -""" -import argparse -import importlib.util -import json -import os -import pathlib -import queue as _queue -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed - -_HERE = pathlib.Path(__file__).resolve().parent -sys.path.insert(0, str(_HERE / "shared")) - -from baseline_params import BASELINE # noqa: E402 -from cpsat_runner import run_cpsat # noqa: E402 -from runtime import parallel_solvers, core_range, alloc_core_blocks # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_OUT = _HERE / "shared" / "local_baseline.json" - - -def _profile(): - return (os.environ.get("OPENEVOLVE_PROFILE") or "small").strip().lower() - - -def _stage_sample(stage_num): - profile = _profile() - if profile != "small": - suffixed = _HERE / "shared" / f"stage{stage_num}_{profile}_sample.json" - if suffixed.exists(): - return suffixed - return _HERE / "shared" / f"stage{stage_num}_sample.json" - - -_STAGE1_SAMPLE = _stage_sample(1) -_STAGE2_SAMPLE = _stage_sample(2) -_STAGE3_SAMPLE = _stage_sample(3) -_STAGE4_SAMPLE = _stage_sample(4) - -REBASELINE_TIMEOUT_S = 3600 - -# Match the evaluator: small profile averages N repeats per solve so the -# baseline is measured the SAME way variants are (fair dtime ratio). large -# profile measures once. OPENEVOLVE_SOLVE_REPEATS overrides. -N_REPEATS_SMALL = 10 - - -def _solve_repeats(): - env = os.environ.get("OPENEVOLVE_SOLVE_REPEATS") - if env: - try: - return max(1, int(env)) - except ValueError: - pass - return N_REPEATS_SMALL if _profile() == "small" else 1 - - -def _average_runs(runs): - """Mean elapsed_ms / stats / objective over N run_cpsat dicts. Mirrors - evaluator._average_runs so baseline + variant are averaged identically.""" - import collections - import statistics - - 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))] - 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 - - -def _load_problem_index(): - idx = {} - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - sha = d["problem_sha256"] - idx[sha] = { - "sha": sha, - "problem_filename": d["problem_filename"], - "raw_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), - "raw_result": (d.get("cpsat_status") or {}).get("result"), - } - return idx - - -def _load_target_shas(include_stage3=True): - """Union of stage sample SHAs (dedup, ordered by first appearance). - include_stage3=False → drop stage3 sample (used for W=1 task list). - - Large profile short-circuits to a single sample (stage1_large only) — - cascade staging is disabled in evaluator for that profile.""" - if _profile() == "large": - large = _HERE / "shared" / "stage1_large_sample.json" - if not large.exists(): - print(f"ERROR: {large} missing — run build_samples.py first", - file=sys.stderr) - sys.exit(2) - return list(dict.fromkeys(json.loads(large.read_text())["sha256"])) - - if not _STAGE1_SAMPLE.exists(): - print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", - file=sys.stderr) - sys.exit(2) - samples = [ - (_STAGE1_SAMPLE, "stage1"), - (_STAGE2_SAMPLE, "stage2"), - (_STAGE4_SAMPLE, "stage4"), - ] - if include_stage3: - samples.insert(2, (_STAGE3_SAMPLE, "stage3")) - ids = [] - seen = set() - for sample_path, label in samples: - if not sample_path.exists(): - print(f"WARN: {sample_path.name} missing — skipping {label}", file=sys.stderr) - continue - for sha in json.loads(sample_path.read_text())["sha256"]: - if sha not in seen: - ids.append(sha) - seen.add(sha) - return ids - - -def _discover_phase_workers(): - """Union of PHASE_LOCKED['num_search_workers'] across sibling phase dirs. - - Returns sorted list of unique worker counts (defaults to [1] if nothing - discovered, so the script still produces a usable baseline).""" - workers = set() - for prog in sorted(_HERE.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.relative_to(_HERE)}: {e}", - file=sys.stderr) - continue - pl = getattr(mod, "PHASE_LOCKED", None) - if isinstance(pl, dict) and "num_search_workers" in pl: - try: - workers.add(int(pl["num_search_workers"])) - 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 - try: - w = int(tok) - except ValueError: - raise SystemExit(f"--workers: bad integer {tok!r}") - 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 _measure_at_workers(tasks, w, cores): - """Run baseline for one worker count W across all tasks. Returns - list of (i, meta, res, core_block) tuples in submission order.""" - if not tasks: - print(f" workers={w}: no tasks — skipping", flush=True) - return [] - blocks = alloc_core_blocks(cores, w) - if not blocks: - blocks = [list(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["num_search_workers"] = w - - def _fmt(b): - if isinstance(b, (list, tuple)): - return ",".join(str(x) for x in b) - return str(b) if b is not None else "-" - - repeats = _solve_repeats() - print(f" workers={w}: parallel={n_parallel} repeats={repeats} " - f"blocks={[_fmt(b) for b in blocks]}", flush=True) - - def _solve(task): - i, meta, path = task - block = pool.get() - try: - runs = [] - for _ in range(repeats): - rr = run_cpsat(path, params, REBASELINE_TIMEOUT_S, cpu_core=block) - runs.append(rr) - if "invalid_param" in rr: - break - res = _average_runs(runs) - finally: - pool.put(block) - return i, meta, res, block - - out = [] - if n_parallel == 1: - for task in tasks: - out.append(_solve(task)) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_solve, 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 main(): - ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - ap.add_argument( - "--workers", - type=str, - default=None, - help="comma-separated worker counts (e.g. '1,8'). Default: union of " - "PHASE_LOCKED['num_search_workers'] across sibling phase dirs.", - ) - args = ap.parse_args() - - if args.workers: - worker_counts = _parse_workers_arg(args.workers) - print(f"[rebaseline] worker counts (from --workers): {worker_counts}") - else: - worker_counts = _discover_phase_workers() - print(f"[rebaseline] worker counts (auto-discovered): {worker_counts}") - - # Build BOTH task lists: full (W>=2) and stage3-excluded (W=1). - idx = _load_problem_index() - - def _build_tasks(shas): - out = [] - 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) - sys.exit(2) - path = _RAW_DIR / meta["problem_filename"] - if not path.exists(): - print(f"ERROR: input not found: {path}", file=sys.stderr) - sys.exit(2) - out.append((i, meta, path)) - return out - - shas_full = _load_target_shas(include_stage3=True) - shas_no_stage3 = _load_target_shas(include_stage3=False) - tasks_full = _build_tasks(shas_full) - tasks_no_stage3 = _build_tasks(shas_no_stage3) - n_stage3_only = len(shas_full) - len(shas_no_stage3) - - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=1) + 1)) - - print(f"rebaselining stage{{1,2,3,4}}_sample.json: " - f"{len(tasks_full)} problems total, {n_stage3_only} stage3-only") - print(f" W=1 skips stage3 ({len(tasks_no_stage3)} problems)") - print(f" W>=2 measures all ({len(tasks_full)} problems)") - print(f" worker counts: {worker_counts}") - print(f" per-problem timeout = {REBASELINE_TIMEOUT_S}s (never cut short), " - f"core pool = {cores}") - print() - - # results[sha]["by_workers"][str(w)] = {...} - 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": {}, - } - - t_start = time.monotonic() - mismatch_total = 0 - for w in worker_counts: - tasks = tasks_no_stage3 if w == 1 else tasks_full - print(f"[W={w}] {len(tasks)} problems " - f"({'stage3 skipped' if w == 1 else 'all stages'})", flush=True) - completed = _measure_at_workers(tasks, w, cores) - 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: - mismatch_total += 1 - - if invalid: - flag = f" INVALID_PARAM={invalid}" - elif ok: - flag = "" - else: - flag = " MISMATCH" - 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)) - print( - f" [W={w} {i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " - f"raw={meta['raw_result']:<10}/{int(meta['raw_ms']):>7}ms " - f"local={got_result:<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 - - elapsed = time.monotonic() - t_start - _OUT.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.relative_to(_BENCH_DIR.parent)} " - f"({len(results)} entries, {n_runs} (sha,W) runs, " - f"{mismatch_total} mismatches)") - print(f"total time: {elapsed:.1f}s") - if mismatch_total: - print(f"WARNING: {mismatch_total} (problem, W) pairs had result mismatch — " - f"evaluator will fall back to raw_ms for those.") - return 0 if mismatch_total == 0 else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/input/cpsat-bench/evolve/shared/baseline_params.py b/input/cpsat-bench/evolve/shared/baseline_params.py deleted file mode 100644 index f6608e6769..0000000000 --- a/input/cpsat-bench/evolve/shared/baseline_params.py +++ /dev/null @@ -1,149 +0,0 @@ -""" -Baseline CP-SAT parameters captured from raw-data/____seed0.meta.jsonl -(uniform `cpsat_applied_params` across all problems in package 71 instances). - -DO NOT MODIFY. Imported by all phases. - -Pkg 71 applied_params has 5 keys: timeout_sec, num_search_workers, random_seed, -interleave_search, tuned. `timeout_sec` and `tuned` are wrapper-level -(scheduler) — NOT real CpSolverParameters proto fields. Including them -would make every variant fail with invalid_param. Dropped from BASELINE -and from LOCKED; the worker also has both in its _DROP set as belt+braces. - -Worker-count policy (2026-05 revision): - - BASELINE.num_search_workers = 1 (minimum) so phases 1+2 isolate other - knobs from parallel-search noise. - - Each phase declares its own PHASE_LOCKED in its initial_program.py, - pinning num_search_workers to the phase-specific value. Phase 3 raises - it (typically 8) to let the LLM explore subsolver variations that only - activate with multi-worker search. - - Global LOCKED only enforces random_seed; worker count is enforced per-phase. -""" - -BASELINE = { - "num_search_workers": 1, - "random_seed": 0, - "interleave_search": True, -} - -# Global locks enforced for all phases. Per-phase locks (incl. num_search_workers) -# live in each phase's initial_program.py as PHASE_LOCKED. -LOCKED = { - "random_seed": 0, -} - - -def _self_test(): - """ - Run stage1 problems with BASELINE in parallel; report per-problem ratio vs - recorded baseline_ms. - OK ratio in [0.5, 2.0] - WARN ratio out of band - FAIL result mismatch / 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 cpsat_runner import run_cpsat # noqa: E402 - from runtime import parallel_solvers, core_range # noqa: E402 - - bench = here.parent.parent - 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 - - ids = 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"]] = { - "problem_filename": d["problem_filename"], - "baseline_ms": (d.get("cpsat_status") or {}).get("elapsed_ms", 0), - "baseline_result": (d.get("cpsat_status") or {}).get("result"), - } - - tasks = [] - for i, pid in enumerate(ids): - meta = idx.get(pid) - if meta is None: - print(f"ERROR: {pid[:12]} not in problems.jsonl", file=sys.stderr) - return 2 - problem_path = raw_dir / meta["problem_filename"] - if not problem_path.exists(): - print(f"ERROR: input not found: {problem_path}", file=sys.stderr) - return 2 - tasks.append((i, pid, meta, problem_path)) - - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=5) + 1)) - n_parallel = min(len(cores), len(tasks)) - cores = cores[:n_parallel] - - print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel} " - f"cores={cores}") - print() - - def solve(t): - i, pid, meta, problem_path = t - timeout_s = max(30, math.ceil(meta["baseline_ms"] * 2 / 1000)) - core = cores[i % n_parallel] - r = run_cpsat(problem_path, BASELINE, timeout_s, cpu_core=core) - return i, pid, 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 - for i, pid, meta, r in results: - got_result = r.get("result", "Unknown") - got_ms = int(r.get("elapsed_ms", 0)) - 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"{pid[:12]:<14}{str(meta['baseline_result']):<10}{str(got_result):<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 - - -if __name__ == "__main__": - import sys - sys.exit(_self_test()) diff --git a/input/cpsat-bench/evolve/shared/cpsat_params_reference.md b/input/cpsat-bench/evolve/shared/cpsat_params_reference.md deleted file mode 100644 index 66b8373964..0000000000 --- a/input/cpsat-bench/evolve/shared/cpsat_params_reference.md +++ /dev/null @@ -1,353 +0,0 @@ -# CpSolverParameters Reference (ortools 9.14.6206) - -Total fields: **275**. Auto-categorized by name pattern; some fields -appear under a best-fit bucket. Always verify with the upstream proto: - -`ortools/sat/sat_parameters.proto`. Setting any field on `cp_model.CpSolver().parameters` -via `setattr(s.parameters, name, value)` works for all of these. - - -## parallel/workers (19) - -| name | type | default | enum / notes | -|---|---|---|---| -| `diversify_lns_params` | bool | False | | -| `extra_subsolvers` | list | | | -| `filter_subsolvers` | list | | | -| `ignore_subsolvers` | list | | | -| `interleave_search` | bool | False | | -| `lb_relax_num_workers_threshold` | int32 | 16 | | -| `log_subsolver_statistics` | bool | False | | -| `num_full_subsolvers` | int32 | 0 | | -| `num_search_workers` | int32 | 0 | | -| `num_workers` | int32 | 0 | | -| `share_binary_clauses` | bool | True | | -| `share_glue_clauses` | bool | False | | -| `share_glue_clauses_dtime` | double | 1.0 | | -| `share_level_zero_bounds` | bool | True | | -| `share_objective_bounds` | bool | True | | -| `shared_tree_num_workers` | int32 | 0 | | -| `subsolver_params` | list | | | -| `subsolvers` | list | | | -| `use_objective_lb_search` | bool | False | | - -## seed/reproducibility (18) - -| name | type | default | enum / notes | -|---|---|---|---| -| `ignore_names` | bool | True | | -| `instantiate_all_variables` | bool | True | | -| `lns_initial_deterministic_limit` | double | 0.1 | | -| `log_prefix` | string | '' | | -| `log_search_progress` | bool | False | | -| `log_to_response` | bool | False | | -| `log_to_stdout` | bool | True | | -| `max_deterministic_time` | double | inf | | -| `max_num_deterministic_batches` | int32 | 0 | | -| `name` | string | '' | | -| `permute_presolve_constraint_order` | bool | False | | -| `permute_variable_randomly` | bool | False | | -| `presolve_probing_deterministic_time_limit` | double | 30.0 | | -| `probing_deterministic_time_limit` | double | 1.0 | | -| `random_seed` | int32 | 1 | | -| `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 (7) - -| name | type | default | enum / notes | -|---|---|---|---| -| `max_memory_in_mb` | int64 | 10000 | | -| `max_number_of_conflicts` | int64 | 9223372036854775807 | | -| `max_presolve_iterations` | int32 | 3 | | -| `max_time_in_seconds` | double | inf | | -| `stop_after_first_solution` | bool | False | | -| `stop_after_presolve` | bool | False | | -| `stop_after_root_propagation` | bool | False | | - -## search/branching (22) - -| name | type | default | enum / notes | -|---|---|---|---| -| `boolean_encoding_level` | int32 | 1 | | -| `fix_variables_to_their_hinted_value` | bool | False | | -| `hint_conflict_limit` | int32 | 10 | | -| `initial_polarity` | enum | 1 | {POLARITY_TRUE, POLARITY_FALSE, POLARITY_RANDOM} | -| `initial_variables_activity` | double | 0.0 | | -| `polarity_exploit_ls_hints` | bool | False | | -| `polarity_rephase_increment` | int32 | 1000 | | -| `preferred_variable_order` | enum | 0 | {IN_ORDER, IN_REVERSE_ORDER, IN_RANDOM_ORDER} | -| `random_polarity_ratio` | double | 0.0 | | -| `repair_hint` | bool | False | | -| `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 | False | | -| `use_disjunctive_constraint_in_cumulative` | bool | True | | -| `use_dual_scheduling_heuristics` | bool | True | | -| `use_erwa_heuristic` | bool | False | | -| `use_extended_probing` | bool | True | | -| `use_implied_bounds` | bool | True | | -| `use_lns` | bool | True | | -| `use_lns_only` | bool | False | | -| `use_objective_shaving_search` | bool | False | | -| `use_phase_saving` | bool | True | | -| `use_strong_propagation_in_disjunctive` | bool | False | | - -## conflict/restart (18) - -| name | type | default | enum / notes | -|---|---|---|---| -| `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` | int32 | 5000 | | -| `default_restart_algorithms` | string | 'LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART' | | -| `feasibility_jump_restart_factor` | int32 | 1 | | -| `glucose_decay_increment` | double | 0.01 | | -| `glucose_decay_increment_period` | int32 | 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` | int32 | 50 | | -| `restart_running_window_size` | int32 | 50 | | -| `subsumption_during_conflict_analysis` | bool | True | | -| `use_blocking_restart` | bool | False | | -| `variable_activity_decay` | double | 0.8 | | - -## clause db (8) - -| name | type | default | enum / notes | -|---|---|---|---| -| `clause_cleanup_lbd_bound` | int32 | 5 | | -| `clause_cleanup_ordering` | enum | 0 | {CLAUSE_ACTIVITY, CLAUSE_LBD} | -| `clause_cleanup_period` | int32 | 10000 | | -| `clause_cleanup_protection` | enum | 0 | {PROTECTION_NONE, PROTECTION_ALWAYS, PROTECTION_LBD} | -| `clause_cleanup_ratio` | double | 0.5 | | -| `clause_cleanup_target` | int32 | 0 | | -| `pb_cleanup_increment` | int32 | 200 | | -| `pb_cleanup_ratio` | double | 0.5 | | - -## presolve (27) - -| name | type | default | enum / notes | -|---|---|---|---| -| `convert_intervals` | bool | True | | -| `cp_model_presolve` | bool | True | | -| `cp_model_probing_level` | int32 | 2 | | -| `debug_crash_if_presolve_breaks_hint` | bool | False | | -| `debug_max_num_presolve_operations` | int32 | 0 | | -| `disable_constraint_expansion` | bool | False | | -| `encode_complex_linear_constraint_with_integer` | bool | False | | -| `expand_alldiff_constraints` | bool | False | | -| `expand_reservoir_constraints` | bool | True | | -| `expand_reservoir_using_circuit` | bool | False | | -| `find_big_linear_overlap` | bool | True | | -| `infer_all_diffs` | bool | True | | -| `max_pairs_pairwise_reasoning_in_no_overlap_2d` | int32 | 1250 | | -| `max_size_to_create_precedence_literals_in_disjunctive` | int32 | 60 | | -| `merge_at_most_one_work_limit` | double | 100000000.0 | | -| `merge_no_overlap_work_limit` | double | 1000000000000.0 | | -| `mip_presolve_level` | int32 | 2 | | -| `presolve_blocked_clause` | bool | True | | -| `presolve_bva_threshold` | int32 | 1 | | -| `presolve_bve_clause_weight` | int32 | 3 | | -| `presolve_bve_threshold` | int32 | 500 | | -| `presolve_extract_integer_enforcement` | bool | False | | -| `presolve_inclusion_work_limit` | int64 | 100000000 | | -| `presolve_substitution_level` | int32 | 1 | | -| `presolve_use_bva` | bool | True | | -| `remove_fixed_variables_early` | bool | True | | -| `symmetry_level` | int32 | 2 | | - -## LP/cuts (31) - -| name | type | default | enum / notes | -|---|---|---|---| -| `add_cg_cuts` | bool | True | | -| `add_clique_cuts` | bool | True | | -| `add_lin_max_cuts` | bool | True | | -| `add_lp_constraints_lazily` | bool | True | | -| `add_mir_cuts` | bool | True | | -| `add_objective_cut` | bool | False | | -| `add_rlt_cuts` | bool | True | | -| `add_zero_half_cuts` | bool | True | | -| `cut_active_count_decay` | double | 0.8 | | -| `cut_cleanup_target` | int32 | 1000 | | -| `cut_level` | int32 | 1 | | -| `cut_max_active_count_value` | double | 10000000000.0 | | -| `exploit_all_lp_solution` | bool | True | | -| `exploit_all_precedences` | bool | False | | -| `exploit_integer_lp_solution` | bool | True | | -| `feasibility_jump_linearization_level` | int32 | 2 | | -| `linearization_level` | int32 | 1 | | -| `lp_dual_tolerance` | double | 1e-07 | | -| `lp_primal_tolerance` | double | 1e-07 | | -| `max_all_diff_cut_size` | int32 | 64 | | -| `max_consecutive_inactive_count` | int32 | 100 | | -| `max_cut_rounds_at_level_zero` | int32 | 1 | | -| `max_integer_rounding_scaling` | int32 | 600 | | -| `max_num_cuts` | int32 | 10000 | | -| `new_constraints_batch_size` | int32 | 50 | | -| `only_add_cuts_at_level_zero` | bool | False | | -| `use_energetic_reasoning_in_no_overlap_2d` | bool | False | | -| `use_optimization_hints` | bool | True | | -| `use_overload_checker_in_cumulative` | bool | False | | -| `use_pb_resolution` | bool | False | | -| `use_timetabling_in_no_overlap_2d` | bool | False | | - -## MIP bridge (11) - -| name | type | default | enum / notes | -|---|---|---|---| -| `mip_automatically_scale_variables` | bool | True | | -| `mip_check_precision` | double | 0.0001 | | -| `mip_compute_true_objective_bound` | bool | True | | -| `mip_drop_tolerance` | double | 1e-16 | | -| `mip_max_activity_exponent` | int32 | 53 | | -| `mip_max_bound` | double | 10000000.0 | | -| `mip_max_valid_magnitude` | double | 1e+20 | | -| `mip_scale_large_domain` | bool | False | | -| `mip_treat_high_magnitude_bounds_as_infinity` | bool | False | | -| `mip_var_scaling` | double | 1.0 | | -| `mip_wanted_precision` | double | 1e-06 | | - -## LNS (1) - -| name | type | default | enum / notes | -|---|---|---|---| -| `lns_initial_difficulty` | double | 0.5 | | - -## feasibility jump/pump (10) - -| name | type | default | enum / notes | -|---|---|---|---| -| `feasibility_jump_batch_dtime` | double | 0.1 | | -| `feasibility_jump_decay` | double | 0.95 | | -| `feasibility_jump_enable_restarts` | bool | True | | -| `feasibility_jump_max_expanded_constraint_size` | int32 | 500 | | -| `feasibility_jump_var_perburbation_range_ratio` | double | 0.2 | | -| `feasibility_jump_var_randomization_probability` | double | 0.05 | | -| `use_feasibility_jump` | bool | True | | -| `use_feasibility_pump` | bool | True | | -| `violation_ls_compound_move_probability` | double | 0.5 | | -| `violation_ls_perturbation_period` | int32 | 100 | | - -## optimization (10) - -| name | type | default | enum / notes | -|---|---|---|---| -| `auto_detect_greater_than_at_least_one_of` | bool | True | | -| `binary_search_num_conflicts` | int32 | -1 | | -| `exploit_best_solution` | bool | False | | -| `exploit_relaxation_solution` | bool | False | | -| `minimize_reduction_during_pb_resolution` | bool | False | | -| `optimize_with_core` | bool | False | | -| `optimize_with_lb_tree_search` | bool | False | | -| `optimize_with_max_hs` | bool | False | | -| `use_absl_random` | bool | False | | -| `use_precedences_in_disjunctive_constraint` | bool | True | | - -## debug/output (3) - -| name | type | default | enum / notes | -|---|---|---|---| -| `debug_crash_on_bad_hint` | bool | False | | -| `debug_postsolve_with_full_solver` | bool | False | | -| `fp_rounding` | enum | 2 | {NEAREST_INTEGER, LOCK_BASED, ACTIVE_LOCK_BASED, PROPAGATION_ASSISTED} | - -## other/uncategorized (90) - -| name | type | default | enum / notes | -|---|---|---|---| -| `absolute_gap_limit` | double | 0.0001 | | -| `also_bump_variables_in_conflict_reasons` | bool | False | | -| `at_most_one_max_expansion_size` | int32 | 3 | | -| `catch_sigint_signal` | bool | True | | -| `clause_activity_decay` | double | 0.999 | | -| `core_minimization_level` | int32 | 2 | | -| `count_assumption_levels_in_lbd` | bool | True | | -| `cover_optimization` | bool | True | | -| `cp_model_use_sat_presolve` | bool | True | | -| `detect_linearized_product` | bool | False | | -| `detect_table_with_cost` | bool | False | | -| `encode_cumulative_as_reservoir` | bool | False | | -| `enumerate_all_solutions` | bool | False | | -| `exploit_objective` | bool | True | | -| `fill_additional_solutions_in_response` | bool | False | | -| `fill_tightened_domains_in_response` | bool | False | | -| `filter_sat_postsolve_clauses` | bool | False | | -| `find_multiple_cores` | bool | True | | -| `inprocessing_dtime_ratio` | double | 0.2 | | -| `inprocessing_minimization_dtime` | double | 1.0 | | -| `inprocessing_minimization_use_all_orderings` | bool | False | | -| `inprocessing_minimization_use_conflict_analysis` | bool | True | | -| `inprocessing_probing_dtime` | double | 1.0 | | -| `interleave_batch_size` | int32 | 0 | | -| `keep_all_feasible_solutions_in_presolve` | bool | False | | -| `keep_symmetry_in_presolve` | bool | False | | -| `linear_split_size` | int32 | 100 | | -| `max_alldiff_domain_size` | int32 | 256 | | -| `max_clause_activity_value` | double | 1e+20 | | -| `max_domain_size_when_encoding_eq_neq_constraints` | int32 | 16 | | -| `max_lin_max_size_for_expansion` | int32 | 0 | | -| `max_num_intervals_for_timetable_edge_finding` | int32 | 100 | | -| `max_sat_assumption_order` | enum | 0 | {DEFAULT_ASSUMPTION_ORDER, ORDER_ASSUMPTION_BY_DEPTH, ORDER_ASSUMPTION_BY_WEIGHT} | -| `max_sat_reverse_assumption_order` | bool | False | | -| `max_sat_stratification` | enum | 1 | {STRATIFICATION_NONE, STRATIFICATION_DESCENT, STRATIFICATION_ASCENT} | -| `maximum_regions_to_split_in_disconnected_no_overlap_2d` | int32 | 0 | | -| `min_orthogonality_for_lp_constraints` | double | 0.05 | | -| `minimize_shared_clauses` | bool | True | | -| `new_linear_propagation` | bool | True | | -| `no_overlap_2d_boolean_relations_limit` | int32 | 10 | | -| `num_conflicts_before_strategy_changes` | int32 | 0 | | -| `num_violation_ls` | int32 | 0 | | -| `only_solve_ip` | bool | False | | -| `polish_lp_solution` | bool | False | | -| `probing_num_combinations_limit` | int32 | 20000 | | -| `propagation_loop_detection_factor` | double | 10.0 | | -| `pseudo_cost_reliability_threshold` | int64 | 100 | | -| `push_all_tasks_toward_start` | bool | False | | -| `random_branches_ratio` | double | 0.0 | | -| `randomize_search` | bool | False | | -| `relative_gap_limit` | double | 0.0 | | -| `root_lp_iterations` | int32 | 2000 | | -| `routing_cut_dp_effort` | double | 10000000.0 | | -| `routing_cut_max_infeasible_path_length` | int32 | 6 | | -| `routing_cut_subset_size_for_binary_relation_bound` | int32 | 0 | | -| `routing_cut_subset_size_for_exact_binary_relation_bound` | int32 | 8 | | -| `routing_cut_subset_size_for_shortest_paths_bound` | int32 | 8 | | -| `routing_cut_subset_size_for_tight_binary_relation_bound` | int32 | 0 | | -| `save_lp_basis_in_lb_tree_search` | bool | False | | -| `search_random_variable_pool_size` | int64 | 0 | | -| `shared_tree_balance_tolerance` | int32 | 1 | | -| `shared_tree_max_nodes_per_worker` | int32 | 10000 | | -| `shared_tree_open_leaves_per_worker` | double | 2.0 | | -| `shared_tree_split_strategy` | enum | 0 | {SPLIT_STRATEGY_AUTO, SPLIT_STRATEGY_DISCREPANCY, SPLIT_STRATEGY_OBJECTIVE_LB, SPLIT_STRATEGY_BALANCED_TREE, SPLIT_STRATEGY_FIRST_PROPOSAL} | -| `shared_tree_worker_enable_phase_sharing` | bool | True | | -| `shared_tree_worker_enable_trail_sharing` | bool | True | | -| `shared_tree_worker_min_restarts_per_subtree` | int32 | 1 | | -| `shaving_search_threshold` | int64 | 64 | | -| `solution_pool_size` | int32 | 3 | | -| `strategy_change_increase_ratio` | double | 0.0 | | -| `table_compression_level` | int32 | 2 | | -| `use_all_different_for_circuit` | bool | False | | -| `use_area_energetic_reasoning_in_no_overlap_2d` | bool | False | | -| `use_conservative_scale_overload_checker` | bool | False | | -| `use_dynamic_precedence_in_cumulative` | bool | False | | -| `use_dynamic_precedence_in_disjunctive` | bool | False | | -| `use_exact_lp_reason` | bool | True | | -| `use_hard_precedences_in_cumulative` | bool | False | | -| `use_lb_relax_lns` | bool | True | | -| `use_linear3_for_no_overlap_2d_precedences` | bool | True | | -| `use_ls_only` | bool | False | | -| `use_optional_variables` | bool | False | | -| `use_probing_search` | bool | False | | -| `use_rins_lns` | bool | True | | -| `use_sat_inprocessing` | bool | True | | -| `use_shared_tree_search` | bool | False | | -| `use_symmetry_in_lp` | bool | False | | -| `use_timetable_edge_finding_in_cumulative` | bool | False | | -| `use_try_edge_reasoning_in_no_overlap_2d` | bool | False | | -| `variables_shaving_level` | int32 | -1 | | diff --git a/input/cpsat-bench/evolve/shared/cpsat_runner.py b/input/cpsat-bench/evolve/shared/cpsat_runner.py deleted file mode 100644 index 86de9089c3..0000000000 --- a/input/cpsat-bench/evolve/shared/cpsat_runner.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -Thin wrapper: run_cpsat = subprocess_runner.run_solver bound to -_cpsat_solve_worker.py. - -Status strings produced by the worker: - OPTIMAL, FEASIBLE, INFEASIBLE, UNKNOWN, MODEL_INVALID -For cost mode, success dict also includes "objective" = solver.ObjectiveValue(). -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_INPUT_DIR = _HERE.parents[2] -_WORKER = _HERE / "_cpsat_solve_worker.py" - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.subprocess_runner import run_solver # noqa: E402 - - -def run_cpsat(problem_path, params, timeout_s, python_bin=None, cpu_core=None): - return run_solver(_WORKER, problem_path, params, timeout_s, - python_bin=python_bin, cpu_core=cpu_core) diff --git a/input/cpsat-bench/evolve/shared/evaluator.py b/input/cpsat-bench/evolve/shared/evaluator.py deleted file mode 100644 index 107b7aec98..0000000000 --- a/input/cpsat-bench/evolve/shared/evaluator.py +++ /dev/null @@ -1,725 +0,0 @@ -""" -OpenEvolve evaluator for cpsat-bench parameter tuning. - -Score mode: cost (minimize CP-SAT objective; see score.py). - -Per-problem timeout = max(MIN_TIMEOUT_S, ceil(baseline_ms * TIMEOUT_FACTOR / 1000)). -Locked params (see baseline_params.LOCKED) must not deviate — violation => combined_score=0. - -Per-problem param resolution: - If the program defines `get_params(problem=None, stage=None)`, the evaluator - calls it once per problem with the problem dict (carries num_constraints, - num_variables, is_outlier) and the active stage name ("stage1"…"stage4"). - This lets phases ship SIZE_BUCKETS (constraint-count conditional overrides) - and STAGE3_OVERRIDES (outlier-only tuning) inside their EVOLVE-BLOCK. - Programs that still expose `get_params()` (no args) keep working — the - evaluator falls back to a single global call and applies it uniformly. - -Worker count / PHASE_LOCKED must stay identical across all problems within -a phase. The evaluator reads them once via the no-arg path; per-problem -get_params() calls must NOT change locked keys or num_search_workers. - -Environment overrides: - OPENEVOLVE_MAX_PROBLEMS cap stage problem count - OPENEVOLVE_PARALLEL_SOLVERS concurrent solver subprocesses (default 1) - OPENEVOLVE_PYTHON_BIN python for worker subprocess -""" -import importlib.util -import inspect -import json -import logging -import math -import os -import pathlib -import sys -import traceback - -logger = logging.getLogger(__name__) - -TIMEOUT_FACTOR = 1.3 -MIN_TIMEOUT_S = 5 - -# small profile repeats each solve N times and averages (deterministic_time -# + wall + counters) to damp multi-worker run-to-run variance. large profile -# (single huge outlier) runs once — repeating a ~10 min solve 10× is wasteful. -# Override with OPENEVOLVE_SOLVE_REPEATS. -N_REPEATS_SMALL = 10 - -_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 cpsat_runner import run_cpsat # noqa: E402 -from runtime import ( # noqa: E402 - parallel_solvers, core_range, alloc_core_blocks, -) - -from openevolve.evaluation_result import EvaluationResult # noqa: E402 - -_BENCH_DIR = _HERE.parent.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_OUTLIERS_JSON = _HERE / "outliers.json" -_LOCAL_BASELINE = _HERE / "local_baseline.json" - - -def _profile(): - """Active sample profile (OPENEVOLVE_PROFILE env). 'small' (default) reads - legacy stage{N}_sample.json; 'large' reads stage{N}_large_sample.json, - falling back to legacy if the large file is missing.""" - return (os.environ.get("OPENEVOLVE_PROFILE") or "small").strip().lower() - - -def _stage_sample(stage_num): - profile = _profile() - if profile != "small": - suffixed = _HERE / f"stage{stage_num}_{profile}_sample.json" - if suffixed.exists(): - return suffixed - return _HERE / f"stage{stage_num}_sample.json" - - -_STAGE1_SAMPLE = _stage_sample(1) -_STAGE2_SAMPLE = _stage_sample(2) -_STAGE3_SAMPLE = _stage_sample(3) -_STAGE4_SAMPLE = _stage_sample(4) - - -def _load_outlier_shas(): - """Return set of SHAs flagged by outliers_top.csv (via build_samples.py - writing shared/outliers.json). Empty if file missing.""" - if not _OUTLIERS_JSON.exists(): - return set() - try: - d = json.loads(_OUTLIERS_JSON.read_text()) - except (json.JSONDecodeError, OSError): - return set() - return set(d.get("outliers") or {}) - -_PYTHON_BIN = os.environ.get("OPENEVOLVE_PYTHON_BIN") - -# Raw per-problem results cache (small profile). {(program_path, stage): [recs]} -_SMALL_RESULTS_CACHE: dict = {} - -_KEY_STATS = ("num_branches", "num_conflicts", "num_booleans", - "wall_time", "user_time", "deterministic_time") -_DECISIVE = ("OPTIMAL", "FEASIBLE") - - -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 _solve_repeats(): - """How many times to run each solve before averaging. small profile → 10 - (damps multi-worker variance), large → 1. OPENEVOLVE_SOLVE_REPEATS env - overrides.""" - env = os.environ.get("OPENEVOLVE_SOLVE_REPEATS") - if env: - try: - return max(1, int(env)) - except ValueError: - pass - return N_REPEATS_SMALL if _profile() == "small" else 1 - - -def _average_runs(runs): - """Collapse N run_cpsat dicts into one with mean elapsed_ms / stats / - objective. Repeated runs of the same params are near-identical except for - multi-worker timing jitter, so the mean is the stable estimate. A run that - surfaced invalid_param short-circuits (config error, not a timing sample). - result = modal label; timeout = any run timed out.""" - import collections - import statistics - - 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))] - 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 - - -def _pick_local_baseline(lo_entry, workers): - """Select the right local baseline entry for `workers`. - - New schema: {"by_workers": {"1": {...}, "8": {...}}, ...}. - Legacy schema (flat keys: elapsed_ms/stats/objective/matches_raw) is - treated as workers=1 measurement. - - Returns the inner dict (with matches_raw/elapsed_ms/stats/objective) - or None if no usable entry exists for that worker count. - """ - if not isinstance(lo_entry, dict): - return None - bw = lo_entry.get("by_workers") - if isinstance(bw, dict) and bw: - key = str(int(workers)) - if key in bw: - return bw[key] - # No exact match. Return None so caller falls back to raw_ms; we - # intentionally do NOT cross-substitute (e.g. W=8 → W=1) because the - # whole point of by_workers is to keep speedup ratios honest. - return None - # Legacy flat schema — treat as W=1 only. - if "elapsed_ms" in lo_entry and int(workers) == 1: - return lo_entry - return None - - -def _load_problems(workers=1): - local = {} - if _LOCAL_BASELINE.exists(): - local = json.loads(_LOCAL_BASELINE.read_text()) - outlier_shas = _load_outlier_shas() - rows = [] - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - sha = d["problem_sha256"] - baseline_ms = (d.get("cpsat_status") or {}).get("elapsed_ms", 0) - baseline_result = (d.get("cpsat_status") or {}).get("result") - baseline_stats = d.get("cpsat_response_stats") or {} - baseline_objective = (d.get("cpsat_status") or {}).get("objective_value") - features = d.get("features") or {} - 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["problem_filename"], - "baseline_ms": baseline_ms, - "baseline_result": baseline_result, - "baseline_stats": baseline_stats, - "baseline_objective": baseline_objective, - "num_variables": int(features.get("num_variables") or 0), - "num_constraints": int(features.get("num_constraints") or 0), - "num_bool": int(features.get("num_bool") or 0), - "num_int": int(features.get("num_int") or 0), - "is_outlier": sha in outlier_shas, - }) - return rows - - -def _filter_stage1(problems): - if not _STAGE1_SAMPLE.exists(): - return problems - keep = set(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) - return [p for p in problems if p["sha"] in keep] - - -def _filter_stage2(problems): - 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_stage4(problems): - 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 _supports_kwargs(fn, *kwargs): - """Return True if `fn` accepts any of the named kwargs (problem/stage).""" - 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(name in params_ for name in kwargs) - - -def _resolve_params(program_get_params, problem, stage_name): - """Call program.get_params(problem=..., stage=...) when supported, else - fall back to get_params() (legacy single-dict per phase). Returns dict.""" - kwargs = {} - if _supports_kwargs(program_get_params, "problem"): - kwargs["problem"] = problem - if _supports_kwargs(program_get_params, "stage"): - kwargs["stage"] = stage_name - p = program_get_params(**kwargs) if kwargs else program_get_params() - if not isinstance(p, dict): - raise TypeError( - f"get_params() returned {type(p).__name__}, expected dict") - return p - - -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}, - ) - - # Resolve "global" params (no problem context) once — used for worker - # count, core-block allocation, and PHASE_LOCKED enforcement. Per-problem - # variation only affects non-locked knobs (see _solve below). - try: - params = _resolve_params(program.get_params, None, stage_name) - 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}, - ) - - # Per-phase lock (worker count varies by phase) overrides the global lock. - phase_locked = getattr(program, "PHASE_LOCKED", None) - locked = phase_locked if isinstance(phase_locked, dict) else LOCKED - - # DEBUG: surface the resolved global params delta vs BASELINE so the run - # log shows exactly which knobs the LLM mutated this iteration. - if logger.isEnabledFor(logging.DEBUG): - global_delta = { - k: params[k] for k in params - if k not in locked and params.get(k) != BASELINE.get(k) - } - logger.debug( - f"[{stage_name}] resolved_global_delta_vs_baseline={global_delta} " - f"program={pathlib.Path(program_path).name} " - f"phase_locked={dict(locked) if isinstance(locked, dict) else locked}" - ) - for attr in ("GLOBAL_OVERRIDES", "SIZE_BUCKETS", "STAGE3_OVERRIDES"): - val = getattr(program, attr, None) - if val: - logger.debug(f"[{stage_name}] {attr}={val}") - - 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 locked keys (see PHASE_LOCKED in " - "this phase's initial_program.py)."}, - ) - - if "OPENEVOLVE_MAX_PROBLEMS" in os.environ: - problems = problems[: int(os.environ["OPENEVOLVE_MAX_PROBLEMS"])] - - # Empty stage sample → pass-through. Score is set well above any sane - # cascade_thresholds entry so downstream stages keep running. Use this to - # debug a single stage in isolation by emptying the samples for the others. - if not problems: - pass_through = { - "combined_score": 100.0, - "geomean_speedup": 100.0, - "geomean_wall_speedup": 100.0, - "solved_rate": 1.0, - "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, - "stage": stage_name, - } - return EvaluationResult( - metrics=pass_through, - artifacts={ - "stage": stage_name, - "summary": "empty sample — stage skipped (cascade pass-through, score=100)", - }, - ) - - for p in problems: - input_path = _RAW_DIR / p["input_file"] - if not input_path.exists(): - return _err_result( - {"error": f"input not found: {p['input_file']}"}, - {"missing_file": str(input_path), "stage": stage_name}, - ) - - import queue as _queue - # Core pool: OPENEVOLVE_CORE_RANGE (e.g. "2-7") overrides; else - # cores 1..parallel_solvers() (core 0 reserved for kernel housekeeping). - # - # When params["num_search_workers"] = W > 1, each variant solve needs W - # cores. We floor-chunk the core list into W-sized blocks; concurrency = - # number of blocks. Leftover cores at the tail are dropped to keep every - # solve's CPU budget identical (comparable timings). - _cores = core_range() - if _cores is None: - _cores = list(range(1, parallel_solvers(default=1) + 1)) - workers_per_solve = int(params.get("num_search_workers", 1) or 1) - _blocks = alloc_core_blocks(_cores, workers_per_solve) - if not _blocks: - # Not enough cores for even one block at workers_per_solve. Fall back - # to a single block of all available cores (still respect taskset pin). - _blocks = [list(_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) - - repeats = _solve_repeats() - - def _solve(idx_p): - idx, p = idx_p - input_path = _RAW_DIR / p["input_file"] - timeout_s = max(MIN_TIMEOUT_S, math.ceil(p["baseline_ms"] * TIMEOUT_FACTOR / 1000)) - # Per-problem param resolution. Locked keys + num_search_workers MUST - # match the global `params` resolved earlier — re-pin defensively. - 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 "num_search_workers" in params: - per_params["num_search_workers"] = params["num_search_workers"] - if logger.isEnabledFor(logging.DEBUG): - per_delta = {k: per_params[k] for k in per_params - if per_params.get(k) != params.get(k)} - if per_delta: - logger.debug( - f"[{stage_name}] sha={p['sha'][:10]} " - f"num_constraints={p['num_constraints']} " - f"is_outlier={p['is_outlier']} " - f"per_problem_delta_vs_global={per_delta}" - ) - core = _core_pool.get() - try: - runs = [] - for _ in range(repeats): - rr = run_cpsat(input_path, per_params, timeout_s, - python_bin=_PYTHON_BIN, cpu_core=core) - runs.append(rr) - if "invalid_param" in rr: - break # config error — no point repeating - r = _average_runs(runs) - finally: - _core_pool.put(core) - return idx, p, r, core, timeout_s - - def _invalid_err(r): - return _err_result( - {"error": f"invalid 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 _fmt_core(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={repeats} " - f"core_blocks={[_fmt_core(b) for b in _blocks]}", flush=True) - - # Non-feasible variants are NO LONGER aborted/zeroed — the variant's real - # measured time (≈ timeout when it ran the full budget) flows into the - # score, so a regression is penalized by its true slowdown + solved_rate - # drop, not by a 1e-6 sentinel. Only invalid_param (a config error, not a - # timing sample) still aborts the whole evaluation. - by_idx = {} - abort = None - if n_parallel == 1: - 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={_fmt_core(core)})", flush=True) - if "invalid_param" in r: - return _invalid_err(r) - by_idx[idx] = (p, r) - else: - 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={_fmt_core(core)})", flush=True) - if "invalid_param" in r: - abort = ("invalid", p, r) - print(f" [{stage_name}] invalid_param — cancelling pending " - f"(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) - - 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) - - # Cache raw per-problem results so the small cascade can merge stage1 + - # stage2 into one combined final score (see evaluate_stage3). Keyed by - # program_path + stage; overwritten each cascade so no stale leakage. - _SMALL_RESULTS_CACHE[(str(program_path), stage_name)] = results - - metrics = score(results) - 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", {})}, - } - for r in results - ] - artifacts = { - "stage": stage_name, - "summary": ( - f"solved={metrics['solved']}/{metrics['total']} " - f"regressions={metrics['regressions']} " - f"geomean_dtime={metrics['geomean_speedup']:.3f} " - f"geomean_wall={metrics.get('geomean_wall_speedup', 0.0):.3f} " - f"dtime_used={metrics.get('dtime_used', 0)}/" - f"{metrics.get('dtime_used', 0) + metrics.get('dtime_fallback', 0)} " - 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 _peek_workers(program_path): - """Resolve num_search_workers for baseline lookup BEFORE running _evaluate. - - Prefer PHASE_LOCKED (authoritative); fall back to get_params(). On any - failure, return 1 — _evaluate's own program-load handles the real error - reporting later.""" - try: - program = _load_program(program_path) - except Exception: - return 1 - pl = getattr(program, "PHASE_LOCKED", None) - if isinstance(pl, dict) and "num_search_workers" in pl: - try: - return int(pl["num_search_workers"]) - except (TypeError, ValueError): - pass - try: - params = program.get_params() - return int(params.get("num_search_workers", 1) or 1) - except Exception: - return 1 - - -# Large profile: no cascade stages — every cascade call evaluates the same -# outlier set once and caches. Cache lifetime = current evaluator process -# (openevolve spawns a fresh worker per variant, so cache holds exactly one -# entry per iteration). Keyed by program_path so a re-used path string doesn't -# leak stale metrics across variants. -_LARGE_RESULT_CACHE: dict = {} - - -def _load_large_sample_shas(): - path = _HERE / "stage1_large_sample.json" - if not path.exists(): - return None - try: - return set(json.loads(path.read_text())["sha256"]) - except (json.JSONDecodeError, KeyError, OSError): - return None - - -def _evaluate_large(program_path): - """Single-stage evaluation for OPENEVOLVE_PROFILE=large. Runs all outliers - once, caches result, returns same EvaluationResult on subsequent cascade - calls (stage2/stage3 entry points) so the final variant score reflects the - real outlier score instead of a pass-through placeholder.""" - key = str(program_path) - cached = _LARGE_RESULT_CACHE.get(key) - if cached is not None: - return cached - - keep = _load_large_sample_shas() - if keep is None: - result = _err_result( - {"error": "stage1_large_sample.json missing/invalid"}, - {"suggestion": "Run build_samples.py to regenerate.", - "stage": "large"}, - ) - _LARGE_RESULT_CACHE[key] = result - return result - - w = _peek_workers(program_path) - problems = [p for p in _load_problems(w) if p["sha"] in keep] - result = _evaluate(program_path, problems, "large") - _LARGE_RESULT_CACHE[key] = result - return result - - -def evaluate_stage1(program_path): - if _profile() == "large": - return _evaluate_large(program_path) - w = _peek_workers(program_path) - return _evaluate(program_path, _filter_stage1(_load_problems(w)), "stage1") - - -def evaluate_stage2(program_path): - if _profile() == "large": - return _evaluate_large(program_path) - w = _peek_workers(program_path) - return _evaluate(program_path, _filter_stage2(_load_problems(w)), "stage2") - - -def _finalize_small(program_path): - """Small-profile final cascade slot. The cascade runs stage1 then stage2; - this merges their cached per-problem results into ONE combined score over - stage1 ∪ stage2. No stage3 (outliers live in the large profile) and no - stage4. Falls back to re-running a stage if its cache entry is absent.""" - key = str(program_path) - r1 = _SMALL_RESULTS_CACHE.get((key, "stage1")) - if r1 is None: - res = evaluate_stage1(program_path) - if not isinstance(res, EvaluationResult): - return res - r1 = _SMALL_RESULTS_CACHE.get((key, "stage1"), []) - r2 = _SMALL_RESULTS_CACHE.get((key, "stage2")) - if r2 is None: - res = evaluate_stage2(program_path) - if not isinstance(res, EvaluationResult): - return res - r2 = _SMALL_RESULTS_CACHE.get((key, "stage2"), []) - - combined = list(r1) + list(r2) - if not combined: - return _evaluate(program_path, [], "small_final") # empty pass-through - - metrics = score(combined) - metrics["stage"] = "small_final" - for k in _KEY_STATS: - metrics[f"total_{k}"] = float(sum(r["stats"].get(k, 0) for r in combined)) - artifacts = { - "stage": "small_final", - "summary": ( - f"combined stage1+stage2 ({len(combined)} problems) " - f"solved={metrics['solved']}/{metrics['total']} " - f"regressions={metrics['regressions']} " - f"geomean_dtime={metrics['geomean_speedup']:.3f} " - f"score={metrics['combined_score']:.3f}" - ), - } - return EvaluationResult(metrics=metrics, artifacts=artifacts) - - -def evaluate_stage3(program_path): - if _profile() == "large": - return _evaluate_large(program_path) - # small profile: cascade is stage1 → stage2 only. This final slot merges - # cached stage1 + stage2 results into one combined score. stage3 (outliers) - # and stage4 (broad spread) are no longer part of the small cascade. - return _finalize_small(program_path) - - -def evaluate_stage4(program_path): - # Standalone entry for manual / final-verify use. Not invoked by cascade. - if _profile() == "large": - return _evaluate_large(program_path) - w = _peek_workers(program_path) - problems = _filter_stage4(_load_problems(w)) - return _evaluate(program_path, problems, "stage4") - - -def evaluate(program_path): - # Evolution loop entry: stage1 only. Cascade chains 2/3/4. - return evaluate_stage1(program_path) diff --git a/input/cpsat-bench/evolve/shared/runtime.py b/input/cpsat-bench/evolve/shared/runtime.py deleted file mode 100644 index 0f15616ab7..0000000000 --- a/input/cpsat-bench/evolve/shared/runtime.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -Thin wrapper around input/_lib/runtime.py. -Binds config.yaml path; re-exports parallel_solvers / cascade_threshold / core_range. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_CONFIG_YAML = _HERE.parent / "config.yaml" -_INPUT_DIR = _HERE.parents[2] # input/ - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib import runtime as _rt # noqa: E402 - -core_range = _rt.core_range -alloc_core_blocks = _rt.alloc_core_blocks - - -def parallel_solvers(default=1): - return _rt.parallel_solvers(_CONFIG_YAML, default=default) - - -def cascade_threshold(index, default): - return _rt.cascade_threshold(_CONFIG_YAML, index, default) diff --git a/input/cpsat-bench/evolve/shared/score.py b/input/cpsat-bench/evolve/shared/score.py deleted file mode 100644 index 826aaa4881..0000000000 --- a/input/cpsat-bench/evolve/shared/score.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -Scoring for cpsat-bench. - -Score mode: cost (minimize CP-SAT objective). - - combined = geomean(cost_ratio^COST_W * time_ratio) * solved_rate^2 * efficiency^STATS_WEIGHT - - time_ratio (preferred): baseline_dtime / variant_dtime - CP-SAT's deterministic_time is hardware-independent, - so per-iteration noise across machines / system load - stays out of the score. ~order-of-magnitude same as - wall_time but stable across runs. - time_ratio (fallback) : baseline_ms / variant_ms (wall) - used when either dtime is missing (legacy local - baselines without dtime in stats, or worker not - emitting it). Diagnostic geomean_wall_speedup is - always reported alongside. - - - both baseline+variant decisive (OPTIMAL or FEASIBLE) with objective values: - cost_ratio = (baseline_obj + EPS) / (variant_obj + EPS) [minimize] - - status mismatch (e.g. variant UNKNOWN where baseline OPTIMAL): 1e-6 - - missing baseline_objective (rebaseline not yet run) falls back to time_ratio only - -Note: all 85 baseline runs in this dataset reach OPTIMAL, so when variant -ALSO reaches OPTIMAL, cost_ratio collapses to 1.0 and score reduces to -geomean(time_ratio). Cost mode mainly catches variants that bail to FEASIBLE -with a worse objective. - -Efficiency factor: cross-problem geomean of per-problem weighted geomean over -CP-SAT counters (num_conflicts, num_branches). Each ratio -(baseline+1)/(variant+1) clipped to [0.1, 10]. Lower variant work => efficiency > 1. - -Env overrides: - OPENEVOLVE_STATS_WEIGHT exponent on efficiency, default 0.333, 0 disables - OPENEVOLVE_COST_WEIGHT exponent on cost_ratio, default 1.0, 0 disables cost factor - OPENEVOLVE_TIME_METRIC "dtime" (default) | "wall" — force wall ratio -""" -import math -import os - -_SCORE_MODE = "cost" - -_STATS_WEIGHTS = { - "num_conflicts": 2.0, - "num_branches": 1.5, -} -_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 - -_DECISIVE = ("OPTIMAL", "FEASIBLE") - - -def _efficiency(per_problem): - 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 _time_ratio(p, metric): - """Compute time_ratio. metric ∈ {"dtime", "wall"}. - Returns (ratio, source) where source describes which clock was used. - Falls back to wall when dtime missing on either side.""" - 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" - # fallback / forced wall - return p["baseline_ms"] / max(p["elapsed_ms"], 1), "wall" - - -def _wall_ratio(p): - return p["baseline_ms"] / max(p["elapsed_ms"], 1) - - -def _score_cost(per_problem): - """ - Cost mode scoring rules: - - baseline NOT decisive → uncomparable; skip from geomean entirely. - (Baseline never reached OPTIMAL/FEASIBLE within timeout → variant has - no target to beat. Counting it as 1e-6 unfairly tanks the geomean.) - - baseline decisive + variant decisive → ratio = cost_ratio^W * time_ratio - (time_ratio is deterministic_time-based when both sides have it, else wall) - - baseline decisive + variant NOT decisive → 1e-6 + regression++ (real loss). - - Returns: (geomean, geomean_wall, solved_rate, solved, regressions, - comparable, dtime_used, dtime_fallback) - comparable = problems where baseline was decisive (counted in geomean). - dtime_used = comparable problems where dtime ratio applied. - dtime_fallback= comparable problems forced to wall ratio (dtime missing). - """ - cost_weight = float(os.environ.get("OPENEVOLVE_COST_WEIGHT", "1.0")) - cost_weight = max(0.0, min(cost_weight, 2.0)) - time_metric = os.environ.get("OPENEVOLVE_TIME_METRIC", "dtime").lower() - if time_metric not in ("dtime", "wall"): - time_metric = "dtime" - - ratios = [] - wall_ratios = [] - solved = 0 - regressions = 0 - comparable = 0 - dtime_used = 0 - dtime_fallback = 0 - for p in per_problem: - b_ok = p["baseline_result"] in _DECISIVE - if not b_ok: - continue # uncomparable - comparable += 1 - v_ok = p["result"] in _DECISIVE - b_cost = p.get("baseline_objective") - v_cost = p.get("objective") - time_r, src = _time_ratio(p, time_metric) - wall_r = _wall_ratio(p) - 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: - # Non-decisive variant (UNKNOWN/INFEASIBLE on a decisive baseline). - # Use its REAL measured time_ratio instead of a 1e-6 sentinel — a - # variant that exhausted the timeout contributes its (slow) timeout - # ratio, not a fixed penalty. solved_rate (squared in combined) - # still drops, so a lost solve is penalized via that channel. - 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): - n = len(per_problem) - 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, - } - - (geomean, geomean_wall, solved_rate, solved, regressions, - comparable, dtime_used, dtime_fallback) = _score_cost(per_problem) - - efficiency, eff_pairs = _efficiency(per_problem) - try: - stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0.333")) - except ValueError: - stats_weight = 0.0 - stats_weight = max(0.0, min(stats_weight, 2.0)) - - combined = geomean * (solved_rate ** 2.0) * (efficiency ** stats_weight) - - return { - "combined_score": float(combined), - "geomean_speedup": float(geomean), # primary (dtime when available) - "geomean_wall_speedup": float(geomean_wall), # diagnostic (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/run_phase.sh b/input/run_phase.sh index 577e7b0dd8..8bad7909cf 100755 --- a/input/run_phase.sh +++ b/input/run_phase.sh @@ -1,57 +1,42 @@ #!/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 -# standard flow: -# parse flags → rebaseline → solver check → openevolve-run → extract_best. +# 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 flags] +# [extra openevolve flags] # # omitted → run ALL phases sequentially (1..N). # numeric → run that single phase. # -# --profile small (default) historical run: phase1/2 W=1, phase3/4 W=8, -# uses stage{1..4}_sample.json. Output: openevolve_output/. -# --profile large outlier-only tuning track: phase1..4 all W=8, uses -# stage{1..4}_large_sample.json (stage1=outliers, rest -# empty → cascade pass-through). Output: -# openevolve_output_large/. Sets OPENEVOLVE_PROFILE=large -# so evaluator + phase initial_program.py pick the right -# sample files and worker count. +# 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 # -# Examples: -# ./input/run_phase.sh cpsat-bench # all phases, small profile -# ./input/run_phase.sh cpsat-bench --profile large # all phases, large profile -# ./input/run_phase.sh cpsat-bench 1 --profile large # single phase, large -# ./input/run_phase.sh cpsat-bench --pin 2-7 # all phases, pinned -# ./input/run_phase.sh cpsat-bench 1 --pin 2-7 -# ./input/run_phase.sh z3-bench 4 -# ./input/run_phase.sh cpsat-bench 2 --extract-only +# 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 `. # -# Bench config (in /evolve/config.yaml under `bench:` key — see -# _lib/load_bench_config.py for schema). Loader exports these into env: -# PHASE_DIRS space-separated phase dir names, in order -# (last entry = "unified"/final phase, no extract step) -# PHASE_ITERS space-separated iter counts per phase (omitted => -# use config.yaml max_iterations only) -# UNIFIED_PREPARE_SCRIPT (optional) script name run before the last phase -# SOLVER_CHECK_CMD (optional) shell command returning 0 if solver works -# SOLVER_INSTALL_HINT (optional) message on solver check fail -# REBASELINE_SCRIPT (optional, default rebaseline_local.py) -# EXTRACT_BEST_SCRIPT (optional, default extract_best.py) -# BUILD_SAMPLES_SCRIPT (optional, default build_samples.py) -# -# Env overrides (any bench): -# SKIP_REBASELINE=1 reuse existing local_baseline.json -# OPENEVOLVE_PARALLEL_SOLVERS=N concurrent solver subprocesses -# OPENEVOLVE_CORE_RANGE=N-M taskset core range (also set via --pin) +# 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] [extra flags]" >&2 + 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 } @@ -63,7 +48,6 @@ fi BENCH="$1"; shift -# Optional : numeric next arg. PHASE="" if [ $# -ge 1 ] && [[ "$1" =~ ^[0-9]+$ ]]; then PHASE="$1"; shift @@ -82,93 +66,56 @@ if [ ! -f "$CONFIG_YAML" ]; then exit 2 fi -# Load `bench:` section from config.yaml → exports PHASE_DIRS, PHASE_ITERS, -# UNIFIED_PREPARE_SCRIPT, SOLVER_CHECK_CMD, ... -_BENCH_EXPORTS="$(python "$INPUT_DIR/_lib/load_bench_config.py" "$CONFIG_YAML")" +_BENCH_EXPORTS="$(cd "$INPUT_DIR" && python3 -m _lib.load_bench_config "$CONFIG_YAML")" eval "$_BENCH_EXPORTS" : "${PHASE_DIRS:?bench.phases missing in $CONFIG_YAML}" -REBASELINE_SCRIPT="${REBASELINE_SCRIPT:-rebaseline_local.py}" -EXTRACT_BEST_SCRIPT="${EXTRACT_BEST_SCRIPT:-extract_best.py}" -BUILD_SAMPLES_SCRIPT="${BUILD_SAMPLES_SCRIPT:-build_samples.py}" read -r -a _DIRS <<< "$PHASE_DIRS" read -r -a _ITERS <<< "${PHASE_ITERS:-}" N_PHASES=${#_DIRS[@]} LAST_PHASE=$N_PHASES -PHASE_RANGE_STR="1..$N_PHASES" -# Parse remaining flags 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 - ;; + --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 - ;; + *) echo "--profile must be 'small' or 'large' (got: $PROFILE)" >&2; exit 2 ;; esac - export OPENEVOLVE_PROFILE="$PROFILE" -echo "[run_phase] OPENEVOLVE_PROFILE=$PROFILE" -# Profile-suffixed output dir so small/large runs don't collide. if [ "$PROFILE" = "small" ]; then OUTPUT_DIR="openevolve_output" else OUTPUT_DIR="openevolve_output_${PROFILE}" fi -# Decide phase list if [ -z "$PHASE" ]; then if [ "$EXTRACT_ONLY" = "1" ]; then - echo "--extract-only requires explicit (refuses to bulk-aggregate all)" >&2 + echo "--extract-only requires explicit " >&2 exit 2 fi PHASES_TO_RUN=() - for ((i = 1; i <= N_PHASES; i++)); do - PHASES_TO_RUN+=("$i") - done + 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 {$PHASE_RANGE_STR} (got: $PHASE)" >&2 + echo "phase must be in {1..$N_PHASES} (got: $PHASE)" >&2 exit 2 fi PHASES_TO_RUN=("$PHASE") @@ -181,122 +128,37 @@ if [ -n "$PIN_RANGE" ]; then exit 2 fi export OPENEVOLVE_CORE_RANGE="$PIN_RANGE" - echo "[run_phase] OPENEVOLVE_CORE_RANGE=$PIN_RANGE" fi REPO_ROOT="$(cd "$INPUT_DIR/.." && pwd)" RUNNER="$REPO_ROOT/openevolve-run.py" +EVALUATOR_ENTRY="$INPUT_DIR/_lib/evaluator_entry.py" -# ============ pre-flight (once, even in run-all mode) ============ +export OPENEVOLVE_BENCH_ROOT="$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 $RUNNER" >&2 exit 1 fi - - need_build=0 - if [ ! -f "$ROOT/shared/stage1_sample.json" ] || [ ! -f "$ROOT/shared/stage2_sample.json" ]; then - need_build=1 - fi - if [ "$PROFILE" = "large" ] && [ ! -f "$ROOT/shared/stage1_large_sample.json" ]; then - need_build=1 - fi - if [ "$need_build" = "1" ]; then - # small profile builds stage1+stage2 only (--small); large/default - # builds all four stages. Both emit stage1_large_sample.json. - build_args=() - if [ "$PROFILE" = "small" ]; then - build_args+=(--small) - fi - echo "[run_phase] sample json missing — running $BUILD_SAMPLES_SCRIPT ${build_args[*]}..." - python "$ROOT/$BUILD_SAMPLES_SCRIPT" "${build_args[@]+"${build_args[@]}"}" + if [ ! -f "$EVALUATOR_ENTRY" ]; then + echo "evaluator_entry not found at $EVALUATOR_ENTRY" >&2 + exit 1 fi - if [ -f "$ROOT/$REBASELINE_SCRIPT" ]; then - # Bench-specific: if phase initial_program.py files declare PHASE_LOCKED - # with num_search_workers, the rebaseline script captures one baseline - # per unique W. SKIP_REBASELINE=1 only reuses an existing file when its - # schema covers every required W; legacy/incomplete files force a - # fresh rebaseline. - rebaseline_needed=0 - if [ ! -f "$ROOT/shared/local_baseline.json" ]; then - rebaseline_needed=1 - reason="missing local_baseline.json" - else - # Determine required worker counts from phase modules. Returns - # space-separated ints (e.g. "1 8") or empty if none declared. - REQ_WORKERS="$( - python - "$ROOT" <<'PY' -import importlib.util -import pathlib -import sys -root = pathlib.Path(sys.argv[1]) -workers = set() -for prog in sorted(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: - continue - pl = getattr(mod, "PHASE_LOCKED", None) - if isinstance(pl, dict) and "num_search_workers" in pl: - try: - workers.add(int(pl["num_search_workers"])) - except (TypeError, ValueError): - pass -print(" ".join(str(w) for w in sorted(workers))) -PY - )" - if [ -n "$REQ_WORKERS" ]; then - # Check that local_baseline.json's by_workers covers each W. - MISSING="$( - python - "$ROOT/shared/local_baseline.json" "$REQ_WORKERS" <<'PY' -import json -import sys -path, req = sys.argv[1], sys.argv[2].split() -try: - data = json.load(open(path)) -except Exception: - print("unreadable") - sys.exit(0) -if not isinstance(data, dict) or not data: - print("empty") - sys.exit(0) -missing = [] -for w in req: - covered = False - for v in data.values(): - if isinstance(v, dict) and isinstance(v.get("by_workers"), dict) and w in v["by_workers"]: - covered = True - break - if not covered: - missing.append(w) -print(",".join(missing)) -PY - )" - if [ "$MISSING" = "unreadable" ] || [ "$MISSING" = "empty" ]; then - rebaseline_needed=1 - reason="local_baseline.json $MISSING" - elif [ -n "$MISSING" ]; then - rebaseline_needed=1 - reason="local_baseline.json missing worker counts: $MISSING (required: $REQ_WORKERS)" - fi - fi - fi + if [ ! -f "$ROOT/cache/stage1_sample.json" ]; then + echo "[run_phase] cache missing — running _lib.sampler..." + (cd "$INPUT_DIR" && python3 -m _lib.sampler "$BENCH") + fi - if [ "${SKIP_REBASELINE:-0}" = "1" ] && [ "$rebaseline_needed" = "0" ]; then - echo "[run_phase] SKIP_REBASELINE=1 — reusing existing local_baseline.json" - elif [ "${SKIP_REBASELINE:-0}" = "1" ] && [ "$rebaseline_needed" = "1" ]; then - echo "[run_phase] SKIP_REBASELINE=1 but $reason — forcing rebaseline." - python "$ROOT/$REBASELINE_SCRIPT" || \ - echo "warning: $REBASELINE_SCRIPT finished with mismatches; evaluator falls back to raw_ms for those." - else - echo "[run_phase] running $REBASELINE_SCRIPT (set SKIP_REBASELINE=1 to skip on re-runs)..." - python "$ROOT/$REBASELINE_SCRIPT" || \ - echo "warning: $REBASELINE_SCRIPT finished with mismatches; evaluator falls back to raw_ms for those." - fi + if [ -f "$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 @@ -305,15 +167,10 @@ PY fi fi - # Re-using local_baseline across the phase loop is the right thing to do — - # force-set SKIP_REBASELINE=1 so the inner per-phase logic (if any) doesn't - # rerun. Currently the inner logic does NOT rerun (we did it above), but - # set anyway for any subscript that honors it. export SKIP_REBASELINE=1 fi -# ============ phase runner (called per phase) ============ - +# ============ phase runner ============ run_one_phase() { local phase="$1" shift @@ -325,69 +182,67 @@ run_one_phase() { if [ "$EXTRACT_ONLY" = "1" ]; then if [ "$phase" -eq "$LAST_PHASE" ]; then - echo "--extract-only not supported for phase $LAST_PHASE (no extract step)" >&2 + echo "--extract-only not supported for phase $LAST_PHASE" >&2 return 2 fi - echo "[run_phase] --extract-only phase $phase: aggregating from checkpoints..." - python "$ROOT/$EXTRACT_BEST_SCRIPT" "$phase" --from-checkpoints + (cd "$INPUT_DIR" && python3 -m _lib.extract_best "$BENCH" "$phase" --from-checkpoints) return 0 fi - # Unified-prep: materialize a phase's EVOLVE-BLOCK from prior-phase winners. - # Fires before the configured UNIFIED_PREPARE_BEFORE_DIR (so a later phase - # can still follow the unified phase); falls back to the last phase when the - # key is unset (legacy benches with the unified phase terminal). local prep_here=0 - if [ -n "${UNIFIED_PREPARE_SCRIPT:-}" ]; then - if [ -n "${UNIFIED_PREPARE_BEFORE_DIR:-}" ]; then - [ "$dir" = "$UNIFIED_PREPARE_BEFORE_DIR" ] && prep_here=1 - elif [ "$phase" -eq "$LAST_PHASE" ]; then - prep_here=1 - fi + if [ -n "${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/shared/phase${i}_best.json" ]; then - echo "phase $phase ($dir) requires shared/phase${i}_best.json (run phase $i first)" >&2 + if [ ! -f "$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 $UNIFIED_PREPARE_SCRIPT..." - python "$ROOT/$UNIFIED_PREPARE_SCRIPT" + 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 profile=$PROFILE output=$OUTPUT_DIR ${iter:+iter=$iter }cwd=$(pwd) ===" + echo "[run_phase] === bench=$BENCH phase=$phase dir=$dir ${iter:+iter=$iter }cwd=$(pwd) ===" local iter_flag=() if [ -n "$iter" ]; then iter_flag=(--iterations "$iter") fi - python "$RUNNER" \ + python3 "$RUNNER" \ initial_program.py \ - "$ROOT/shared/evaluator.py" \ + "$EVALUATOR_ENTRY" \ --config "$ROOT/config.yaml" \ --output "$OUTPUT_DIR" \ "${iter_flag[@]+"${iter_flag[@]}"}" \ "$@" - echo "[run_phase] phase $phase finished." - if [ "$phase" -lt "$LAST_PHASE" ]; then - echo "[run_phase] extracting best overrides for phase $phase..." - python "$ROOT/$EXTRACT_BEST_SCRIPT" "$phase" + echo "[run_phase] extracting best for phase $phase..." + (cd "$INPUT_DIR" && python3 -m _lib.extract_best "$BENCH" "$phase") fi } -# ============ drive ============ - +_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 diff --git a/input/z3-bench/evolve/OPENEVOLVE_INTRO.md b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md index 6473a64d53..6308b5c617 100644 --- a/input/z3-bench/evolve/OPENEVOLVE_INTRO.md +++ b/input/z3-bench/evolve/OPENEVOLVE_INTRO.md @@ -168,9 +168,13 @@ python openevolve-run.py \ - `sls.random_seed = 0` - `parallel.enable = False` -**Phase 간 핸드오프**: 자동. P{N} 종료 후 `run_phase.sh`가 `extract_best.py N` 자동 호출 → `shared/phase{N}_best.json` 작성 → P{N+1}이 import. +**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 시 `prepare_phase4.py` 자동 실행 → `phase4_unified/initial_program.py`의 EVOLVE-BLOCK을 union dict literal로 머터리얼라이즈 (LLM이 diff 편집 가능하도록). +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 diff --git a/input/z3-bench/evolve/README.md b/input/z3-bench/evolve/README.md index 00dc3ac051..212587aa19 100644 --- a/input/z3-bench/evolve/README.md +++ b/input/z3-bench/evolve/README.md @@ -1,284 +1,143 @@ -# Z3 Parameter Tuning via OpenEvolve — 실행 & 구조 +# z3-bench — Z3 SMT 솔버 파라미터 튜닝 -OpenEvolve를 사용해 Z3 SMT 솔버의 파라미터를 진화적으로 탐색한다. 입력은 `input/z3-bench/` 데이터셋(50개 SMT2 인스턴스 + baseline 실행 로그). 목표는 baseline 대비 wall-clock 시간 단축, 단 정답(Sat/Unsat) 보존. +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. -> OpenEvolve 소개, 본 프로젝트의 목적/접근, 스코어링 공식, 설계 결정, 로드맵은 [OPENEVOLVE_INTRO.md](OPENEVOLVE_INTRO.md) 참고. +플랫폼 전반 구조는 [`input/README.md`](../../README.md), OpenEvolve 개념은 +[`OPENEVOLVE_INTRO.md`](OPENEVOLVE_INTRO.md) 참고. 본 문서는 z3-bench 고유 +사항만 다룬다. ---- - -## 1. 디렉토리 구조 +## 디렉토리 구조 ``` input/z3-bench/ -├── README.md # 데이터셋 스키마 설명 -├── problems.jsonl # baseline 실행 결과 50행 -├── problems.csv # 평탄화 버전 -├── raw-data/ # 원본 SMT2 + meta jsonl +├── raw-data/ # .smt2 + meta.jsonl +├── problems.jsonl # baseline 실행 기록 (50 rows) └── evolve/ - ├── README.md # 이 파일 (실행 & 구조) - ├── OPENEVOLVE_INTRO.md # 개념/목적/설계 - ├── config.yaml # 공유 OpenEvolve config - ├── run_phase.sh # 1/2/3/4 phase 실행 진입점 - ├── build_samples.py # stage1/stage2 sample 생성 - ├── extract_best.py # phase N 종료 후 best 추출 - ├── prepare_phase4.py # phase4 EVOLVE-BLOCK 머터리얼라이즈 - ├── rebaseline_local.py # 로컬에서 baseline 재측정 - ├── final_verify.py # P4 best 전수 검증 - ├── shared/ - │ ├── baseline_params.py # BASELINE 19키, LOCKED 4키 - │ ├── score.py # geomean × solved_rate^2 - │ ├── z3_runner.py # subprocess z3 CLI 호출 - │ ├── evaluator.py # cascade stage1/stage2 - │ ├── stage1_sample.json # 5문제 stratified sample (seed=42) - │ ├── stage2_sample.json # stage2 problem set - │ └── phase{1,2,3}_best.json # 각 phase 종료 후 생성됨 - ├── phase1_opt_sls/ - │ └── initial_program.py # EVOLVE-BLOCK: OPT_SLS_OVERRIDES (~34키) - ├── phase2_sat/ - │ └── initial_program.py # EVOLVE-BLOCK: SAT_OVERRIDES (~121키) - ├── phase3_smt/ - │ └── initial_program.py # EVOLVE-BLOCK: SMT_OVERRIDES (~97키) - └── phase4_unified/ - └── initial_program.py # EVOLVE-BLOCK: UNIFIED_OVERRIDES (union) + ├── 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 ``` -## 2. 평가 흐름 (cascade) - -``` -LLM 변이된 initial_program.py - ↓ -evaluator.py: - 1. get_params() 호출 → dict - 2. LOCKED 위반 체크 → 위반 시 0점 + locked_violated artifact - 3. stage1 (5문제, per-problem 15s timeout): - for each problem in stage1_sample: - run_z3(smt2, params, timeout=15s) - invalid_param 감지 시 즉시 0점 + 어떤 키인지 artifact - score → cascade_threshold 0.3 통과 시 stage2 진입 - 4. stage2 (50문제, per-problem 120s timeout): - 동일 방식, 전수 - 5. 최종 metrics + per_problem artifacts (상위 20개) 반환 -``` +## 평가 흐름 -### Stage1 sample (stratified by baseline elapsed_ms, seed=42) - -``` -ac90ca97ff99 239 ms Unsat (fast) -133383a624ef 480 ms Unsat (fast) -29efe6d38d7b 12,712 ms Unsat (medium) -86468fd861ff 15,671 ms Sat (medium) -3854194b901b 66,100 ms Sat (slow) -``` +`config.yaml` `bench.evaluation`: -5분위 버킷에서 하나씩 → Sat/Unsat + 빠름/느림 골고루. - -## 3. Initial program 표준 형태 - -각 phase의 `initial_program.py`는 동일 패턴: - -```python -import pathlib, sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) - -from baseline_params import BASELINE +| 키 | 값 | +|---|---| +| `repeats` | 10 (10회 평균) | +| `score_mode` | `speedup` (wall-clock) | +| `enable_size_buckets` | `false` (z3는 단일 surface) | +| `enable_outlier_stage` | `false` | -# (phase 2-3-4는 이전 phase best.json 로드) -import json -_PHASE1 = json.loads((_SHARED / "phase1_best.json").read_text()) \ - if (_SHARED / "phase1_best.json").exists() else {} +Cascade: stage1 (5문제) → stage2 (5문제) → stage3 (5문제, outlier) → stage4 +(20문제, 전체 spread). 각 stage gate는 `cascade_thresholds`. -# EVOLVE-BLOCK-START -PHASE_OVERRIDES = { - "opt.priority": "pareto", - "opt.maxsat_engine": "wmax", - # ... -} -# EVOLVE-BLOCK-END +정답성 regression (baseline decisive + variant mismatch)은 abort + `1e-6` +penalty. invalid_param은 즉시 0점 + 어떤 키인지 artifact. -def get_params(): - p = dict(BASELINE) - p.update(_PHASE1) # 누적 (phase 2+) - p.update(PHASE_OVERRIDES) # 현재 phase - return p +## Clustering (config.yaml `bench.clustering`) -def get_phase_overrides(): - """extract_best.py가 사용 — 현재 phase의 dict만 반환.""" - return dict(PHASE_OVERRIDES) -``` +| 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 | -evaluator는 phase를 모름 — `get_params()` 결과만 받음. `extract_best.py`는 `get_phase_overrides()`만 호출 → phase별 dict 분리 유지. +`python -m _lib.sampler z3-bench`로 `cache/stage{1..4}_sample.json` 생성. -## 4. 실행 절차 (Docker) +## Phase별 surface -### 4.1 백엔드 선택 +cpsat과 달리 z3는 SIZE_BUCKETS / STAGE3_OVERRIDES 미사용. 단일 surface +`OVERRIDES` 만 LLM 변이. -| 항목 | OpenAI 호환 (기본) | Claude Code | +| Phase | EVOLVE-BLOCK | inheritance | |---|---|---| -| 인증 | env var `OPENAI_API_KEY` (Gemini/OpenAI/local 등) | env var `CLAUDE_CODE_OAUTH_TOKEN` (host에서 `claude setup-token`) | -| config.yaml model | `provider` 없음 또는 `openai` | `provider: claude_code` | -| Config 예시 | `configs/default_config.yaml` | `configs/claude_code_example.yaml` | -| Rate limit | 키 발급사 정책 | Pro/Max 5h window — 빠르게 막힘 | -| 재현성 | `temperature`/`seed` 적용 | SDK 미지원, 약함 | -| Docker 자동 셋업 | skip 권장 (`AUTO_INSTALL_CLAUDE=0`) | docker-init-claude.sh가 자동 실행 | - -### 4.2 Quickstart — Claude Code 백엔드 +| 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 | -```bash -# === Host에서 (1회만) === -claude setup-token # 출력 토큰 복사 (Keychain 우회용 long-lived OAuth) -export CLAUDE_CODE_OAUTH_TOKEN="sk-..." # ~/.zshrc 등 영구화 권장 +`get_params()` 적용 순서: `BASELINE → prior_phase_best → current OVERRIDES`. -./docker-run.sh dev -s z3evo # 진입 — 첫 실행: claude CLI + SDK 자동 설치 (수 분) - # 두 번째부터: 즉시 셸 (영속 mount로 skip) -``` +## Quick start ```bash -# === Container 안 (1회만) === -pip install -e ".[dev]" # OpenEvolve 본체 -apt-get install -y z3 # 또는: pip install z3-solver - -python -c "from openevolve.llm.claude_code import ClaudeCodeLLM; print('ok')" # sanity check - -# 데이터 sample (이미 생성됨; 재생성 원할 때만) -python input/z3-bench/evolve/build_samples.py - -# Phase 순차 실행 — 각 phase 종료 시 extract_best.py 자동 호출 -./input/z3-bench/evolve/run_phase.sh 1 -./input/z3-bench/evolve/run_phase.sh 2 -./input/z3-bench/evolve/run_phase.sh 3 -./input/z3-bench/evolve/run_phase.sh 4 +# 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 ``` -### 4.3 Quickstart — OpenAI 호환 백엔드 - -```bash -# === Host에서 === -export OPENAI_API_KEY="..." # config.yaml의 api_base에 맞는 키 -AUTO_INSTALL_CLAUDE=0 ./docker-run.sh dev -s z3evo # claude 셋업 skip - -# === Container 안 === -pip install -e ".[dev]" -apt-get install -y z3 -export OPENAI_API_KEY="..." # 셸 안에서도 필요 (-e로 전달됨) -./input/z3-bench/evolve/run_phase.sh 1 -``` - -`OPENAI_API_KEY`라는 이름은 OpenEvolve가 OpenAI 호환 SDK를 쓰기 때문. 실제 라우팅은 `config.yaml`의 `api_base`가 결정 (Gemini, OpenAI, vLLM 등). - -### 4.4 docker-run.sh의 Claude Code 셋업 동작 - -docker-run.sh가 [scripts/docker-init-claude.sh](../../../scripts/docker-init-claude.sh)를 컨테이너 startup 명령으로 실행. **멱등** (이미 설치돼 있으면 skip). +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`. -| 단계 | 동작 | Skip 조건 | -|---|---|---| -| 1 | `~/.local/bin`을 PATH 추가 + `~/.bashrc` 영구화 | grep로 중복 차단 | -| 2 | `claude` CLI 설치 (`curl -fsSL https://claude.ai/install.sh \| bash`) | `command -v claude` 성공 | -| 3 | `pip install -e ".[claude-code]"` (= claude-agent-sdk) | `import claude_agent_sdk` 성공 | -| 4 | Auth env var 체크 → 없으면 경고 | — | - -**영속화**: `--rm` 컨테이너지만 `~/.axion-docker-persist/claude-local/` → `/root/.local` 마운트로 설치 결과 호스트에 남음. 다음 컨테이너 즉시 사용. - -**자동 셋업 끄기**: `AUTO_INSTALL_CLAUDE=0 ./docker-run.sh ...` - -**Host OS별 차이**: -- Linux host: host의 `claude` 바이너리도 read-only 자동 마운트 (`/usr/local/bin/claude`) → init script가 installer skip. -- Mac host: cross-OS 불가 → init script가 standalone installer 실행 (Node 번들 포함). +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. -**Mount 요약** (root mode 기준): -- `$HOME/.claude/` → `/root/.claude` (settings/sessions/projects 공유) -- `~/.axion-docker-persist/claude-local/` → `/root/.local` (claude 바이너리 영속) -- env forward: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `ANTHROPIC_AUTH_TOKEN`, `ANTHROPIC_BASE_URL`, `CLAUDE_CODE_OAUTH_TOKEN`, `CLAUDE_CODE_USE_BEDROCK`, `CLAUDE_CODE_USE_VERTEX` +## Score 공식 (speedup mode) -### 4.5 체크포인트 재개 - -OpenEvolve가 `phase{N}_*/openevolve_output/checkpoints/checkpoint_K/`에 자동 저장. 재개: - -```bash -cd input/z3-bench/evolve/phase2_sat/ -python /app/openevolve-run.py \ - initial_program.py \ - ../shared/evaluator.py \ - --config ../config.yaml \ - --checkpoint openevolve_output/checkpoints/checkpoint_50 \ - --iterations 100 ``` +combined_score = weighted_geomean(speedup) * solved_rate^2 * efficiency^STATS_WEIGHT -### 4.6 Detached 장시간 실행 - -```bash -./docker-run.sh dev -s z3evo -d -docker exec -it axion-cell-container-dev-$USER-z3evo bash -nohup ./input/z3-bench/evolve/run_phase.sh 1 \ - &> /app/logs/phase1.log & -``` - -Detached mode에서도 init script가 background로 한 번 실행됨. `docker logs $CONTAINER_NAME`로 init 출력 확인. - -### 4.7 트러블슈팅 - -| 증상 | 원인 / 해결 | -|---|---| -| 진입 시 `[init-claude] no CLAUDE_CODE_OAUTH_TOKEN ...` 경고 | host에서 `claude setup-token` → token export → 재실행 | -| `claude --version` 안 됨 | `source ~/.bashrc` 또는 `export PATH="$HOME/.local/bin:$PATH"` | -| init이 매번 재설치 | `~/.axion-docker-persist/claude-local/` 마운트 누락 (rootless면 `$HOME` bind에 포함되어 있어야 함). `ls ~/.local/bin/claude` 확인 | -| `pip install` 실패 | cwd 확인 (`pyproject.toml` 있는지) — rootless면 `cd $SCRIPT_DIR` 먼저 | -| Rate limit | Pro/Max 5h window 소진. `OPENEVOLVE_MAX_PROBLEMS` 축소 또는 OpenAI 호환 백엔드 병행 | -| Mac에서 host claude 마운트 시도 | Mac 바이너리는 Linux 컨테이너에서 안 돎 → docker-run.sh가 자동 skip하고 installer로 fallback | - -### 4.8 CPU 핀닝 / 코어 격리 (측정 안정성) - -z3 wall-clock을 일관되게 재기 위해 컨테이너를 특정 코어에 고정. 호스트 사이드 격리(cgroup v2 isolated partition)와 컨테이너 핀닝을 짝지어 사용한다. - -```bash -# === Host (재부팅 없음) === -sudo ./scripts/host-isolate-cores.sh start # cores 1-6 격리 -sudo ./scripts/host-isolate-cores.sh status # 상태 확인 -# 끝나면 -sudo ./scripts/host-isolate-cores.sh stop # 원복 - -# === Docker 실행 === -./docker-run.sh dev -s z3evo --pin # = --pin 1-6 (default) -./docker-run.sh dev -s z3evo --pin 2-7 # 다른 범위 +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] ``` -`--pin` 동작: -- `--cpuset-cpus=` 추가 (커널 cpuset 강제) -- `/sys/fs/cgroup/isolated.slice` 존재 시 `--cgroup-parent=/isolated.slice`로 격리된 cgroup에 합류 (없으면 경고만) -- 컨테이너 엔트리포인트를 `taskset -c bash -lc ...`로 래핑 +- 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. -`host-isolate-cores.sh start` 메커니즘: -- `/sys/fs/cgroup/isolated.slice` 생성 → `cpuset.cpus=1-6`, `cpuset.cpus.partition=isolated` -- 커널이 1-6을 system.slice/user.slice 등 다른 모든 cgroup에서 제거 + 스케줄러 load-balancing 끔 -- `/proc/irq/*/smp_affinity`를 `0,7-19`로 마스크 → 디바이스 IRQ가 격리 코어에 안 떨어짐 -- 상태/IRQ 백업은 `/var/lib/host-isolate-cores/`에 저장됨 (stop이 원복) +## Locked params -| 변수 | 기본 | 용도 | -|---|---|---| -| `ISOLATED_CPUS` | `1-6` | 격리할 CPU 리스트 (`host-isolate-cores.sh`) | -| `CGROUP_NAME` | `isolated.slice` | 격리 cgroup 이름 | -| `ISOLATED_CGROUP_NAME` | `isolated.slice` | `docker-run.sh`가 합류할 cgroup 이름 | +`sat.random_seed=0`, `smt.random_seed=0`, `sls.random_seed=0`, +`parallel.enable=false`, `threads=1`. 위반 시 `combined_score=0`. -**한계**: 런타임 격리로는 커널 스레드(kthread)와 타이머 틱은 못 막음. 완전 격리가 필요하면 boot param을 추가하고 재부팅: +## 디버깅 / 트러블슈팅 -``` -isolcpus=1-6 nohz_full=1-6 rcu_nocbs=1-6 -``` - -(`host-isolate-cores.sh start` 출력 마지막에 같은 안내가 나옴.) - -## 5. 환경 변수 - -| 변수 | 기본 | 용도 | -|---|---|---| -| `OPENAI_API_KEY` | — | LLM API 키 (api_base에 맞는 것) | -| `OPENEVOLVE_MAX_PROBLEMS` | 50 | stage2 문제수 상한 (테스트용 축소) | -| `OPENEVOLVE_STAGE1_TIMEOUT` | 15 | stage1 문제당 초 | -| `OPENEVOLVE_STAGE2_TIMEOUT` | 120 | stage2 문제당 초 | -| `OPENEVOLVE_Z3_BIN` | `z3` | z3 바이너리 경로 | +| 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. | -## 6. 도커 안에서 추가 검증 필요 +## 참고 -- `z3 -pmd | less` 출력으로 4.13.3.0의 실제 키 검증 (일부 키명/타입이 마이너 버전마다 다를 수 있음) -- baseline 변이로 stage1 1회 평가 직접 호출 → z3 binary 동작/타임아웃 검증 -- LLM API 호출 sanity check (`config.yaml`의 api_base + 키 매칭) +- 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/_probe_parallel.py b/input/z3-bench/evolve/_probe_parallel.py deleted file mode 100644 index 29b7851001..0000000000 --- a/input/z3-bench/evolve/_probe_parallel.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Probe: serial vs parallel z3 measurement noise (run inside docker).""" -import json -import pathlib -import statistics -import sys -import time -from concurrent.futures import ThreadPoolExecutor - -_HERE = pathlib.Path(__file__).resolve().parent -sys.path.insert(0, str(_HERE / "shared")) -from baseline_params import BASELINE # noqa -from z3_runner import run_z3 # noqa - - -def main(): - shas = json.loads((_HERE / "shared/stage1_sample.json").read_text())["sha256"] - prob_idx = {} - with open(_HERE.parent / "problems.jsonl") as f: - for line in f: - d = json.loads(line) - if d["problem_sha256"] in shas: - prob_idx[d["problem_sha256"]] = d["smt2_filename"] - - raw_dir = _HERE.parent / "raw-data" - tasks = [(i, sha, raw_dir / prob_idx[sha]) for i, sha in enumerate(shas)] - - def solve(i, sha, smt2, pin): - r = run_z3(smt2, BASELINE, 60, cpu_core=pin) - return i, sha, r - - # --- A: serial, no pin (3 reps each) --- - print("=== A: serial no-pin (3 reps) ===") - serial = {sha: [] for _, sha, _ in tasks} - for rep in range(3): - for i, sha, smt2 in tasks: - t0 = time.monotonic() - _, _, r = solve(i, sha, smt2, None) - wall = int((time.monotonic() - t0) * 1000) - serial[sha].append((r.get("result"), r["elapsed_ms"], wall)) - print(f" rep{rep} [{i+1}/5] {sha[:10]} {r.get('result')} " - f"z3={r['elapsed_ms']}ms wall={wall}ms") - - # --- B: parallel=5, pin (3 reps batch) --- - print("\n=== B: parallel=5 pinned (3 reps) ===") - par = {sha: [] for _, sha, _ in tasks} - for rep in range(3): - with ThreadPoolExecutor(max_workers=5) as ex: - futs = [ex.submit(solve, i, sha, smt2, (i % 5) + 1) for i, sha, smt2 in tasks] - for fut in futs: - i, sha, r = fut.result() - par[sha].append((r.get("result"), r["elapsed_ms"])) - print(f" rep{rep} [{i+1}/5] {sha[:10]} {r.get('result')} " - f"z3={r['elapsed_ms']}ms core={(i % 5) + 1}") - - # --- summary --- - print("\n=== SUMMARY (z3 elapsed_ms) ===") - print(f"{'sha':<12} {'serial(med/stdev)':<24} {'parallel(med/stdev)':<24} par/ser") - for _, sha, _ in tasks: - s = [t[1] for t in serial[sha]] - p = [t[1] for t in par[sha]] - sm, ss = statistics.median(s), statistics.stdev(s) if len(s) > 1 else 0 - pm, ps = statistics.median(p), statistics.stdev(p) if len(p) > 1 else 0 - ratio = pm / sm if sm else 0 - print(f"{sha[:12]} {sm:>7.0f} / {ss:>6.0f} {pm:>7.0f} / {ps:>6.0f} " - f"{ratio:.2f}x") - # result drift - s_res = set(t[0] for t in serial[sha]) - p_res = set(t[0] for t in par[sha]) - if s_res != p_res: - print(f" RESULT DRIFT: serial={s_res} parallel={p_res}") - - -if __name__ == "__main__": - main() diff --git a/input/z3-bench/evolve/shared/_z3_solve_worker.py b/input/z3-bench/evolve/_solve_worker.py similarity index 100% rename from input/z3-bench/evolve/shared/_z3_solve_worker.py rename to input/z3-bench/evolve/_solve_worker.py diff --git a/input/z3-bench/evolve/adapter.py b/input/z3-bench/evolve/adapter.py new file mode 100644 index 0000000000..da9aa9841e --- /dev/null +++ b/input/z3-bench/evolve/adapter.py @@ -0,0 +1,33 @@ +""" +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"} +STATS_FIELD = None # baseline has no separate stats block +FEATURES_FIELD = "features" +OBJECTIVE_FIELD = None # SMT2/MaxSMT: no objective in baseline + +DECISIVE_RESULTS = ("Sat", "Unsat") +DECIDED_RESULTS = ("Sat", "Unsat") + +KEY_STATS = ("decisions", "propagations", "conflicts", "mk clause") + +STATS_WEIGHTS = { + "conflicts": 2.0, + "decisions": 1.5, + "propagations": 0.5, +} + +SCORE_MODE = "speedup" + +WORKERS_KEY = None # z3 single-threaded in this bench + + +def get_problem_size(features): + """Z3 problems carry num_hard_constraints as the dominant size signal + (mean ~106k hard / ~2k soft / ~33k vars across the workload).""" + return int((features or {}).get("num_hard_constraints") or 0) diff --git a/input/z3-bench/evolve/build_samples.py b/input/z3-bench/evolve/build_samples.py deleted file mode 100644 index 98f6c70e61..0000000000 --- a/input/z3-bench/evolve/build_samples.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -Build problems.jsonl + stage1/2/3/4 sample files from raw-data. - -Source of truth = raw-data/*.meta.jsonl. raw-data accumulates over time; -this script rescans each run, rewrites problems.jsonl as the full aggregate, -and re-selects all stage samples. - -Sample pool cap: baseline_ms <= MAX_BASELINE_MS (5 min). problems.jsonl -still contains the full set; only sample selection applies the cap. - -Outlier filter: after the cap, problems whose baseline_ms lies beyond -[Q1 - k*IQR, Q3 + k*IQR] (Tukey rule, k=OUTLIER_IQR_K=3.0 "far outlier") are -dropped. k=3 instead of the textbook 1.5 because runtime is heavily right- -skewed — k=1.5 would trim away the entire upper quartile that stage3 is -supposed to test. k=3 only removes the genuine long-tail monsters that -distort quintile boundaries and inflate stage3/4 wall-clock. - -Runtime quintiles (SAT-only, sorted by elapsed_ms ascending): - Q1 = bottom 20%, Q2 = 20-40%, ..., Q5 = top 20%. - -Stage1 (5): SAT, quintiles 1+2 (fastest 40%). Strategy: STAGE1_STRATEGY. - Cascade gate: geomean_speedup >= 1.03 → stage2. -Stage2 (5): SAT, quintiles 3+4 (middle 40-80%). Strategy: STAGE2_STRATEGY. - Cascade gate: geomean_speedup >= 1.03 → stage3. -Stage3 (5): SAT, quintile 5 (slowest 20%). Strategy: STAGE3_STRATEGY. - Cascade gate: geomean_speedup >= 1.03 → stage4. -Stage4 (20): SAT+UNSAT, broad. Strategy: STAGE4_STRATEGY. Deduplicated - against stage1+2+3. - -Strategies (STAGE{N}_STRATEGY): - "center" : pick N contiguous elements around the median of the pool. - Tight within-stage runtime variance (~2-3x). - "spread" : quintile-spread across the pool (N/N_BUCKETS per bucket via - rank-linspace). Intentionally wide distribution. - -Quintile-spread = sort by key, split into N_BUCKETS equal-rank buckets, -pick N/N_BUCKETS from each bucket via rank-linspace within bucket. -Deterministic, no randomness. -""" -import json -import pathlib - -_HERE = pathlib.Path(__file__).resolve().parent -_BENCH = _HERE.parent -_RAW = _BENCH / "raw-data" -_PROBLEMS = _BENCH / "problems.jsonl" -_STAGE1 = _HERE / "shared" / "stage1_sample.json" -_STAGE2 = _HERE / "shared" / "stage2_sample.json" -_STAGE3 = _HERE / "shared" / "stage3_sample.json" -_STAGE4 = _HERE / "shared" / "stage4_sample.json" - -STAGE1_N = 5 -STAGE2_N = 5 -STAGE3_N = 5 -STAGE4_N = 20 -N_BUCKETS = 5 -MAX_BASELINE_MS = 300_000 # 5 min cap — exclude monster problems from sample pool -OUTLIER_IQR_K = 3.0 # linear Tukey k (k=1.5=outlier, k=3=far outlier). - # k=3 drops only extreme tails (e.g. 181s, 132s vs ~13s median). - -# Per-stage selection strategy. "center" = contiguous N picks around median -# (tight within-stage runtime variance); "spread" = quintile-spread across -# whole pool (intentionally wide distribution). -STAGE1_STRATEGY = "center" -STAGE2_STRATEGY = "center" -STAGE3_STRATEGY = "center" -STAGE4_STRATEGY = "spread" - - -def _scan_raw(): - rows = [] - for path in sorted(_RAW.glob("*.meta.jsonl")): - with open(path) as f: - for line in f: - line = line.strip() - if not line: - continue - rows.append(json.loads(line)) - break - return rows - - -def _runtime_key(d): - return (d.get("z3_status") or {}).get("elapsed_ms", 0) - - -def _drop_runtime_outliers(rows, k=OUTLIER_IQR_K): - """ - Remove problems whose baseline_ms lies beyond [Q1 - k*IQR, Q3 + k*IQR]. - Tukey-style linear IQR rule. k=1.5 = standard "outlier", k=3.0 = "far - outlier" — we use 3.0 because runtime is heavily right-skewed and 1.5 - would trim too aggressively (the upper quartile already lives in the - long tail). - Returns (kept_rows, dropped_rows). - """ - ms_sorted = sorted(_runtime_key(d) for d in rows if _runtime_key(d) > 0) - n = len(ms_sorted) - if n < 4: - return list(rows), [] - q1 = ms_sorted[n // 4] - q3 = ms_sorted[(3 * n) // 4] - iqr = q3 - q1 - lo, hi = q1 - k * iqr, q3 + k * iqr - kept, dropped = [], [] - for d in rows: - ms = _runtime_key(d) - if ms <= 0 or lo <= ms <= hi: - kept.append(d) - else: - dropped.append(d) - return kept, dropped - - -def _pick(strategy, sorted_rows, n_pick): - if strategy == "center": - return _center_pick(sorted_rows, n_pick) - if strategy == "spread": - return _quintile_spread(sorted_rows, n_pick, N_BUCKETS) - raise ValueError(f"unknown sample strategy: {strategy!r}") - - -def _center_pick(sorted_rows, n_pick): - """ - Pick n_pick contiguous elements centered on the median of sorted_rows. - Used for stage1/2/3 where samples should cluster tightly (similar runtime) - rather than span the whole pool — reduces within-stage variance to ~2-3x - instead of 5-8x. - """ - total = len(sorted_rows) - if total == 0 or n_pick <= 0: - return [] - if total <= n_pick: - return list(sorted_rows) - start = (total - n_pick) // 2 - return sorted_rows[start:start + n_pick] - - -def _quintile_spread(sorted_rows, n_pick, n_buckets=N_BUCKETS): - total = len(sorted_rows) - if total == 0 or n_pick <= 0: - return [] - if total <= n_pick: - return list(sorted_rows) - - per_bucket = n_pick // n_buckets - remainder = n_pick % n_buckets - picked = [] - for b in range(n_buckets): - lo = (b * total) // n_buckets - hi = ((b + 1) * total) // n_buckets - bucket = sorted_rows[lo:hi] - if not bucket: - continue - k = per_bucket + (1 if b < remainder else 0) - if k <= 0: - continue - if k == 1: - picked.append(bucket[len(bucket) // 2]) - else: - for j in range(k): - idx = round(j * (len(bucket) - 1) / (k - 1)) - picked.append(bucket[idx]) - return picked - - -def _summary(d): - f = d.get("features") or {} - z = d.get("z3_status") or {} - return { - "sha": d["problem_sha256"][:12], - "num_hard_constraints": f.get("num_hard_constraints", 0), - "num_variables": f.get("num_variables", 0), - "baseline_result": z.get("result"), - "baseline_ms": z.get("elapsed_ms"), - } - - -def _write_sample(path, picks, label, criteria): - path.write_text( - json.dumps( - { - "selection": f"{len(picks)} {criteria}", - "source": str(_PROBLEMS.relative_to(_BENCH.parent)), - "sha256": [d["problem_sha256"] for d in picks], - "summary": [_summary(d) for d in picks], - }, - indent=2, - ) - + "\n" - ) - print(f"wrote {path.relative_to(_BENCH.parent)} ({len(picks)} {label})") - - -def main(): - rows = _scan_raw() - if not rows: - raise SystemExit(f"no *.meta.jsonl files found under {_RAW}") - print(f"scanned {len(rows)} raw meta files") - - with open(_PROBLEMS, "w") as f: - for d in rows: - f.write(json.dumps(d) + "\n") - print(f"wrote {_PROBLEMS.relative_to(_BENCH.parent)} ({len(rows)} entries)") - - candidates = [ - d for d in rows - if (d.get("z3_status") or {}).get("elapsed_ms", 0) <= MAX_BASELINE_MS - ] - skipped = len(rows) - len(candidates) - print(f"sample pool: {len(candidates)} (skipped {skipped} with " - f"baseline_ms > {MAX_BASELINE_MS}ms)") - - candidates, outliers = _drop_runtime_outliers(candidates) - if outliers: - print(f"dropped {len(outliers)} runtime outliers " - f"(Tukey IQR k={OUTLIER_IQR_K}):") - for d in sorted(outliers, key=_runtime_key): - print(f" {d['problem_sha256'][:12]} " - f"{_runtime_key(d):>7}ms " - f"{(d.get('z3_status') or {}).get('result', '?')}") - - # SAT pool sorted by runtime — basis for stage1/2/3 quintile split. - sat_by_rt = sorted( - (d for d in candidates if (d.get("z3_status") or {}).get("result") == "Sat"), - key=_runtime_key, - ) - n_sat = len(sat_by_rt) - - def q_idx(i): # rank boundary for the i-th quintile cut (i in 0..5) - return (i * n_sat) // 5 - - pool_q12 = sat_by_rt[q_idx(0):q_idx(2)] # quintiles 1+2 (fastest 40%) - pool_q34 = sat_by_rt[q_idx(2):q_idx(4)] # quintiles 3+4 (middle 40%) - pool_q5 = sat_by_rt[q_idx(4):q_idx(5)] # quintile 5 (slowest 20%) - print(f"SAT runtime pool: {n_sat} | Q1+2={len(pool_q12)} | " - f"Q3+4={len(pool_q34)} | Q5={len(pool_q5)}") - - # Strategy per stage configurable via STAGE{N}_STRATEGY constants. - s1 = _pick(STAGE1_STRATEGY, pool_q12, STAGE1_N) - s2 = _pick(STAGE2_STRATEGY, pool_q34, STAGE2_N) - s3 = _pick(STAGE3_STRATEGY, pool_q5, STAGE3_N) - - # Stage4: SAT+UNSAT, exclude SHAs already in stage1+2+3. - used = {d["problem_sha256"] for d in (s1 + s2 + s3)} - broad = sorted( - (d for d in candidates if d["problem_sha256"] not in used), - key=_runtime_key, - ) - s4 = _pick(STAGE4_STRATEGY, broad, STAGE4_N) - - _write_sample(_STAGE1, s1, "stage1", "SAT runtime Q1+2 (fastest 40%)") - _write_sample(_STAGE2, s2, "stage2", "SAT runtime Q3+4 (middle 40%)") - _write_sample(_STAGE3, s3, "stage3", "SAT runtime Q5 (slowest 20%)") - _write_sample(_STAGE4, s4, "stage4", "SAT+UNSAT broad, dedup vs stage1-3") - - for label, picks in (("stage1", s1), ("stage2", s2), - ("stage3", s3), ("stage4", s4)): - print(f"\n{label}:") - for d in picks: - f_ = d.get("features") or {} - z = d.get("z3_status") or {} - print( - f" {d['problem_sha256'][:12]} " - f"hc={f_.get('num_hard_constraints', 0):>7} " - f"vars={f_.get('num_variables', 0):>7} " - f"{z.get('result', '?'):<7} " - f"{z.get('elapsed_ms', 0):>6}ms" - ) - - -if __name__ == "__main__": - main() diff --git a/input/z3-bench/evolve/cache/stage1_sample.json b/input/z3-bench/evolve/cache/stage1_sample.json new file mode 100644 index 0000000000..83a88a2015 --- /dev/null +++ b/input/z3-bench/evolve/cache/stage1_sample.json @@ -0,0 +1,44 @@ +{ + "selection": "5 from 27 candidates", + "criteria": "kmeans-clusters=[0, 1] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", + "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", + "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", + "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", + "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c" + ], + "summary": [ + { + "sha": "fac53f0ffed2", + "baseline_result": "Unsat", + "baseline_ms": 221, + "features.num_hard_constraints": 14932 + }, + { + "sha": "4fb703b12127", + "baseline_result": "Unsat", + "baseline_ms": 311, + "features.num_hard_constraints": 15034 + }, + { + "sha": "872cbdb83f73", + "baseline_result": "Sat", + "baseline_ms": 1298, + "features.num_hard_constraints": 59387 + }, + { + "sha": "069af7891076", + "baseline_result": "Sat", + "baseline_ms": 13885, + "features.num_hard_constraints": 105784 + }, + { + "sha": "03b7e129d625", + "baseline_result": "Sat", + "baseline_ms": 16518, + "features.num_hard_constraints": 106100 + } + ] +} diff --git a/input/z3-bench/evolve/cache/stage2_sample.json b/input/z3-bench/evolve/cache/stage2_sample.json new file mode 100644 index 0000000000..3a44296d91 --- /dev/null +++ b/input/z3-bench/evolve/cache/stage2_sample.json @@ -0,0 +1,44 @@ +{ + "selection": "5 from 17 candidates", + "criteria": "kmeans-clusters=[2, 3] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", + "3cacf544225ef77231cc5a1ef778fd06534731623cc06144babac1140aa5acf1", + "7d51ef2bfc4c3e45350aa61d7585a4c562f3c13412b841fd4de9830a052ca385", + "86efb0762e6d99d9ade8f6718be01a366a53704b6ee199bf0e91af310c68d6f3", + "7f7b5c632ef82955fa7e13961d897ffb114e76ca4e2d183d7178639c3218af1b" + ], + "summary": [ + { + "sha": "aeec9f77b092", + "baseline_result": "Sat", + "baseline_ms": 2193, + "features.num_hard_constraints": 112978 + }, + { + "sha": "3cacf544225e", + "baseline_result": "Unsat", + "baseline_ms": 2996, + "features.num_hard_constraints": 112978 + }, + { + "sha": "7d51ef2bfc4c", + "baseline_result": "Sat", + "baseline_ms": 40558, + "features.num_hard_constraints": 524936 + }, + { + "sha": "86efb0762e6d", + "baseline_result": "Sat", + "baseline_ms": 80512, + "features.num_hard_constraints": 696736 + }, + { + "sha": "7f7b5c632ef8", + "baseline_result": "Sat", + "baseline_ms": 181205, + "features.num_hard_constraints": 696736 + } + ] +} diff --git a/input/z3-bench/evolve/cache/stage3_sample.json b/input/z3-bench/evolve/cache/stage3_sample.json new file mode 100644 index 0000000000..3c27606ff6 --- /dev/null +++ b/input/z3-bench/evolve/cache/stage3_sample.json @@ -0,0 +1,44 @@ +{ + "selection": "5 from 6 candidates", + "criteria": "kmeans-clusters=[4] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "f54dba052fbf975e36b4f6dae5b47802965275f27cc276cd8a6199f763a7b8a4", + "3b58bd47434b81258f5d6b182dde28df64a155b70a2aa790c4d608ec8f11ab65", + "bc60e5e73924b7f663b6320df3ffba8cb70b7ca50069d0bb586a8656aca15677", + "96158dd28c0bfa150bffb4e0a6bcd7689cb8fa3d041af533c002004b5c1aec4d", + "be1c308d797798dce2f3663ad9790822ce632331f6d9c7309a738ab8e438804e" + ], + "summary": [ + { + "sha": "f54dba052fbf", + "baseline_result": "Unsat", + "baseline_ms": 31406, + "features.num_hard_constraints": 1092831 + }, + { + "sha": "3b58bd47434b", + "baseline_result": "Unsat", + "baseline_ms": 31732, + "features.num_hard_constraints": 1092831 + }, + { + "sha": "bc60e5e73924", + "baseline_result": "Unsat", + "baseline_ms": 32076, + "features.num_hard_constraints": 1093104 + }, + { + "sha": "96158dd28c0b", + "baseline_result": "Unsat", + "baseline_ms": 33160, + "features.num_hard_constraints": 1095737 + }, + { + "sha": "be1c308d7977", + "baseline_result": "Unsat", + "baseline_ms": 33752, + "features.num_hard_constraints": 1095737 + } + ] +} diff --git a/input/z3-bench/evolve/cache/stage4_sample.json b/input/z3-bench/evolve/cache/stage4_sample.json new file mode 100644 index 0000000000..0737417ddd --- /dev/null +++ b/input/z3-bench/evolve/cache/stage4_sample.json @@ -0,0 +1,149 @@ +{ + "selection": "20 from 50 candidates", + "criteria": "kmeans-clusters=[0, 1, 2, 3, 4] feature=features.num_hard_constraints spread=quintile", + "source": "z3-bench/problems.jsonl", + "sha256": [ + "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", + "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", + "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", + "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", + "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", + "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", + "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", + "21069961e8dc3386828e2608f2ece27db08b11be6e2da9ebadbc40675687e1fb", + "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84", + "42bc61a62698412bbad582db74dd0c9d92710d69f358348ae993deaf2e8d2c08", + "65444d69388120f98e21ea06a1cf388e621dbc77318828bd673832e90963c568", + "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", + "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", + "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c", + "3b58bd47434b81258f5d6b182dde28df64a155b70a2aa790c4d608ec8f11ab65", + "96158dd28c0bfa150bffb4e0a6bcd7689cb8fa3d041af533c002004b5c1aec4d", + "7d51ef2bfc4c3e45350aa61d7585a4c562f3c13412b841fd4de9830a052ca385", + "5250d459bef23286b67be03e7abec394ceb5d337f40885df8ac4e76eb48d33f8", + "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40", + "7f7b5c632ef82955fa7e13961d897ffb114e76ca4e2d183d7178639c3218af1b" + ], + "summary": [ + { + "sha": "fac53f0ffed2", + "baseline_result": "Unsat", + "baseline_ms": 221, + "features.num_hard_constraints": 14932 + }, + { + "sha": "e57b3cf2bc7e", + "baseline_result": "Unsat", + "baseline_ms": 268, + "features.num_hard_constraints": 24936 + }, + { + "sha": "505a2e36055c", + "baseline_result": "Sat", + "baseline_ms": 307, + "features.num_hard_constraints": 24937 + }, + { + "sha": "17cc4d349ce3", + "baseline_result": "Sat", + "baseline_ms": 366, + "features.num_hard_constraints": 15243 + }, + { + "sha": "133383a624ef", + "baseline_result": "Unsat", + "baseline_ms": 480, + "features.num_hard_constraints": 24797 + }, + { + "sha": "872cbdb83f73", + "baseline_result": "Sat", + "baseline_ms": 1298, + "features.num_hard_constraints": 59387 + }, + { + "sha": "aeec9f77b092", + "baseline_result": "Sat", + "baseline_ms": 2193, + "features.num_hard_constraints": 112978 + }, + { + "sha": "21069961e8dc", + "baseline_result": "Sat", + "baseline_ms": 2900, + "features.num_hard_constraints": 113006 + }, + { + "sha": "d8d5ffa7d824", + "baseline_result": "Sat", + "baseline_ms": 3350, + "features.num_hard_constraints": 112812 + }, + { + "sha": "42bc61a62698", + "baseline_result": "Unsat", + "baseline_ms": 12692, + "features.num_hard_constraints": 105784 + }, + { + "sha": "65444d693881", + "baseline_result": "Unsat", + "baseline_ms": 13089, + "features.num_hard_constraints": 105784 + }, + { + "sha": "069af7891076", + "baseline_result": "Sat", + "baseline_ms": 13885, + "features.num_hard_constraints": 105784 + }, + { + "sha": "4a02d7846394", + "baseline_result": "Sat", + "baseline_ms": 14428, + "features.num_hard_constraints": 105784 + }, + { + "sha": "03b7e129d625", + "baseline_result": "Sat", + "baseline_ms": 16518, + "features.num_hard_constraints": 106100 + }, + { + "sha": "3b58bd47434b", + "baseline_result": "Unsat", + "baseline_ms": 31732, + "features.num_hard_constraints": 1092831 + }, + { + "sha": "96158dd28c0b", + "baseline_result": "Unsat", + "baseline_ms": 33160, + "features.num_hard_constraints": 1095737 + }, + { + "sha": "7d51ef2bfc4c", + "baseline_result": "Sat", + "baseline_ms": 40558, + "features.num_hard_constraints": 524936 + }, + { + "sha": "5250d459bef2", + "baseline_result": "Sat", + "baseline_ms": 67552, + "features.num_hard_constraints": 524936 + }, + { + "sha": "a7df812c620d", + "baseline_result": "Sat", + "baseline_ms": 93445, + "features.num_hard_constraints": 696365 + }, + { + "sha": "7f7b5c632ef8", + "baseline_result": "Sat", + "baseline_ms": 181205, + "features.num_hard_constraints": 696736 + } + ] +} diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index b99bed10ae..bae9f23e2d 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -11,10 +11,43 @@ bench: - dir: phase2_sat - dir: phase3_smt - dir: phase4_unified - unified_prepare_script: prepare_phase4.py + 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 + + # === Clustering / stage sampling. === + clustering: + method: kmeans + feature: features.num_hard_constraints + n_clusters: 5 + max_baseline_ms: 300000 # 5 min cap + 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. === + evaluation: + repeats: 10 + timeout_factor: 1.3 + min_timeout_s: 5 + score_mode: speedup # z3: wall-clock speedup + 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. diff --git a/input/z3-bench/evolve/extract_best.py b/input/z3-bench/evolve/extract_best.py deleted file mode 100644 index f7f611501f..0000000000 --- a/input/z3-bench/evolve/extract_best.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Thin wrapper: calls _lib.extract_best.main with z3-bench phase map. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_SHARED = _HERE / "shared" -_INPUT_DIR = _HERE.parents[1] - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.extract_best import main # noqa: E402 - -PHASE_DIRS = { - 1: "phase1_opt_sls", - 2: "phase2_sat", - 3: "phase3_smt", -} - -if __name__ == "__main__": - main(_HERE, _SHARED, PHASE_DIRS) diff --git a/input/z3-bench/evolve/final_verify.py b/input/z3-bench/evolve/final_verify.py deleted file mode 100644 index b289340ee7..0000000000 --- a/input/z3-bench/evolve/final_verify.py +++ /dev/null @@ -1,216 +0,0 @@ -""" -Final verification: on a final-test sample, measure LOCAL baseline elapsed_ms -and then run the optimized program. Report per-problem speedup using the -fresh local baseline (not the raw-data baseline which was recorded on a -different machine). - -Usage: - python final_verify.py - -Sample selection (in priority order): - 1. shared/final_sample.json — JSON file with {"sha256": [, ...]}. - Hand-edit or generate this to pin a specific subset for verification. - 2. Fall back to ALL problems in problems.jsonl (50). - -Order of operations: - for each problem p in final sample: - run BASELINE on p → record base_ms_local - run params on p → record variant_ms - speedup = base_ms_local / variant_ms (when result matches) - -Baseline + variant are run back-to-back per problem so they share the same -warm cache / system noise. Concurrency = config parallel_solvers (one z3 -process pair per problem; each problem runs baseline then variant serially -inside its slot to keep timing apples-to-apples). -""" -import importlib.util -import json -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 / "shared")) - -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 # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_FINAL_SAMPLE = _HERE / "shared" / "final_sample.json" - -TIMEOUT_S = 120 - - -def _load_get_params(program_path): - spec = importlib.util.spec_from_file_location("program", program_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - if not hasattr(module, "get_params"): - print(f"ERROR: {program_path} missing get_params()", file=sys.stderr) - sys.exit(2) - return module.get_params() - - -def _load_problem_index(): - idx = {} - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - idx[d["problem_sha256"]] = { - "sha": d["problem_sha256"], - "smt2": d["smt2_filename"], - "raw_ms": d["z3_status"]["elapsed_ms"], - "raw_result": d["z3_status"]["result"], - } - return idx - - -def _resolve_sample(idx): - """Pick the SHA list for final verification.""" - if _FINAL_SAMPLE.exists(): - shas = list(json.loads(_FINAL_SAMPLE.read_text())["sha256"]) - source = f"shared/final_sample.json ({len(shas)} SHAs)" - else: - shas = list(idx.keys()) - source = f"problems.jsonl (full {len(shas)})" - metas = [] - for sha in shas: - meta = idx.get(sha) - if meta is None: - print(f"ERROR: {sha[:12]} from sample not in problems.jsonl", file=sys.stderr) - sys.exit(2) - smt2 = _RAW_DIR / meta["smt2"] - if not smt2.exists(): - print(f"ERROR: missing {smt2}", file=sys.stderr) - sys.exit(2) - metas.append((meta, smt2)) - return metas, source - - -def main(): - if len(sys.argv) != 2: - print(__doc__, file=sys.stderr) - return 2 - - program_path = pathlib.Path(sys.argv[1]).resolve() - if not program_path.exists(): - print(f"ERROR: {program_path} not found", file=sys.stderr) - return 2 - - variant_params = _load_get_params(program_path) - violations = {k: variant_params.get(k) for k in LOCKED - if variant_params.get(k) != LOCKED[k]} - if violations: - print(f"ERROR: locked params violated: {violations}", file=sys.stderr) - return 2 - - idx = _load_problem_index() - metas, source = _resolve_sample(idx) - n_parallel = min(parallel_solvers(default=1), len(metas)) - - print(f"final verify: {program_path}") - print(f" sample : {source}") - print(f" params : {len(variant_params)} keys, " - f"{sum(1 for k, v in variant_params.items() if BASELINE.get(k) != v)} differ from BASELINE") - print(f" parallel solvers : {n_parallel} (taskset core pin)") - print(f" per-problem timeout : {TIMEOUT_S}s × 2 (baseline + variant)") - print() - - def _measure(idx_meta): - i, meta, smt2 = idx_meta - # Skip core 0 — pin baseline + variant to cores 1..n_parallel. - core = ((i % n_parallel) + 1) if n_parallel > 1 else None - # Baseline first, then variant — back-to-back so system noise affects - # both equally and speedup ratio cancels it out. - b = run_z3(smt2, BASELINE, TIMEOUT_S, cpu_core=core) - v = run_z3(smt2, variant_params, TIMEOUT_S, cpu_core=core) - return i, meta, b, v - - tasks = [(i, meta, smt2) for i, (meta, smt2) in enumerate(metas)] - t_start = time.monotonic() - completed = [] - if n_parallel == 1: - for t in tasks: - completed.append(_measure(t)) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_measure, t) for t in tasks] - for fut in as_completed(futures): - completed.append(fut.result()) - completed.sort(key=lambda x: x[0]) - elapsed = time.monotonic() - t_start - - results = [] - for i, meta, b, v in completed: - base_ms_local = int(b.get("elapsed_ms", 0)) - base_result = b.get("result", "Unknown") - var_ms = int(v.get("elapsed_ms", 0)) - var_result = v.get("result", "Unknown") - var_invalid = v.get("invalid_param") - # Speedup uses LOCAL baseline (this run), not raw_ms. - if var_invalid: - speedup = 0.0 - flag = f" invalid={var_invalid}" - elif var_result != base_result: - speedup = 0.0 - flag = f" MISMATCH (base={base_result} variant={var_result})" - else: - speedup = base_ms_local / max(var_ms, 1) - flag = "" - print( - f" [{i+1:>2}/{len(metas)}] {meta['sha'][:10]} " - f"base_local={base_result:<7}/{base_ms_local:>7}ms " - f"variant={var_result:<7}/{var_ms:>7}ms " - f"speedup={speedup:.2f}x{flag}", - flush=True, - ) - results.append({ - "sha": meta["sha"], - "smt2": meta["smt2"], - "baseline_ms": base_ms_local, - "baseline_result": base_result, - "result": var_result, - "elapsed_ms": var_ms, - "timeout": bool(v.get("timeout")), - "raw_baseline_ms": meta["raw_ms"], - }) - - metrics = score(results) - print() - print(f"== summary (speedup vs fresh LOCAL baseline) ==") - print(f" solved : {metrics['solved']}/{metrics['total']}") - print(f" regressions : {metrics['regressions']}") - print(f" geomean_speedup : {metrics['geomean_speedup']:.3f}") - print(f" solved_rate : {metrics['solved_rate']:.3f}") - print(f" combined_score : {metrics['combined_score']:.3f}") - print(f" wall-clock : {elapsed:.1f}s") - - out_path = program_path.parent / "final_verify.json" - out_path.write_text(json.dumps({ - "program": str(program_path), - "sample_source": source, - "metrics": metrics, - "per_problem": [ - { - "sha": r["sha"][:12], - "base_result": r["baseline_result"], - "got_result": r["result"], - "base_local_ms": r["baseline_ms"], - "variant_ms": r["elapsed_ms"], - "raw_baseline_ms": r["raw_baseline_ms"], - "timeout": r["timeout"], - } - for r in results - ], - }, indent=2) + "\n") - print(f" wrote {out_path}") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) 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 index bafdea9fde..c1f517d673 100644 --- a/input/z3-bench/evolve/phase1_opt_sls/initial_program.py +++ b/input/z3-bench/evolve/phase1_opt_sls/initial_program.py @@ -1,70 +1,49 @@ """ -Phase 1: tune opt.* + sls.*. +Phase 1: tune Z3 opt.* + sls.* knobs. 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. +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 -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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) + ) -from baseline_params import BASELINE # noqa: E402 +_BENCH = _resolve_bench_root() +_INPUT = _BENCH.parent.parent +if str(_INPUT) not in sys.path: + sys.path.insert(0, str(_INPUT)) -# 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, +from _lib import params_catalog # noqa: E402 + +BASELINE = params_catalog.load_for_bench(_BENCH).defaults - # 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-START +OVERRIDES = {} # EVOLVE-BLOCK-END def get_params(): p = dict(BASELINE) - p.update(OPT_SLS_OVERRIDES) + p.update(OVERRIDES) return p def get_phase_overrides(): - """Used by extract_best.py — returns ONLY this phase's evolved dict.""" - return dict(OPT_SLS_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 index 22861aaeaf..074bc77188 100644 --- a/input/z3-bench/evolve/phase2_sat/initial_program.py +++ b/input/z3-bench/evolve/phase2_sat/initial_program.py @@ -1,177 +1,60 @@ """ 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. +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. +Do NOT modify sat.random_seed (locked). Invalid keys → evaluator returns 0. """ import json +import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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) + ) -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 {} -) +_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 -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, -} +OVERRIDES = {} # EVOLVE-BLOCK-END def get_params(): p = dict(BASELINE) p.update(_PHASE1) - p.update(SAT_OVERRIDES) + p.update(OVERRIDES) return p def get_phase_overrides(): - return dict(SAT_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 index 5635303efb..42126f09fd 100644 --- a/input/z3-bench/evolve/phase3_smt/initial_program.py +++ b/input/z3-bench/evolve/phase3_smt/initial_program.py @@ -1,154 +1,55 @@ """ 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. +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; -we force False here so the LLM's choices stick. +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. +Do NOT modify smt.random_seed (locked). Invalid keys → evaluator returns 0. """ import json +import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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) + ) -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 {} -) +_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 -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, -} +OVERRIDES = {} # EVOLVE-BLOCK-END @@ -156,9 +57,9 @@ def get_params(): p = dict(BASELINE) p.update(_PHASE1) p.update(_PHASE2) - p.update(SMT_OVERRIDES) + p.update(OVERRIDES) return p def get_phase_overrides(): - return dict(SMT_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 index 11575f0e92..873c63080f 100644 --- a/input/z3-bench/evolve/phase4_unified/initial_program.py +++ b/input/z3-bench/evolve/phase4_unified/initial_program.py @@ -1,248 +1,42 @@ """ -Phase 4: unified fine-tuning — interaction effects. +Phase 4: unified Z3 refinement. -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: +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. - 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. +Do NOT modify locked keys. """ +import os import pathlib import sys -_SHARED = pathlib.Path(__file__).resolve().parent.parent / "shared" -sys.path.insert(0, str(_SHARED)) +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 -from baseline_params import BASELINE # noqa: E402 +BASELINE = params_catalog.load_for_bench(_BENCH).defaults # 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': 5, - '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': 'rc2', - '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': False, - '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': 0.1, - 'sat.branching.heuristic': 'lrb', - 'sat.burst_search': 0, - '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': 'dyn_psm', - 'sat.gc.burst': False, - 'sat.gc.defrag': True, - 'sat.gc.increment': 500, - 'sat.gc.initial': 80000, - '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': 'binary_merge', - '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': 'ema', - 'sat.restart.emafastglue': 0.03, - 'sat.restart.emaslowglue': 1e-05, - 'sat.restart.factor': 1.5, - 'sat.restart.fast': True, - 'sat.restart.initial': 100, - 'sat.restart.margin': 1.1, - 'sat.retain_blocked_clauses': True, - 'sat.scc': True, - 'sat.scc.tr': True, - 'sat.search.sat.conflicts': 1500, - 'sat.search.unsat.conflicts': 1500, - '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': 23.0, - 'sls.walksat_ucb_forget': 0.1, - 'sls.walksat_ucb_init': False, - 'sls.walksat_ucb_noise': 0.0002, - 'sls.wp': 23, - '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.rounds': 512, - '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': 2, - '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': 2, - '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': 1000, - 'smt.pb.conflict_frequency': 1000, - 'smt.pb.learn_complements': True, - 'smt.phase_caching_off': 100, - 'smt.phase_caching_on': 400, - 'smt.phase_selection': 3, - 'smt.propagate_values': True, - 'smt.pull_nested_quantifiers': False, - 'smt.qi.cost': '(+ weight generation)', - 'smt.qi.eager_threshold': 15.0, - 'smt.qi.lazy_threshold': 30.0, - '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': 1, - '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} +UNIFIED_OVERRIDES = {} # EVOLVE-BLOCK-END @@ -253,4 +47,4 @@ def get_params(): def get_phase_overrides(): - return dict(UNIFIED_OVERRIDES) + return dict(UNIFIED_OVERRIDES) \ No newline at end of file diff --git a/input/z3-bench/evolve/prepare_phase4.py b/input/z3-bench/evolve/prepare_phase4.py deleted file mode 100644 index 0aa8996b7b..0000000000 --- a/input/z3-bench/evolve/prepare_phase4.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Thin wrapper: calls _lib.prepare_phase.main with z3-bench phase config. -Materializes phase4_unified/initial_program.py from phase{1,2,3}_best.json. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_SHARED = _HERE / "shared" -_INPUT_DIR = _HERE.parents[1] -_UNIFIED_FILE = _HERE / "phase4_unified" / "initial_program.py" - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.prepare_phase import main # noqa: E402 - -PRIOR_PHASES = [1, 2, 3] - -if __name__ == "__main__": - main(_HERE, _SHARED, PRIOR_PHASES, _UNIFIED_FILE) diff --git a/input/z3-bench/evolve/probe_cli_keys.py b/input/z3-bench/evolve/probe_cli_keys.py deleted file mode 100644 index 5a4aa4a8e0..0000000000 --- a/input/z3-bench/evolve/probe_cli_keys.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -Probe which keys from shared/z3_valid_keys.json the Z3 CLI actually accepts -as positional `key=value` arguments. Some keys exist in `-pm:` doc but -are rejected by the CLI (structural / API-only options). - -For each key, run: - z3 -smt2 = -Capture stderr -> classify as CLI_OK / CLI_REJECT. - -Writes shared/z3_cli_skip_keys.json with the list to skip. - -Run inside the container (z3 binary required). This is slow (~N seconds for -N ~1000 keys, ~1s per probe). Use --limit to test a subset first. -""" -import argparse -import json -import pathlib -import re -import shutil -import subprocess -import sys -import tempfile - -ROOT = pathlib.Path(__file__).resolve().parent -SHARED = ROOT / "shared" - -# Trivial SMT2: instant decide. -_TRIVIAL_SMT2 = "(declare-const x Bool)\n(assert x)\n(check-sat)\n" - -_REJECT_RES = [ - re.compile(r"unknown\s+parameter", re.IGNORECASE), - re.compile(r"invalid\s+parameter", re.IGNORECASE), - re.compile(r"unknown\s+option", re.IGNORECASE), - re.compile(r"error\s+setting", re.IGNORECASE), - re.compile(r"is\s+a\s+structural\s+parameter", re.IGNORECASE), -] - - -def is_rejection(stderr): - for rx in _REJECT_RES: - if rx.search(stderr): - return True - return False - - -def probe(key, value, smt2_path, z3_bin): - """Return True if CLI rejects this key.""" - args = [z3_bin, "-smt2", "-T:5", f"{key}={value}", smt2_path] - try: - proc = subprocess.run(args, capture_output=True, text=True, timeout=10) - except subprocess.TimeoutExpired: - return False # timeout != reject; key was accepted but eval slow - return is_rejection(proc.stderr) - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--limit", type=int, default=0, help="probe at most N keys (0 = all)") - ap.add_argument("--z3-bin", default="z3") - args = ap.parse_args() - - if shutil.which(args.z3_bin) is None: - print(f"ERROR: {args.z3_bin} not on PATH", file=sys.stderr) - sys.exit(1) - - valid_keys_path = SHARED / "z3_valid_keys.json" - if not valid_keys_path.exists(): - print(f"ERROR: {valid_keys_path} missing. Run validate_keys.py first.", file=sys.stderr) - sys.exit(1) - keys = sorted(json.loads(valid_keys_path.read_text())) - - # Use a safe sentinel value: integer 0 works for bool/uint/int/double; - # for symbol-typed options z3 ignores numeric mismatches but typically - # doesn't error at the parse level. Falls back to "false" if 0 rejected. - candidate_values = ["0", "false"] - - with tempfile.NamedTemporaryFile("w", suffix=".smt2", delete=False) as f: - f.write(_TRIVIAL_SMT2) - smt2 = f.name - - rejects = [] - total = len(keys) if args.limit == 0 else min(args.limit, len(keys)) - print(f"probing {total} keys with z3={args.z3_bin}") - for i, k in enumerate(keys[:total], 1): - rejected_all = True - for v in candidate_values: - if not probe(k, v, smt2, args.z3_bin): - rejected_all = False - break - if rejected_all: - rejects.append(k) - if i % 50 == 0: - print(f" {i}/{total} rejects so far: {len(rejects)}") - - out = SHARED / "z3_cli_skip_keys.json" - out.write_text(json.dumps(sorted(rejects), indent=2) + "\n") - print(f"\nwrote {out.relative_to(ROOT)} ({len(rejects)} CLI-rejected keys)") - if rejects: - print("examples:") - for r in rejects[:20]: - print(f" - {r}") - - -if __name__ == "__main__": - main() diff --git a/input/z3-bench/evolve/rebaseline_local.py b/input/z3-bench/evolve/rebaseline_local.py deleted file mode 100644 index babea9b3ab..0000000000 --- a/input/z3-bench/evolve/rebaseline_local.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Init-phase rebaseline: measure BASELINE elapsed_ms on the union of -stage1 + stage2 + stage3 + stage4 sample files and write -shared/local_baseline.json. - -Wall-clock varies by hardware / z3 version. Raw-data baseline_ms was recorded -on a different machine, so comparing against it gives misleading speedup. -evaluator._load_problems overlays this local file onto raw data so that -speedup = local_baseline_ms / variant_elapsed_ms and per-problem timeout -= baseline_ms * 1.3 is calibrated for the local box. - -All three stages feed the evolve loop (cascade_evaluation: true). Without -rebaselining stage3 (50 SHAs), 45/50 problems would fall back to raw_ms -recorded elsewhere and timeout/speedup calculations would drift. -final_verify.py re-measures baseline on the fly per problem anyway, but -the evolve-loop stage3 invocation needs trustworthy local baselines up front. - -Per-problem: 1 run, timeout = REBASELINE_TIMEOUT_S (1 hr safety floor — a -truncated baseline measurement is worse than a slow one). MISMATCH-by-timeout -that the old multiplier produced would poison local_baseline. Big problems -under parallel contention may run ~2x raw; let them finish. -Concurrency = config parallel_solvers (env OPENEVOLVE_PARALLEL_SOLVERS override). -""" -import json -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 / "shared")) - -from baseline_params import BASELINE # noqa: E402 -from z3_runner import run_z3 # noqa: E402 -from runtime import parallel_solvers, core_range # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" -_STAGE2_SAMPLE = _HERE / "shared" / "stage2_sample.json" -_STAGE3_SAMPLE = _HERE / "shared" / "stage3_sample.json" -_STAGE4_SAMPLE = _HERE / "shared" / "stage4_sample.json" -_OUT = _HERE / "shared" / "local_baseline.json" - -# Baseline measurement must never be truncated — let z3 finish naturally. -# 1 hour cap is just a safety against true infinite loops; not expected to trigger. -REBASELINE_TIMEOUT_S = 3600 - - -def _load_problem_index(): - idx = {} - with open(_PROBLEMS_JSONL) as f: - for line in f: - d = json.loads(line) - idx[d["problem_sha256"]] = { - "sha": d["problem_sha256"], - "smt2": d["smt2_filename"], - "raw_ms": d["z3_status"]["elapsed_ms"], - "raw_result": d["z3_status"]["result"], - } - return idx - - -def _load_target_shas(): - # Rebaseline the union of stage1 + stage2 + stage3 samples. With cascade - # evaluation enabled all three stages run in the evolve loop, so accurate - # local baseline is required for per-problem timeout = baseline_ms * 1.3 - # to be machine-correct. Without rebaselining stage3, 45/50 problems fall - # back to raw_ms recorded on a different machine and timeout calibration - # drifts. - if not _STAGE1_SAMPLE.exists(): - print(f"ERROR: {_STAGE1_SAMPLE} missing — run build_samples.py first", - file=sys.stderr) - sys.exit(2) - shas = [] - seen = set() - for sample_path, label in ( - (_STAGE1_SAMPLE, "stage1"), - (_STAGE2_SAMPLE, "stage2"), - (_STAGE3_SAMPLE, "stage3"), - (_STAGE4_SAMPLE, "stage4"), - ): - if not sample_path.exists(): - print(f"WARN: {sample_path.name} missing — skipping {label}", - file=sys.stderr) - continue - for sha in json.loads(sample_path.read_text())["sha256"]: - if sha not in seen: - shas.append(sha) - seen.add(sha) - return shas - - -def main(): - shas = _load_target_shas() - idx = _load_problem_index() - - 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_path = _RAW_DIR / meta["smt2"] - if not smt2_path.exists(): - print(f"ERROR: smt2 not found: {smt2_path}", file=sys.stderr) - return 2 - tasks.append((i, meta, smt2_path)) - - import queue as _queue - # Cores leased from a queue so each in-flight task holds a unique slot. - # OPENEVOLVE_CORE_RANGE (e.g. "1-5") overrides; else cores 1..N from - # parallel_solvers (core 0 reserved for kernel housekeeping). - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(cores), len(tasks)) - cores = cores[:n_parallel] - print(f"rebaselining stage1+stage2+stage3+stage4 samples: {len(tasks)} problems " - f"(union of stage{{1,2,3,4}}_sample.json)") - print(f"timeout per problem = {REBASELINE_TIMEOUT_S}s (effectively unbounded " - f"— never cut a baseline run short), parallel={n_parallel} cores={cores}") - print() - - _core_pool = _queue.Queue() - for _c in cores: - _core_pool.put(_c) - - def _solve(task): - i, meta, smt2_path = task - core = _core_pool.get() - try: - res = run_z3(smt2_path, BASELINE, REBASELINE_TIMEOUT_S, cpu_core=core) - finally: - _core_pool.put(core) - return i, meta, res, core - - t_start = time.monotonic() - completed = [] - if n_parallel == 1: - for task in tasks: - completed.append(_solve(task)) - else: - with ThreadPoolExecutor(max_workers=n_parallel) as ex: - futures = [ex.submit(_solve, t) for t in tasks] - for fut in as_completed(futures): - completed.append(fut.result()) - completed.sort(key=lambda x: x[0]) - - out = {} - mismatch = 0 - for i, meta, res, core in completed: - got_result = res.get("result", "Unknown") - got_ms = int(res.get("elapsed_ms", 0)) - ok = (got_result == meta["raw_result"]) - if not ok: - mismatch += 1 - - flag = "" if ok else " MISMATCH" - ratio = got_ms / max(meta["raw_ms"], 1) - print( - f" [{i+1:>2}/{len(tasks)}] {meta['sha'][:10]} " - f"raw={meta['raw_result']:<7}/{meta['raw_ms']:>7}ms " - f"local={got_result:<7}/{got_ms:>7}ms ratio={ratio:.2f}x{flag} " - f"core={core}", - flush=True, - ) - - out[meta["sha"]] = { - "elapsed_ms": got_ms, - "result": got_result, - "matches_raw": ok, - "raw_elapsed_ms": meta["raw_ms"], - "stats": res.get("stats") or {}, - } - - elapsed = time.monotonic() - t_start - _OUT.write_text(json.dumps(out, indent=2) + "\n") - print() - print(f"wrote {_OUT.relative_to(_BENCH_DIR.parent)} " - f"({len(out)} entries, {mismatch} mismatches)") - print(f"total time: {elapsed:.1f}s") - if mismatch: - print(f"WARNING: {mismatch} problems had result mismatch — " - f"evaluator will keep raw_ms for those") - return 0 if mismatch == 0 else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/input/z3-bench/evolve/shared/baseline_params.py b/input/z3-bench/evolve/shared/baseline_params.py deleted file mode 100644 index 27e7a04796..0000000000 --- a/input/z3-bench/evolve/shared/baseline_params.py +++ /dev/null @@ -1,164 +0,0 @@ -""" -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, core_range # 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)) - - cores = core_range() - if cores is None: - cores = list(range(1, parallel_solvers(default=5) + 1)) - n_parallel = min(len(cores), len(tasks)) - cores = cores[:n_parallel] - - print(f"BASELINE self-test: {len(tasks)} stage1 problems, parallel={n_parallel} " - f"cores={cores}") - 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)) - core = cores[i % n_parallel] - 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"{cores[i % n_parallel]:>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/shared/evaluator.py b/input/z3-bench/evolve/shared/evaluator.py deleted file mode 100644 index e03857c54b..0000000000 --- a/input/z3-bench/evolve/shared/evaluator.py +++ /dev/null @@ -1,443 +0,0 @@ -""" -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 = 1.3 -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, core_range # 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. - # Cores leased from a queue.Queue so each in-flight task holds a unique - # slot (idx % n_parallel would collide when len(problems) > n_parallel). - # OPENEVOLVE_CORE_RANGE (e.g. "1-5") overrides; else cores - # 1..parallel_solvers() (core 0 reserved for kernel housekeeping). - import queue as _queue - _cores = core_range() - if _cores is None: - _cores = list(range(1, parallel_solvers(default=1) + 1)) - n_parallel = min(len(_cores), len(problems)) - _cores = _cores[:n_parallel] - _core_pool = _queue.Queue() - for _c in _cores: - _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/shared/runtime.py b/input/z3-bench/evolve/shared/runtime.py deleted file mode 100644 index ba708c1055..0000000000 --- a/input/z3-bench/evolve/shared/runtime.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Thin wrapper around input/_lib/runtime.py. -Binds config.yaml path; re-exports parallel_solvers / cascade_threshold / core_range. -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_CONFIG_YAML = _HERE.parent / "config.yaml" -_INPUT_DIR = _HERE.parents[2] # input/ - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib import runtime as _rt # noqa: E402 - -core_range = _rt.core_range - - -def parallel_solvers(default=1): - return _rt.parallel_solvers(_CONFIG_YAML, default=default) - - -def cascade_threshold(index, default): - return _rt.cascade_threshold(_CONFIG_YAML, index, default) diff --git a/input/z3-bench/evolve/shared/score.py b/input/z3-bench/evolve/shared/score.py deleted file mode 100644 index 04062cd4da..0000000000 --- a/input/z3-bench/evolve/shared/score.py +++ /dev/null @@ -1,137 +0,0 @@ -""" -Scoring: weighted_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 -- per-problem weight = baseline_ms so absolute time savings on long-running - problems dominate; small-runtime wins barely move the needle, and a - regression on a slow problem is penalized far more than on a fast one -- solved_rate squared to strongly gate on correctness -- efficiency = cross-problem geomean of per-problem weighted geomean over - SMT solver stats {conflicts, decisions, propagations}, with each ratio - (baseline_stat+1)/(variant_stat+1) clipped to [0.1, 10] to bound outliers. - Only solved problems with baseline stats present contribute. Lower solver - work vs baseline -> efficiency > 1. Folded multiplicatively via STATS_WEIGHT - exponent (default 0.333 -> stats / (speedup + stats) ~= 25% in log space; - recommended band 0.2-0.5). Override via env OPENEVOLVE_STATS_WEIGHT - (0 disables). - -Per-key weights reflect SMT solver signal quality: -- conflicts (2.0): CDCL backtracks/learned clauses. Strongest predictor of - search difficulty; lower vs baseline = smarter navigation; robust to - hardware noise. -- decisions (1.5): branching choices. Tracks search-tree size and branching - heuristic quality independent of conflicts. -- propagations (0.5): BCP + theory propagation. High variance; can mean - early pruning (good) or theory thrashing (bad). Tiebreaker only. -- 'mk clause' / 'restarts' intentionally excluded: learning is a feature - (fewer learned clauses != better), restart count alone no work signal. - -Two-level aggregate (per-problem then cross-problem) gives each benchmark -an equal vote regardless of how many stat keys it reports. - -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_WEIGHTS = {"conflicts": 2.0, "decisions": 1.5, "propagations": 0.5} -_RATIO_CLIP_LO = 0.1 -_RATIO_CLIP_HI = 10.0 - - -def _efficiency(per_problem): - """Cross-problem geomean of per-problem weighted geomean of clipped ratios. - - Returns (efficiency, num_problems). efficiency=1.0 if no usable problems - (no baseline stats yet, or no solved problems) so the multiplier is a no-op. - """ - 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 - # +1 smoothing avoids div-by-zero and absurd ratios for tiny counts - r = (float(b) + 1.0) / (float(v) + 1.0) - # Clip so one runaway problem/key can't dominate the geomean - 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 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 = [] - weights = [] - solved = 0 - regressions = 0 - for p in per_problem: - baseline_decided = p["baseline_result"] in ("Sat", "Unsat") - match = p["result"] == p["baseline_result"] - w = max(float(p["baseline_ms"]), 1.0) - weights.append(w) - 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 - - 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 / n - - efficiency, eff_problems = _efficiency(per_problem) - try: - stats_weight = float(os.environ.get("OPENEVOLVE_STATS_WEIGHT", "0.333")) - 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_problems), - "stats_weight": float(stats_weight), - } diff --git a/input/z3-bench/evolve/shared/stage1_sample.json b/input/z3-bench/evolve/shared/stage1_sample.json deleted file mode 100644 index 2f7e6d7d1f..0000000000 --- a/input/z3-bench/evolve/shared/stage1_sample.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "selection": "5 SAT runtime Q1+2 (fastest 40%)", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "b6393502fdb5834104bcd66ed1a29e6150c036beb3c8a185f04a6341b3fd3102", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", - "a4658d3a051a31bc6d19ea051195dacfaaea52a34530d63756eab51df3168f97", - "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", - "aa358daf42261994339da8ef373dcdf18401ca35669f908e1023568b5302de07" - ], - "summary": [ - { - "sha": "b6393502fdb5", - "num_hard_constraints": 25205, - "num_variables": 5854, - "baseline_result": "Sat", - "baseline_ms": 312 - }, - { - "sha": "17cc4d349ce3", - "num_hard_constraints": 15243, - "num_variables": 3932, - "baseline_result": "Sat", - "baseline_ms": 366 - }, - { - "sha": "a4658d3a051a", - "num_hard_constraints": 25205, - "num_variables": 5854, - "baseline_result": "Sat", - "baseline_ms": 597 - }, - { - "sha": "872cbdb83f73", - "num_hard_constraints": 59387, - "num_variables": 10244, - "baseline_result": "Sat", - "baseline_ms": 1298 - }, - { - "sha": "aa358daf4226", - "num_hard_constraints": 59387, - "num_variables": 10244, - "baseline_result": "Sat", - "baseline_ms": 1674 - } - ] -} diff --git a/input/z3-bench/evolve/shared/stage2_sample.json b/input/z3-bench/evolve/shared/stage2_sample.json deleted file mode 100644 index f49a539fa8..0000000000 --- a/input/z3-bench/evolve/shared/stage2_sample.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "selection": "5 SAT runtime Q3+4 (middle 40%)", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "6aab51935a092a1d7f05bb431d3d05e0de50c19f5f983026a6d14e7c4b9cf240", - "75ee534dbc926b1e245277f82a9f102ccba7718fa1f7cd6e4ed60fec3db0c4d8", - "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "318bc251e0bd49f3b67bc85aa2e58b370e8b7ed5228e059d06bf58748284e4e4", - "493242c379920ed6cd98a5096fd2674877b98eaa17bbb3a758bbf1b3db38df12" - ], - "summary": [ - { - "sha": "6aab51935a09", - "num_hard_constraints": 113172, - "num_variables": 17151, - "baseline_result": "Sat", - "baseline_ms": 3681 - }, - { - "sha": "75ee534dbc92", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Sat", - "baseline_ms": 13086 - }, - { - "sha": "069af7891076", - "num_hard_constraints": 105784, - "num_variables": 32912, - "baseline_result": "Sat", - "baseline_ms": 13885 - }, - { - "sha": "318bc251e0bd", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 14232 - }, - { - "sha": "493242c37992", - "num_hard_constraints": 106100, - "num_variables": 33068, - "baseline_result": "Sat", - "baseline_ms": 14353 - } - ] -} diff --git a/input/z3-bench/evolve/shared/stage3_sample.json b/input/z3-bench/evolve/shared/stage3_sample.json deleted file mode 100644 index 720a79a9c9..0000000000 --- a/input/z3-bench/evolve/shared/stage3_sample.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "selection": "5 SAT runtime Q5 (slowest 20%)", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "cc611a5155b2d4fc3e0ee9f52181aea53ceefcbb5763e9f5e481ae7c93ebcc52", - "3854194b901b0b424b5199a54fa8e626506e605f0dd8cae64c75786012037c44", - "5250d459bef23286b67be03e7abec394ceb5d337f40885df8ac4e76eb48d33f8", - "86efb0762e6d99d9ade8f6718be01a366a53704b6ee199bf0e91af310c68d6f3", - "a7df812c620d14325d4d18dccdb4e3ae469f5727ac6f31d49331dd79d223af40" - ], - "summary": [ - { - "sha": "cc611a5155b2", - "num_hard_constraints": 524539, - "num_variables": 59881, - "baseline_result": "Sat", - "baseline_ms": 42599 - }, - { - "sha": "3854194b901b", - "num_hard_constraints": 524914, - "num_variables": 59955, - "baseline_result": "Sat", - "baseline_ms": 66100 - }, - { - "sha": "5250d459bef2", - "num_hard_constraints": 524936, - "num_variables": 59955, - "baseline_result": "Sat", - "baseline_ms": 67552 - }, - { - "sha": "86efb0762e6d", - "num_hard_constraints": 696736, - "num_variables": 83621, - "baseline_result": "Sat", - "baseline_ms": 80512 - }, - { - "sha": "a7df812c620d", - "num_hard_constraints": 696365, - "num_variables": 83551, - "baseline_result": "Sat", - "baseline_ms": 93445 - } - ] -} diff --git a/input/z3-bench/evolve/shared/stage4_sample.json b/input/z3-bench/evolve/shared/stage4_sample.json deleted file mode 100644 index 5dfe3cfec6..0000000000 --- a/input/z3-bench/evolve/shared/stage4_sample.json +++ /dev/null @@ -1,168 +0,0 @@ -{ - "selection": "20 SAT+UNSAT broad, dedup vs stage1-3", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", - "fe3a23ba2a11867b3e3498c501502f51befc6e98b7117d10773441d2838f093b", - "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", - "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", - "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", - "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", - "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", - "0f6f9aa28440a0a26cc61b28f0a52d2a5f5b83061b46651c0741ffac1bb4d678", - "21069961e8dc3386828e2608f2ece27db08b11be6e2da9ebadbc40675687e1fb", - "8a304b9c461ac8f19ce464b71010e8ef043d1ee4b857a60d871941a168fea59a", - "d8d5ffa7d82453487927cb6588bf547b89fa3149bb1b8865bac16dcc2516af84", - "29efe6d38d7b8fcf41ed69b73f5a0bb370f782ab61ffe564fbe41512ea75ce5f", - "65444d69388120f98e21ea06a1cf388e621dbc77318828bd673832e90963c568", - "4a02d7846394cb507e7d12f4db736ab7c54c536a8097c32502ad952026dfa64a", - "86468fd861ffa391b567ca57dc57c9b95b8577942d3fd2587e244073bdee43ef", - "f54dba052fbf975e36b4f6dae5b47802965275f27cc276cd8a6199f763a7b8a4", - 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Python -binding is preferred over CLI because z3 4.13.x CLI rejects globals like -`threads`, `parallel.enable`, `sls.parallel` as positional `key=value`, -which the binding accepts. Matches the original benchmark's -`applied_params_hash 543b29...` setup. - -Status strings: "Sat" / "Unsat" / "Unknown". -""" -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -_INPUT_DIR = _HERE.parents[2] -_WORKER = _HERE / "_z3_solve_worker.py" - -if str(_INPUT_DIR) not in sys.path: - sys.path.insert(0, str(_INPUT_DIR)) - -from _lib.subprocess_runner import run_solver # noqa: E402 - - -def run_z3(smt2_path, params, timeout_s, python_bin=None, cpu_core=None): - return run_solver(_WORKER, smt2_path, params, timeout_s, - python_bin=python_bin, cpu_core=cpu_core) diff --git a/input/z3-bench/evolve/shared/z3_valid_keys.json b/input/z3-bench/evolve/shared/z3_valid_keys.json deleted file mode 100644 index 2ff0093f49..0000000000 --- a/input/z3-bench/evolve/shared/z3_valid_keys.json +++ /dev/null @@ -1,1266 +0,0 @@ -[ - "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/validate_keys.py b/input/z3-bench/evolve/validate_keys.py deleted file mode 100644 index b289ccce07..0000000000 --- a/input/z3-bench/evolve/validate_keys.py +++ /dev/null @@ -1,160 +0,0 @@ -""" -Validate phase EVOLVE-BLOCK keys against the installed z3 binary. - -Strategy: - 1. `z3 -pm` -> list of module names - 2. `z3 -pm:` for each module -> option names + types + defaults - 3. `z3 -p` -> global (module-less) option names - -> build canonical set of valid `module.option` (and bare global) keys - Then for each phase initial_program.py, report invalid keys. - -Run inside the container (z3 binary required): - - python input/z3-bench/evolve/validate_keys.py - -Output: - shared/z3_valid_keys.json # full valid set - stdout report per phase, listing invalid keys - -Does NOT modify initial_program.py. Edit manually after reviewing. -""" -import importlib.util -import json -import pathlib -import re -import shutil -import subprocess -import sys - -ROOT = pathlib.Path(__file__).resolve().parent -SHARED = ROOT / "shared" -PHASES = [ - "phase1_opt_sls", - "phase2_sat", - "phase3_smt", - "phase4_unified", -] - - -_OPTION_LINE_RE = re.compile(r"^\s+([\w\-]+(?:\.[\w\-]+)*)\s+\(") - - -def _run_z3(args): - proc = subprocess.run(["z3", *args], capture_output=True, text=True) - if proc.returncode != 0: - # Some -pm: invocations exit non-zero on unknown module; treat as soft. - return proc.stdout, proc.stderr, proc.returncode - return proc.stdout, proc.stderr, 0 - - -def _parse_options(text): - """Pull ' name (type) ...' lines out of any z3 doc dump.""" - names = [] - for line in text.splitlines(): - m = _OPTION_LINE_RE.match(line) - if m: - names.append(m.group(1)) - return names - - -# Z3 4.13.x format: `[module] (, description: )?` -# Older builds: `[module] ` alone. Take only the first identifier token. -_MODULE_HEADER_RE = re.compile(r"^\[module\]\s+([A-Za-z_][\w\-]*)") - - -def _list_modules(): - stdout, stderr, rc = _run_z3(["-pm"]) - if rc != 0: - print(f"`z3 -pm` failed: {stderr.strip()}", file=sys.stderr) - sys.exit(1) - mods = set() - for line in stdout.splitlines(): - m = _MODULE_HEADER_RE.match(line) - if m: - mods.add(m.group(1)) - if not mods: - print("warning: no `[module] ` headers found in `z3 -pm` output", file=sys.stderr) - return sorted(mods) - - -def get_valid_keys(): - if shutil.which("z3") is None: - print("ERROR: z3 binary not on PATH. Install in container first.", file=sys.stderr) - sys.exit(1) - - valid = set() - - # 1. Globals via `z3 -p` - stdout, _, rc = _run_z3(["-p"]) - if rc == 0: - for opt in _parse_options(stdout): - valid.add(opt) - - # 2. Modules + their options. Try each candidate token from `-pm` output; - # silently skip ones z3 rejects (prose/noise gets filtered this way). - mods = _list_modules() - if not mods: - print("warning: no modules parsed from `z3 -pm`", file=sys.stderr) - tried = 0 - accepted = 0 - debug_dumped = False - for mod in mods: - tried += 1 - stdout, stderr, rc = _run_z3([f"-pm:{mod}"]) - if rc != 0: - continue # quiet skip - accepted += 1 - opts = _parse_options(stdout) - if not opts and not debug_dumped: - print(f" no options parsed from module {mod!r}; raw output (first 30 lines):", file=sys.stderr) - for line in stdout.splitlines()[:30]: - print(f" | {line}", file=sys.stderr) - debug_dumped = True - for opt in opts: - valid.add(f"{mod}.{opt}") - print(f" modules: {accepted}/{tried} accepted, {len(valid)} valid keys total") - - return valid - - -def load_program_params(phase_dir): - """Load initial_program.py and return its get_params() output.""" - sys.path.insert(0, str(SHARED)) - spec = importlib.util.spec_from_file_location( - f"prog_{phase_dir.name}", phase_dir / "initial_program.py" - ) - m = importlib.util.module_from_spec(spec) - spec.loader.exec_module(m) - return m.get_params() - - -def main(): - print("Probing z3 -p / z3 -pm / z3 -pm: ...") - valid = get_valid_keys() - out = SHARED / "z3_valid_keys.json" - out.write_text(json.dumps(sorted(valid), indent=2)) - print(f"wrote {out.relative_to(ROOT)} ({len(valid)} entries)\n") - - any_invalid = False - for ph in PHASES: - d = ROOT / ph - if not (d / "initial_program.py").exists(): - continue - params = load_program_params(d) - invalid = sorted(k for k in params if k not in valid) - status = "OK" if not invalid else f"INVALID x{len(invalid)}" - print(f"=== {ph}: {len(params)} keys, {status} ===") - for k in invalid: - print(f" - {k!r}") - print() - if invalid: - any_invalid = True - - if any_invalid: - print("Edit the offending initial_program.py files to remove or rename invalid keys.") - sys.exit(2) - print("All phase keys validate against `z3 -pm` introspection.") - - -if __name__ == "__main__": - main() diff --git a/input/z3-bench/evolve/verify_stage1_baseline.py b/input/z3-bench/evolve/verify_stage1_baseline.py deleted file mode 100644 index 820e3b4e87..0000000000 --- a/input/z3-bench/evolve/verify_stage1_baseline.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -Verify stage1_sample.json reproducibility against raw baseline. - -For each of the 5 sampled problems, run z3 with BASELINE params and compare: - - result must match baseline_result - - elapsed_ms ratio (got / baseline) must lie in [LOW, HIGH] - -Per-problem timeout = baseline_ms (as recorded in raw data). -Single run per problem (no median). -""" -import json -import math -import pathlib -import sys - -_HERE = pathlib.Path(__file__).resolve().parent -sys.path.insert(0, str(_HERE / "shared")) - -from baseline_params import BASELINE # noqa: E402 -from z3_runner import run_z3 # noqa: E402 - -_BENCH_DIR = _HERE.parent -_RAW_DIR = _BENCH_DIR / "raw-data" -_PROBLEMS_JSONL = _BENCH_DIR / "problems.jsonl" -_STAGE1_SAMPLE = _HERE / "shared" / "stage1_sample.json" - -RATIO_LOW = 0.5 -RATIO_HIGH = 2.0 - - -def _load_sample_shas(): - return list(json.loads(_STAGE1_SAMPLE.read_text())["sha256"]) - - -def _index_problems_by_sha(): - 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"], - } - return idx - - -def verify(): - shas = _load_sample_shas() - idx = _index_problems_by_sha() - - rows = [] - fail = 0 - warn = 0 - for sha in shas: - meta = idx.get(sha) - if meta is None: - print(f"[FAIL] {sha[:12]} not in problems.jsonl") - fail += 1 - continue - - smt2_path = _RAW_DIR / meta["smt2"] - if not smt2_path.exists(): - print(f"[FAIL] {sha[:12]} missing smt2 {smt2_path}") - fail += 1 - continue - - timeout_s = max(1, math.ceil(meta["baseline_ms"] / 1000)) - r = run_z3(smt2_path, BASELINE, timeout_s) - - got_result = r.get("result", "Unknown") - got_ms = int(r.get("elapsed_ms", 0)) - is_timeout = bool(r.get("timeout")) - invalid = r.get("invalid_param") - err = r.get("error") - stderr = r.get("stderr") - - ratio = got_ms / max(meta["baseline_ms"], 1) - result_ok = (got_result == meta["baseline_result"]) - ratio_ok = RATIO_LOW <= ratio <= RATIO_HIGH - - if invalid: - status = "FAIL(invalid)" - fail += 1 - elif err: - status = "FAIL(error)" - fail += 1 - elif is_timeout or not result_ok: - status = "FAIL" - fail += 1 - elif not ratio_ok: - status = "WARN" - warn += 1 - else: - status = "OK" - - rows.append((sha, meta, got_result, got_ms, ratio, status, invalid, is_timeout, err, stderr)) - - print() - print(f"{'sha':<14}{'base_res':<10}{'got_res':<10}" - f"{'base_ms':>10}{'got_ms':>10}{'ratio':>8} status") - print("-" * 78) - for sha, meta, got_result, got_ms, ratio, status, invalid, is_timeout, err, stderr in rows: - extra = "" - if invalid: - extra = f" invalid={invalid}" - elif err: - extra = f" err={err[:200]}" - elif is_timeout: - extra = " (timeout)" - print(f"{sha[:12]:<14}{meta['baseline_result']:<10}{got_result:<10}" - f"{meta['baseline_ms']:>10}{got_ms:>10}{ratio:>7.2f}x {status}{extra}") - if stderr and err: - print(f" stderr: {stderr[:400]}") - - print() - print(f"summary: {len(rows) - fail - warn} ok, {warn} warn, {fail} fail " - f"(ratio band [{RATIO_LOW}, {RATIO_HIGH}], timeout=baseline_ms)") - return 0 if fail == 0 else 1 - - -if __name__ == "__main__": - sys.exit(verify()) From 1af4575b506b0f40900d2c8b6794ce79c6a256fa Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 1 Jun 2026 14:04:59 +0900 Subject: [PATCH 40/42] [chore] sampler stage reports, build_problems scripts, gitignore cleanup - _lib/sampler.py: print per-stage report (picks vs pool quartiles for baseline_ms + feature, result distribution, pick list with sha / result / ms / feature). Surfaces clustering quality + sample spread at a glance. - cpsat-bench/build_problems.py + z3-bench/build_problems.py: scan raw-data/*.meta.jsonl and emit problems.jsonl (one row per meta entry, sorted by sha + applied_params_hash). Replaces the raw-scan portion of the deleted build_samples.py wrappers. Flags: --filter-decisive, --applied-params-hash, --dry-run, --out. _lib stays solver-agnostic; these scripts handle solver-specific meta.jsonl format. - ADD_NEW_SOLVER.md: quick-start guide for integrating a new solver (cvc5, minizinc, picosat, ...). Covers raw-data layout, problems.jsonl schema, the 4 hand-edited per-bench files (config.yaml + params.json + adapter.py + _solve_worker.py), phase modules, verification steps, option decisions (score_mode / worker axis / buckets / clustering), common pitfalls, time estimates. - .gitignore: stop tracking locally-regenerated artifacts. Drop the dead `shared/*` patterns (refactor removed shared/), add: input/*/evolve/cache/ # _lib regenerates on demand input/*/evolve/final_program.py # _lib.finalize regenerates input/*/evolve/phase*/openevolve_output*/ input/*/evolve/phase*/__pycache__/ Untrack 8 previously-committed cache/stage{1..4}_sample.json files (still on disk, rebuilt by _lib.sampler). - cpsat/z3 config.yaml: minor knob tweaks (user-side feature-path tuning during smoke runs). Co-Authored-By: Claude Opus 4.7 (1M context) --- .gitignore | 21 +- input/ADD_NEW_SOLVER.md | 664 ++++++++++++++++++ input/_lib/sampler.py | 77 +- input/cpsat-bench/build_problems.py | 122 ++++ .../evolve/cache/stage1_sample.json | 79 --- .../evolve/cache/stage2_sample.json | 79 --- .../evolve/cache/stage3_sample.json | 44 -- .../evolve/cache/stage4_sample.json | 149 ---- input/cpsat-bench/evolve/config.yaml | 8 +- input/z3-bench/build_problems.py | 122 ++++ .../z3-bench/evolve/cache/stage1_sample.json | 44 -- .../z3-bench/evolve/cache/stage2_sample.json | 44 -- .../z3-bench/evolve/cache/stage3_sample.json | 44 -- .../z3-bench/evolve/cache/stage4_sample.json | 149 ---- input/z3-bench/evolve/config.yaml | 2 +- 15 files changed, 998 insertions(+), 650 deletions(-) create mode 100644 input/ADD_NEW_SOLVER.md create mode 100644 input/cpsat-bench/build_problems.py delete mode 100644 input/cpsat-bench/evolve/cache/stage1_sample.json delete mode 100644 input/cpsat-bench/evolve/cache/stage2_sample.json delete mode 100644 input/cpsat-bench/evolve/cache/stage3_sample.json delete mode 100644 input/cpsat-bench/evolve/cache/stage4_sample.json create mode 100644 input/z3-bench/build_problems.py delete mode 100644 input/z3-bench/evolve/cache/stage1_sample.json delete mode 100644 input/z3-bench/evolve/cache/stage2_sample.json delete mode 100644 input/z3-bench/evolve/cache/stage3_sample.json delete mode 100644 input/z3-bench/evolve/cache/stage4_sample.json diff --git a/.gitignore b/.gitignore index 39007b6e08..6abec3a4ac 100644 --- a/.gitignore +++ b/.gitignore @@ -59,15 +59,14 @@ problems input/z3-bench/raw-data input/cpsat-bench/raw-data -# z3-bench evolve run artifacts (locally regenerated) -input/z3-bench/evolve/shared/local_baseline.json -input/z3-bench/evolve/shared/phase*_best.json +# Bench evolve cache (locally regenerated by _lib.sampler / _lib.rebaseline / +# _lib.extract_best / _lib.prepare_phase). Safe to delete. +input/*/evolve/cache/ -# cpsat-bench evolve run artifacts (locally regenerated) -input/cpsat-bench/evolve/shared/local_baseline.json -input/cpsat-bench/evolve/shared/outliers.json -input/cpsat-bench/evolve/shared/has_objective_cache.json -input/cpsat-bench/evolve/shared/*_sample.json -input/cpsat-bench/evolve/shared/phase*_best.json -input/cpsat-bench/evolve/shared/phase*_buckets.json -input/cpsat-bench/evolve/shared/phase*_stage3.json +# 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/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/_lib/sampler.py b/input/_lib/sampler.py index 6a08c638d3..2c5edf5b8b 100644 --- a/input/_lib/sampler.py +++ b/input/_lib/sampler.py @@ -144,6 +144,74 @@ def _pick_spread(pool, n_pick, mode): 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/`.""" @@ -243,6 +311,9 @@ def _range(b): 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 [] @@ -269,8 +340,10 @@ def _range(b): } for r in picks], }, indent=2) + "\n" ) - print(f"wrote {sample_path.name} ({len(picks)} from clusters " - f"{cluster_ids})") + + _print_stage_report(stage_name, picks, merged, cluster_ids, + feature_path, _baseline_ms, _baseline_result, + sample_path.name) def main(argv=None): 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/cache/stage1_sample.json b/input/cpsat-bench/evolve/cache/stage1_sample.json deleted file mode 100644 index 344e6096a8..0000000000 --- a/input/cpsat-bench/evolve/cache/stage1_sample.json +++ /dev/null @@ -1,79 +0,0 @@ -{ - "selection": "10 from 352 candidates", - "criteria": "kmeans-clusters=[0, 1] feature=features.num_constraints spread=quintile", - "source": "cpsat-bench/problems.jsonl", - "sha256": [ - "5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5", - "d7e09c73e7c387ce90ed94c06031780e15353ec9e09ac3a8d2ce6cb1af0dc6f9", - "2c283b1f086ec33a86a8cf25694b14acb56c62c180fb239dd3813e63d7c3a137", - "cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4", - "cda37747aa2ae783bc3093f9fadf7f26653039f6e52fbfc193cae8538c2777e4", - "303a6733c9ec03beaf7942544bf89ce2ecba38c65ae2e6a93e248b36e6b7fc2a", - "41f6ecf9ca6462dcb7ca49e028b2970175579c0250058e0430e979a04435bf09", - "7a78d4951c1a8b67cebf1dd62ec358ef792f9fd6000319b75cda30b8a24775e6", - "6396211e6842d6969cce80194013fff987eb3e308c435eccd942b5007dec4639", - "ba0fe698c28be555ec428f05daa2aa901ecf9694c6c23043b76a91a7a847b111" - ], - "summary": [ - { - "sha": "5976726485c2", - "baseline_result": "OPTIMAL", - "baseline_ms": 105.102, - "features.num_constraints": 7361 - }, - { - "sha": "d7e09c73e7c3", - "baseline_result": "OPTIMAL", - "baseline_ms": 688.182, - "features.num_constraints": 20658 - }, - { - "sha": "2c283b1f086e", - "baseline_result": "OPTIMAL", - "baseline_ms": 1361.71, - "features.num_constraints": 20886 - }, - { - "sha": "cda37747aa2a", - "baseline_result": "OPTIMAL", - "baseline_ms": 1928.9, - "features.num_constraints": 15385 - }, - { - "sha": "cda37747aa2a", - "baseline_result": "OPTIMAL", - "baseline_ms": 2872.95, - "features.num_constraints": 15385 - }, - { - "sha": "303a6733c9ec", - "baseline_result": "OPTIMAL", - "baseline_ms": 3530.81, - "features.num_constraints": 20766 - }, - { - "sha": "41f6ecf9ca64", - "baseline_result": "OPTIMAL", - "baseline_ms": 5144.56, - "features.num_constraints": 21983 - }, - { - "sha": "7a78d4951c1a", - "baseline_result": "OPTIMAL", - "baseline_ms": 7019.67, - "features.num_constraints": 30546 - }, - { - "sha": "6396211e6842", - "baseline_result": "OPTIMAL", - "baseline_ms": 8973.46, - "features.num_constraints": 31882 - }, - { - "sha": "ba0fe698c28b", - "baseline_result": "OPTIMAL", - "baseline_ms": 25129, - "features.num_constraints": 15565 - } - ] -} diff --git a/input/cpsat-bench/evolve/cache/stage2_sample.json b/input/cpsat-bench/evolve/cache/stage2_sample.json deleted file mode 100644 index bf1bd8e7a4..0000000000 --- a/input/cpsat-bench/evolve/cache/stage2_sample.json +++ /dev/null @@ -1,79 +0,0 @@ -{ - "selection": "10 from 332 candidates", - "criteria": "kmeans-clusters=[2, 3] feature=features.num_constraints spread=quintile", - "source": "cpsat-bench/problems.jsonl", - "sha256": [ - "4c230e355482c3652bd60bf83cf4ad77382cdb0b9abe08c27007683545e8f071", - "9c3c55cd54e6074a7e2732ccfaac2e90698141e7e9fb30c946657a21fb186061", - "6ecbb5bc3a035ad7b51348fac58112aa35296e0e6ae9405119a1296fb9384045", - "e5c9e667fbee60ca8a589904e1a7afc30c81394efd58e388a9f63470fa4ea406", - "b13bb2432694ba5fe9768abc03525698986ba13ffe70a7112b90bd7fbad02aec", - "f7041abae337b502aa03618b1c0f40cc693acb9c0bd48efe21b41c42a7849bef", - "f77cdd0764d8aa490133369275f4d62d71f47133f6102d90caa9a799c69ddc63", - "f5931bc8ee19786a4ade31e56bdc19d415c4dbfa42da07785765944fe6748326", - "a46c87c567e8ea0a6fa70198b83d8523a69f38448aeafbb35a956a845987993a", - "58d58a76371ef7762bf96386ce5d2677d1bfc7d7df745d40f4a02bd30006662d" - ], - "summary": [ - { - "sha": "4c230e355482", - "baseline_result": "OPTIMAL", - "baseline_ms": 3021.82, - "features.num_constraints": 42727 - }, - { - "sha": "9c3c55cd54e6", - "baseline_result": "OPTIMAL", - "baseline_ms": 4022.2, - "features.num_constraints": 58096 - }, - { - "sha": "6ecbb5bc3a03", - "baseline_result": "OPTIMAL", - "baseline_ms": 5580.53, - "features.num_constraints": 58433 - }, - { - "sha": "e5c9e667fbee", - "baseline_result": "OPTIMAL", - "baseline_ms": 6608.39, - "features.num_constraints": 47566 - }, - { - "sha": "b13bb2432694", - "baseline_result": "OPTIMAL", - "baseline_ms": 8842.37, - "features.num_constraints": 57828 - }, - { - "sha": "f7041abae337", - "baseline_result": "OPTIMAL", - "baseline_ms": 11511.1, - "features.num_constraints": 38286 - }, - { - "sha": "f77cdd0764d8", - "baseline_result": "OPTIMAL", - "baseline_ms": 14497.4, - "features.num_constraints": 47233 - }, - { - "sha": "f5931bc8ee19", - "baseline_result": "OPTIMAL", - "baseline_ms": 18510.4, - "features.num_constraints": 45243 - }, - { - "sha": "a46c87c567e8", - "baseline_result": "OPTIMAL", - "baseline_ms": 25758.6, - "features.num_constraints": 62282 - }, - { - "sha": "58d58a76371e", - "baseline_result": "OPTIMAL", - "baseline_ms": 80851.6, - "features.num_constraints": 64588 - } - ] -} diff --git a/input/cpsat-bench/evolve/cache/stage3_sample.json b/input/cpsat-bench/evolve/cache/stage3_sample.json deleted file mode 100644 index 0c90536be7..0000000000 --- a/input/cpsat-bench/evolve/cache/stage3_sample.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "selection": "5 from 48 candidates", - "criteria": "kmeans-clusters=[4] feature=features.num_constraints spread=quintile", - "source": "cpsat-bench/problems.jsonl", - "sha256": [ - "c8c8858fad7755abe96b96b28bcbb93492fb84c5213e56797044f8ec51fbdf8a", - 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"selection": "20 from 732 candidates", - "criteria": "kmeans-clusters=[0, 1, 2, 3, 4] feature=features.num_constraints spread=quintile", - "source": "cpsat-bench/problems.jsonl", - "sha256": [ - "5976726485c23db055e872eabd8a172c73609854b8c228b47ccf42422bf7c8b5", - "67f5fac68782f2951e196e4ae8273a46728b821ae433617aaf6d3242d36d5657", - "90e1185ded530d8fbb2b773076a2fdbdfbb9a725f2fc07b4d8b8280e93069b54", - "6c64b95d90a6caf1573da836e471273a2845a145a3ce07ccc4124cf7654bc226", - "024c5d8184d58d42edd90236a3922645eb1d0d94f1691afc6068b1d66a87ac5a", - "40d5a4d59eb7467a69cb957e595b84c37998dd03e00f071ad1063e219053a924", - "5978abbd9b38469692be7af1f78c5fd15d0736c6192c38ba083c6ca4778659a5", - "1bbeb46bf12047759193db1b6d07e587c7274c1c0b7623c1f146535eb7920965", - "1cbb4fb20584a2df3ff85a980e992daccf8381fb6da66657c08a35a9f6f61fe3", - "683f174a879de4bb230fe99336ee2df4ccb16d43051717a616a6bd5bfbdabfa5", - "0072ac8710594a94dcd7c8c2c6823756922cac11f5c15b2e655b9b081bedf6bc", - "6396211e6842d6969cce80194013fff987eb3e308c435eccd942b5007dec4639", - "d1b2c9992efda91256f54b5da850272632e2cd7a46cab2a4e81f74cedf156620", - "ccfac7f93fb9565b6c623253e6a722369a005a8479fe7e6d3112942ed39d274f", - "78efbf308c9f28e54d3cad8847352b9a8b3abd79a7017e40247b96b73692c5a3", - "58d58a76371ef7762bf96386ce5d2677d1bfc7d7df745d40f4a02bd30006662d", - "d381b7ed2453aa3404ecf44978c151549d5166e44f9359a2d61b3e2c19b84d0a", - "e272c0e4899bd231855a0205e36e42663a9208b44d081e1149dc37bdafe3a33d", - "62835d0eb69fc765c6c7a3e9536c90861584976cf492d552c8207ee2c9893abc", - "38e83ff258692eadb3c637027ef94f7c4f1ff1790c584444cba3c4f450281b9d" - ], - "summary": [ - { - "sha": "5976726485c2", - "baseline_result": "OPTIMAL", - "baseline_ms": 105.102, - "features.num_constraints": 7361 - }, - { - "sha": "67f5fac68782", - "baseline_result": "OPTIMAL", - "baseline_ms": 680.4, - "features.num_constraints": 14600 - }, - { - "sha": "90e1185ded53", - "baseline_result": "OPTIMAL", - "baseline_ms": 1323.68, - "features.num_constraints": 20644 - }, - { - "sha": "6c64b95d90a6", - "baseline_result": "OPTIMAL", - 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"baseline_ms": 118894, - "features.num_constraints": 135485 - } - ] -} diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index e7820b47d8..cc7f69f13f 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -35,14 +35,14 @@ bench: # === Clustering / stage sampling (consumed by _lib.sampler). === clustering: - method: kmeans # kmeans | quintile | thresholds - feature: features.num_constraints + 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: 10 - stage2: 10 + stage1: 5 + stage2: 5 stage3: 5 stage4: 20 stage_clusters: 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/evolve/cache/stage1_sample.json b/input/z3-bench/evolve/cache/stage1_sample.json deleted file mode 100644 index 83a88a2015..0000000000 --- a/input/z3-bench/evolve/cache/stage1_sample.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "selection": "5 from 27 candidates", - "criteria": "kmeans-clusters=[0, 1] feature=features.num_hard_constraints spread=quintile", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", - "4fb703b1212783821a005b7e69b3b6f6bd668f15c2877bef52c6fe2fb10fabac", - "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", - "069af7891076de4d10677710b05411844fac7d5f33f40910cc41229207fcbbc3", - "03b7e129d6254e599dcef91096f13ca6289f1d6b04e7f694a174b8f4bbb6f90c" - ], - "summary": [ - { - "sha": "fac53f0ffed2", - "baseline_result": "Unsat", - "baseline_ms": 221, - "features.num_hard_constraints": 14932 - }, - { - "sha": "4fb703b12127", - "baseline_result": "Unsat", - "baseline_ms": 311, - "features.num_hard_constraints": 15034 - }, - { - "sha": "872cbdb83f73", - "baseline_result": "Sat", - "baseline_ms": 1298, - "features.num_hard_constraints": 59387 - }, - { - "sha": "069af7891076", - "baseline_result": "Sat", - "baseline_ms": 13885, - "features.num_hard_constraints": 105784 - }, - { - "sha": "03b7e129d625", - "baseline_result": "Sat", - "baseline_ms": 16518, - "features.num_hard_constraints": 106100 - } - ] -} diff --git a/input/z3-bench/evolve/cache/stage2_sample.json b/input/z3-bench/evolve/cache/stage2_sample.json deleted file mode 100644 index 3a44296d91..0000000000 --- a/input/z3-bench/evolve/cache/stage2_sample.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "selection": "5 from 17 candidates", - "criteria": "kmeans-clusters=[2, 3] feature=features.num_hard_constraints spread=quintile", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", - "3cacf544225ef77231cc5a1ef778fd06534731623cc06144babac1140aa5acf1", - "7d51ef2bfc4c3e45350aa61d7585a4c562f3c13412b841fd4de9830a052ca385", - "86efb0762e6d99d9ade8f6718be01a366a53704b6ee199bf0e91af310c68d6f3", - "7f7b5c632ef82955fa7e13961d897ffb114e76ca4e2d183d7178639c3218af1b" - ], - "summary": [ - { - "sha": "aeec9f77b092", - "baseline_result": "Sat", - "baseline_ms": 2193, - "features.num_hard_constraints": 112978 - }, - { - "sha": "3cacf544225e", - "baseline_result": "Unsat", - "baseline_ms": 2996, - "features.num_hard_constraints": 112978 - }, - { - "sha": "7d51ef2bfc4c", - "baseline_result": "Sat", - "baseline_ms": 40558, - "features.num_hard_constraints": 524936 - }, - { - "sha": "86efb0762e6d", - "baseline_result": "Sat", - "baseline_ms": 80512, - "features.num_hard_constraints": 696736 - }, - { - "sha": "7f7b5c632ef8", - "baseline_result": "Sat", - "baseline_ms": 181205, - "features.num_hard_constraints": 696736 - } - ] -} diff --git a/input/z3-bench/evolve/cache/stage3_sample.json b/input/z3-bench/evolve/cache/stage3_sample.json deleted file mode 100644 index 3c27606ff6..0000000000 --- a/input/z3-bench/evolve/cache/stage3_sample.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "selection": "5 from 6 candidates", - "criteria": "kmeans-clusters=[4] feature=features.num_hard_constraints spread=quintile", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "f54dba052fbf975e36b4f6dae5b47802965275f27cc276cd8a6199f763a7b8a4", - "3b58bd47434b81258f5d6b182dde28df64a155b70a2aa790c4d608ec8f11ab65", - "bc60e5e73924b7f663b6320df3ffba8cb70b7ca50069d0bb586a8656aca15677", - "96158dd28c0bfa150bffb4e0a6bcd7689cb8fa3d041af533c002004b5c1aec4d", - "be1c308d797798dce2f3663ad9790822ce632331f6d9c7309a738ab8e438804e" - ], - "summary": [ - { - "sha": "f54dba052fbf", - "baseline_result": "Unsat", - "baseline_ms": 31406, - "features.num_hard_constraints": 1092831 - }, - { - "sha": "3b58bd47434b", - "baseline_result": "Unsat", - "baseline_ms": 31732, - "features.num_hard_constraints": 1092831 - }, - { - "sha": "bc60e5e73924", - "baseline_result": "Unsat", - "baseline_ms": 32076, - "features.num_hard_constraints": 1093104 - }, - { - "sha": "96158dd28c0b", - "baseline_result": "Unsat", - "baseline_ms": 33160, - "features.num_hard_constraints": 1095737 - }, - { - "sha": "be1c308d7977", - "baseline_result": "Unsat", - "baseline_ms": 33752, - "features.num_hard_constraints": 1095737 - } - ] -} diff --git a/input/z3-bench/evolve/cache/stage4_sample.json b/input/z3-bench/evolve/cache/stage4_sample.json deleted file mode 100644 index 0737417ddd..0000000000 --- a/input/z3-bench/evolve/cache/stage4_sample.json +++ /dev/null @@ -1,149 +0,0 @@ -{ - "selection": "20 from 50 candidates", - "criteria": "kmeans-clusters=[0, 1, 2, 3, 4] feature=features.num_hard_constraints spread=quintile", - "source": "z3-bench/problems.jsonl", - "sha256": [ - "fac53f0ffed255416c5d6c6cec93f2aaa8144b18ebfac4d4cf5a973f941ef8d3", - "e57b3cf2bc7e5d44864063485fcf0b98d6123bfacb936933dab8a49fec33c078", - "505a2e36055c971b7ff50c806430e09386fbd2fac5645d1480df806c1fdfbf2e", - "17cc4d349ce3ebeba20934b5f0c14c6716bd918a6bcf47bad7a684c2d1162f9a", - "133383a624eff676bc61f99b5a18375904aaba4fe865cd2d4afc76d5f00f948f", - "872cbdb83f738a59db803c6c998facb249d5e4a7b082adda9c16997cd3b19e22", - "aeec9f77b092fb1f9c13c7b1cd4914bf6f31a8c3e4e34b97556ee942431f9d1c", - "21069961e8dc3386828e2608f2ece27db08b11be6e2da9ebadbc40675687e1fb", - 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"baseline_result": "Unsat", - "baseline_ms": 268, - "features.num_hard_constraints": 24936 - }, - { - "sha": "505a2e36055c", - "baseline_result": "Sat", - "baseline_ms": 307, - "features.num_hard_constraints": 24937 - }, - { - "sha": "17cc4d349ce3", - "baseline_result": "Sat", - "baseline_ms": 366, - "features.num_hard_constraints": 15243 - }, - { - "sha": "133383a624ef", - "baseline_result": "Unsat", - "baseline_ms": 480, - "features.num_hard_constraints": 24797 - }, - { - "sha": "872cbdb83f73", - "baseline_result": "Sat", - "baseline_ms": 1298, - "features.num_hard_constraints": 59387 - }, - { - "sha": "aeec9f77b092", - "baseline_result": "Sat", - "baseline_ms": 2193, - "features.num_hard_constraints": 112978 - }, - { - "sha": "21069961e8dc", - "baseline_result": "Sat", - "baseline_ms": 2900, - "features.num_hard_constraints": 113006 - }, - { - "sha": "d8d5ffa7d824", - "baseline_result": "Sat", - "baseline_ms": 3350, - "features.num_hard_constraints": 112812 - }, - { - "sha": "42bc61a62698", - 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}, - { - "sha": "5250d459bef2", - "baseline_result": "Sat", - "baseline_ms": 67552, - "features.num_hard_constraints": 524936 - }, - { - "sha": "a7df812c620d", - "baseline_result": "Sat", - "baseline_ms": 93445, - "features.num_hard_constraints": 696365 - }, - { - "sha": "7f7b5c632ef8", - "baseline_result": "Sat", - "baseline_ms": 181205, - "features.num_hard_constraints": 696736 - } - ] -} diff --git a/input/z3-bench/evolve/config.yaml b/input/z3-bench/evolve/config.yaml index bae9f23e2d..18c1266fd2 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -24,7 +24,7 @@ bench: # === Clustering / stage sampling. === clustering: method: kmeans - feature: features.num_hard_constraints + feature: z3_status.elapsed_ms n_clusters: 5 max_baseline_ms: 300000 # 5 min cap spread: quintile From 4837e010dc09c45a74981fae4aea23a6c3f1341d Mon Sep 17 00:00:00 2001 From: hdson Date: Mon, 1 Jun 2026 14:49:28 +0900 Subject: [PATCH 41/42] [skill] add openevolve-pipeline scaffold generator Per-bench pipeline generator skill at .claude/skills/openevolve-pipeline/. Drives the ADD_NEW_SOLVER.md contract: interview user, scaffold 4 per-bench files + N phase modules under input//evolve/, verify with _lib CLIs (sampler / self_test / rebaseline / run_phase.sh). Templates cover both speedup (z3-style flat overrides) and cost+worker-axis (cpsat-style SIZE_BUCKETS) phase shapes, plus Python-binding and CLI-binary worker variants. References split into interview / decision-guide / verify / gotchas to keep SKILL.md short. Unignore .claude/skills/ so the skill ships with the repo. Co-Authored-By: Claude Opus 4.7 (1M context) --- .claude/skills/openevolve-pipeline/SKILL.md | 123 ++++++++++++++++++ .../references/decision-guide.md | 58 +++++++++ .../openevolve-pipeline/references/gotchas.md | 89 +++++++++++++ .../references/interview-checklist.md | 71 ++++++++++ .../references/verify-checklist.md | 90 +++++++++++++ .../templates/_solve_worker_cli.tmpl | 98 ++++++++++++++ .../templates/_solve_worker_py.tmpl | 104 +++++++++++++++ .../templates/adapter.py.tmpl | 36 +++++ .../templates/config.yaml.tmpl | 80 ++++++++++++ .../templates/initial_program_cpsat.py.tmpl | 84 ++++++++++++ .../templates/initial_program_simple.py.tmpl | 51 ++++++++ .../templates/initial_program_unified.py.tmpl | 53 ++++++++ .../templates/params.json.tmpl | 55 ++++++++ .gitignore | 3 +- 14 files changed, 994 insertions(+), 1 deletion(-) create mode 100644 .claude/skills/openevolve-pipeline/SKILL.md create mode 100644 .claude/skills/openevolve-pipeline/references/decision-guide.md create mode 100644 .claude/skills/openevolve-pipeline/references/gotchas.md create mode 100644 .claude/skills/openevolve-pipeline/references/interview-checklist.md create mode 100644 .claude/skills/openevolve-pipeline/references/verify-checklist.md create mode 100644 .claude/skills/openevolve-pipeline/templates/_solve_worker_cli.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/adapter.py.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/config.yaml.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/initial_program_cpsat.py.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/initial_program_simple.py.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/initial_program_unified.py.tmpl create mode 100644 .claude/skills/openevolve-pipeline/templates/params.json.tmpl diff --git a/.claude/skills/openevolve-pipeline/SKILL.md b/.claude/skills/openevolve-pipeline/SKILL.md new file mode 100644 index 0000000000..3b5d0cf403 --- /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`). + +## 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..1fb8609f4f --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/decision-guide.md @@ -0,0 +1,58 @@ +# 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. | + +## Existing solver reference + +| 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) | + +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..e8e1d629ce --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/gotchas.md @@ -0,0 +1,89 @@ +# 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. 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..9716793a5c --- /dev/null +++ b/.claude/skills/openevolve-pipeline/references/interview-checklist.md @@ -0,0 +1,71 @@ +# 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. + +### 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..915b3906ed --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl @@ -0,0 +1,104 @@ +""" +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 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 + + problem_path = sys.argv[2] + timeout_s = int(sys.argv[3]) + + 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..51d741f5f2 --- /dev/null +++ b/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl @@ -0,0 +1,80 @@ +# 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 + + # Clustering — consumed by _lib.sampler. + 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 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: 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 6abec3a4ac..4c82474783 100644 --- a/.gitignore +++ b/.gitignore @@ -51,7 +51,8 @@ htmlcov/ # Misc .DS_Store .venv -.claude +.claude/* +!.claude/skills/ # For SR secrets.yaml From 5ecc16470904f934ac0b6c774e3f4e61cbe00897 Mon Sep 17 00:00:00 2001 From: hdson Date: Tue, 9 Jun 2026 10:16:11 +0000 Subject: [PATCH 42/42] [z3-bench] add reboot pipeline, sat-mode caches, and shared _lib updates - z3-bench: build_problems_reboot.py, sat/optimize config examples, phase4_unified update, regenerated shared + cache-sat baselines, and a 20260519 backup snapshot of the prior phase layout - _lib: sampler/scorer/evaluator_core/finalize/bench_paths refinements - openevolve-pipeline skill + claude_code llm backend updates - gitignore: ignore input/z3-bench/reboot-raw-data Co-Authored-By: Claude Opus 4.8 (1M context) --- .claude/skills/openevolve-pipeline/SKILL.md | 2 +- .../references/decision-guide.md | 40 +- .../openevolve-pipeline/references/gotchas.md | 18 +- .../references/interview-checklist.md | 10 + .../templates/_solve_worker_py.tmpl | 21 + .../templates/config.yaml.tmpl | 23 + .gitignore | 1 + configs/claude_code_example.yaml | 14 +- input/_lib/bench_paths.py | 30 +- input/_lib/evaluator_core.py | 241 +- input/_lib/finalize.py | 42 +- input/_lib/sampler.py | 115 +- input/_lib/scorer.py | 6 +- input/cpsat-bench/evolve/config.yaml | 6 +- input/z3-bench/build_problems_reboot.py | 122 + input/z3-bench/evolve/_solve_worker.py | 126 +- input/z3-bench/evolve/adapter.py | 23 +- .../phase1_opt_sls/initial_program.py | 70 + .../20260519/phase2_sat/initial_program.py | 177 + .../20260519/phase3_smt/initial_program.py | 164 + .../phase4_unified/initial_program.py | 275 ++ .../20260519/shared/_z3_solve_worker.py | 154 + .../backup/20260519/shared/baseline_params.py | 161 + .../backup/20260519/shared/evaluator.py | 443 +++ .../20260519/shared/local_baseline.json | 2857 +++++++++++++++++ .../backup/20260519/shared/phase1_best.json | 33 + .../backup/20260519/shared/phase2_best.json | 118 + .../backup/20260519/shared/phase3_best.json | 96 + .../evolve/backup/20260519/shared/runtime.py | 66 + .../evolve/backup/20260519/shared/score.py | 103 + .../backup/20260519/shared/stage1_sample.json | 88 + .../backup/20260519/shared/stage2_sample.json | 88 + .../backup/20260519/shared/stage3_sample.json | 88 + .../backup/20260519/shared/stage4_sample.json | 328 ++ .../backup/20260519/shared/z3_runner.py | 97 + .../backup/20260519/shared/z3_valid_keys.json | 1266 ++++++++ .../evolve/cache-sat/local_baseline.json | 297 ++ .../evolve/cache-sat/phase1_best.json | 5 + .../evolve/cache-sat/stage1_sample.json | 7 + .../evolve/cache-sat/stage2_sample.json | 7 + .../evolve/cache-sat/stage3_sample.json | 51 + .../evolve/cache-sat/stage4_sample.json | 7 + .../evolve/config.optimize.example.yaml | 205 ++ input/z3-bench/evolve/config.sat.example.yaml | 206 ++ input/z3-bench/evolve/config.yaml | 74 +- input/z3-bench/evolve/final_verify.json | 26 + .../evolve/phase4_unified/initial_program.py | 14 +- .../evolve/shared/local_baseline.json | 2857 +++++++++++++++++ input/z3-bench/evolve/shared/phase1_best.json | 4 + input/z3-bench/evolve/shared/phase2_best.json | 118 + input/z3-bench/evolve/shared/phase3_best.json | 76 + input/z3-bench/problems.jsonl | 139 +- openevolve/llm/claude_code.py | 83 +- 53 files changed, 11407 insertions(+), 281 deletions(-) create mode 100644 input/z3-bench/build_problems_reboot.py create mode 100644 input/z3-bench/evolve/backup/20260519/phase1_opt_sls/initial_program.py create mode 100644 input/z3-bench/evolve/backup/20260519/phase2_sat/initial_program.py create mode 100644 input/z3-bench/evolve/backup/20260519/phase3_smt/initial_program.py create mode 100644 input/z3-bench/evolve/backup/20260519/phase4_unified/initial_program.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/_z3_solve_worker.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/baseline_params.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/evaluator.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/local_baseline.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/phase1_best.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/phase2_best.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/phase3_best.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/runtime.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/score.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/stage1_sample.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/stage2_sample.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/stage3_sample.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/stage4_sample.json create mode 100644 input/z3-bench/evolve/backup/20260519/shared/z3_runner.py create mode 100644 input/z3-bench/evolve/backup/20260519/shared/z3_valid_keys.json create mode 100644 input/z3-bench/evolve/cache-sat/local_baseline.json create mode 100644 input/z3-bench/evolve/cache-sat/phase1_best.json create mode 100644 input/z3-bench/evolve/cache-sat/stage1_sample.json create mode 100644 input/z3-bench/evolve/cache-sat/stage2_sample.json create mode 100644 input/z3-bench/evolve/cache-sat/stage3_sample.json create mode 100644 input/z3-bench/evolve/cache-sat/stage4_sample.json create mode 100644 input/z3-bench/evolve/config.optimize.example.yaml create mode 100644 input/z3-bench/evolve/config.sat.example.yaml create mode 100644 input/z3-bench/evolve/final_verify.json create mode 100644 input/z3-bench/evolve/shared/local_baseline.json create mode 100644 input/z3-bench/evolve/shared/phase1_best.json create mode 100644 input/z3-bench/evolve/shared/phase2_best.json create mode 100644 input/z3-bench/evolve/shared/phase3_best.json diff --git a/.claude/skills/openevolve-pipeline/SKILL.md b/.claude/skills/openevolve-pipeline/SKILL.md index 3b5d0cf403..78abf8bae4 100644 --- a/.claude/skills/openevolve-pipeline/SKILL.md +++ b/.claude/skills/openevolve-pipeline/SKILL.md @@ -108,7 +108,7 @@ 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`). +- `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 diff --git a/.claude/skills/openevolve-pipeline/references/decision-guide.md b/.claude/skills/openevolve-pipeline/references/decision-guide.md index 1fb8609f4f..0288d82252 100644 --- a/.claude/skills/openevolve-pipeline/references/decision-guide.md +++ b/.claude/skills/openevolve-pipeline/references/decision-guide.md @@ -48,11 +48,49 @@ across phases. z3-bench is single-threaded. | `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`) | speedup | NO | NO | 4 (opt_sls + sat + smt + unified) | +| 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 index e8e1d629ce..4fe6ebd2f2 100644 --- a/.claude/skills/openevolve-pipeline/references/gotchas.md +++ b/.claude/skills/openevolve-pipeline/references/gotchas.md @@ -80,7 +80,23 @@ If baseline was captured single-threaded, running candidates with vs baseline → false regression. Either pin core ranges via `--pin` or recapture baseline at the same parallelism. -### v4. Catalog `defaults` ≠ binding's real defaults +### 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` diff --git a/.claude/skills/openevolve-pipeline/references/interview-checklist.md b/.claude/skills/openevolve-pipeline/references/interview-checklist.md index 9716793a5c..c85df6e34b 100644 --- a/.claude/skills/openevolve-pipeline/references/interview-checklist.md +++ b/.claude/skills/openevolve-pipeline/references/interview-checklist.md @@ -57,6 +57,16 @@ and confirm with user: - `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). diff --git a/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl b/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl index 915b3906ed..2ab618a78b 100644 --- a/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl +++ b/.claude/skills/openevolve-pipeline/templates/_solve_worker_py.tmpl @@ -18,6 +18,7 @@ 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 @@ -27,6 +28,21 @@ def emit(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"}) @@ -41,6 +57,11 @@ def main(): 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: diff --git a/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl b/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl index 51d741f5f2..553810e59e 100644 --- a/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl +++ b/.claude/skills/openevolve-pipeline/templates/config.yaml.tmpl @@ -23,8 +23,22 @@ bench: 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 @@ -36,6 +50,15 @@ bench: 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: diff --git a/.gitignore b/.gitignore index 4c82474783..c8027d596b 100644 --- a/.gitignore +++ b/.gitignore @@ -58,6 +58,7 @@ htmlcov/ 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 / diff --git a/configs/claude_code_example.yaml b/configs/claude_code_example.yaml index e9c2247bc6..9b81071fc2 100644 --- a/configs/claude_code_example.yaml +++ b/configs/claude_code_example.yaml @@ -22,12 +22,16 @@ llm: provider: "claude_code" weight: 0.7 system_message: "You are an expert code evolution assistant." - timeout: 180 + timeout: 300 retries: 2 retry_delay: 10 # Optional Claude Code-specific knobs: reasoning_effort: "medium" # one of: low, medium, high, xhigh, max - max_thinking_tokens: null # cap extended thinking budget + # 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" @@ -35,13 +39,15 @@ llm: - name: "claude-haiku-4-5" provider: "claude_code" weight: 0.3 - timeout: 120 + timeout: 300 + max_thinking_tokens: 4000 evaluator_models: - name: "claude-haiku-4-5" provider: "claude_code" weight: 1.0 - timeout: 120 + timeout: 300 + max_thinking_tokens: 4000 prompt: system_message: "You are an expert code evolution assistant." diff --git a/input/_lib/bench_paths.py b/input/_lib/bench_paths.py index cbb0103f25..c670e18635 100644 --- a/input/_lib/bench_paths.py +++ b/input/_lib/bench_paths.py @@ -5,6 +5,7 @@ either the bench name (e.g. `"cpsat-bench"`) or an already-resolved bench root path. """ + import importlib.util import os import pathlib @@ -27,8 +28,26 @@ def resolve_bench(bench_name_or_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): - return pathlib.Path(bench_root).resolve() / "cache" + 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): @@ -51,7 +70,7 @@ 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") + 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}") @@ -59,13 +78,11 @@ def worker_path(bench_root): def evaluation_cfg(bench_root): - return ((load_config(bench_root).get("bench") or {}) - .get("evaluation") or {}) + 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 {}) + return (load_config(bench_root).get("bench") or {}).get("clustering") or {} _yaml_cache = {} @@ -81,6 +98,7 @@ def load_config(bench_root): _yaml_cache[key] = {} return _yaml_cache[key] import yaml + _yaml_cache[key] = yaml.safe_load(path.read_text()) or {} return _yaml_cache[key] diff --git a/input/_lib/evaluator_core.py b/input/_lib/evaluator_core.py index 2755e7e025..02e16c6e31 100644 --- a/input/_lib/evaluator_core.py +++ b/input/_lib/evaluator_core.py @@ -16,6 +16,7 @@ 10-run averaging is the standard (configurable via `bench.evaluation.repeats`). """ + import importlib.util import inspect import json @@ -33,6 +34,7 @@ try: from openevolve.evaluation_result import EvaluationResult except Exception: + class EvaluationResult: def __init__(self, metrics=None, artifacts=None): self.metrics = metrics or {} @@ -105,6 +107,13 @@ def build_evaluators(bench_root): 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)) @@ -157,8 +166,7 @@ def _load_problems(workers=1): 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 {}) + 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) @@ -170,17 +178,19 @@ def _load_problems(workers=1): 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, - }) + 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): @@ -192,8 +202,12 @@ def _filter_by_sample(problems, stage_n): def _err(metrics_extra, artifacts): m = { - "combined_score": 0.0, "geomean_speedup": 0.0, - "solved_rate": 0.0, "regressions": 0, "solved": 0, "total": 0, + "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) @@ -220,21 +234,28 @@ 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}) + 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}) + 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}) + 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): @@ -243,21 +264,24 @@ def _evaluate(program_path, problems, stage_name): else: locked = dict(catalog_locked) - violations = {k: global_params.get(k) for k in locked - if global_params.get(k) != locked[k]} + 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}) + 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}) + 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"])] @@ -265,23 +289,28 @@ def _evaluate(program_path, problems, stage_name): 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, + "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"}) + 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}) + 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)) + 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) @@ -302,14 +331,21 @@ def _evaluate(program_path, problems, stage_name): 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)) + 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 + 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: @@ -320,9 +356,13 @@ def _solve(idx_p): 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) + 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 @@ -338,37 +378,48 @@ def _fmt(c): 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) + 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) + 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}) + { + "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"]) + 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) + 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: @@ -378,9 +429,12 @@ def _fmt(c): 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}) + { + "invalid_param": abort["invalid_param"], + "stderr": abort.get("stderr", "")[:2000], + "stage": stage_name, + }, + ) results = [] for idx in range(len(problems)): @@ -399,29 +453,38 @@ def _fmt(c): _SMALL_CACHE[(str(program_path), stage_name)] = results metrics = scorer.score( - results, mode=score_mode, - decisive_results=decisive, decided_results=decided, + results, + mode=score_mode, + decisive_results=decisive, + decided_results=decided, stats_weights=stats_weights, - time_metric=time_metric, cost_weight=cost_weight) + 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] + 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": ( diff --git a/input/_lib/finalize.py b/input/_lib/finalize.py index fbe1dd0bfe..bd25c323cc 100644 --- a/input/_lib/finalize.py +++ b/input/_lib/finalize.py @@ -28,6 +28,7 @@ 2. `/openevolve_output/checkpoints/checkpoint_*/best_program.py` (highest combined_score) """ + import argparse import json import pathlib @@ -74,9 +75,7 @@ def repl_dict(m): 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 - ) + 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) @@ -103,8 +102,9 @@ def _pick_from_checkpoints(phase_dir): if not info.exists() or not prog.exists(): continue try: - sc = float(json.loads(info.read_text()) - .get("metrics", {}).get("combined_score", float("-inf"))) + sc = float( + json.loads(info.read_text()).get("metrics", {}).get("combined_score", float("-inf")) + ) except (json.JSONDecodeError, ValueError, TypeError): continue if sc > best_score: @@ -127,8 +127,7 @@ def finalize(bench_root, from_checkpoints=False): 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})") + 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(): @@ -139,8 +138,10 @@ def finalize(bench_root, from_checkpoints=False): 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})") + print( + f"[finalize] best/best_program.py missing — using checkpoint " + f"{ck.name} (combined_score={score:.4f})" + ) src = best_py.read_text() header = ( @@ -159,11 +160,13 @@ def finalize(bench_root, from_checkpoints=False): # 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():] + out_src = resolved_src[: match.end()] + "\n" + header + resolved_src[match.end() :] else: out_src = header + resolved_src - out = bench_root / "final_program.py" + 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) @@ -172,19 +175,22 @@ def finalize(bench_root, from_checkpoints=False): 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_program.py ({out.stat().st_size} bytes)") + 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") + 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) + finalize(bench_paths.resolve_bench(args.bench), from_checkpoints=args.from_checkpoints) if __name__ == "__main__": diff --git a/input/_lib/sampler.py b/input/_lib/sampler.py index 2c5edf5b8b..5a18c24f01 100644 --- a/input/_lib/sampler.py +++ b/input/_lib/sampler.py @@ -29,6 +29,7 @@ The decisive-baseline filter is driven by the adapter (`DECISIVE_RESULTS` + `STATUS_FIELD`). """ + import argparse import json import pathlib @@ -130,7 +131,7 @@ def _pick_spread(pool, n_pick, mode): return list(pool) if mode == "center": start = (len(pool) - n_pick) // 2 - return pool[start:start + n_pick] + return pool[start : start + n_pick] # default: quintile spread within the pool if n_pick == 1: return [pool[len(pool) // 2]] @@ -164,14 +165,14 @@ def _fmt_ms(v): return f"{int(v)}ms" -def _print_stage_report(stage_name, picks, pool, cluster_ids, - feature_path, baseline_ms, baseline_result, fname): +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})") + 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] @@ -191,25 +192,27 @@ def _print_stage_report(stage_name, picks, pool, cluster_ids, 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" {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( + 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}") + print(f" {sha} {res:<10} {_fmt_ms(ms):>8} " f"{feature_path}={feat}") def build_samples(bench_root, *, adapter=None): @@ -221,6 +224,18 @@ def build_samples(bench_root, *, adapter=None): 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) @@ -265,8 +280,7 @@ def _baseline_result(r): 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) + 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] @@ -282,8 +296,7 @@ def _baseline_result(r): elif method == "thresholds": thr = list(cluster_cfg.get("thresholds") or []) if not thr: - raise SystemExit("clustering.method=thresholds requires " - "non-empty `thresholds` list") + raise SystemExit("clustering.method=thresholds requires " "non-empty `thresholds` list") labels = _threshold_labels(feature_vals, thr) n_buckets = len(thr) + 1 else: @@ -300,12 +313,13 @@ def _baseline_result(r): 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) - )) + 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 {} @@ -324,26 +338,39 @@ def _range(b): 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}") + 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" + 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) + _print_stage_report( + stage_name, + picks, + merged, + cluster_ids, + feature_path, + _baseline_ms, + _baseline_result, + sample_path.name, + ) def main(argv=None): diff --git a/input/_lib/scorer.py b/input/_lib/scorer.py index 6256b9ab49..ae4af1b7a8 100644 --- a/input/_lib/scorer.py +++ b/input/_lib/scorer.py @@ -86,7 +86,7 @@ def _score_speedup(per_problem, decided_set): weights.append(w) if match: solved += 1 - speedups.append(p["baseline_ms"] / max(p["elapsed_ms"], 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: @@ -109,7 +109,7 @@ def _time_ratio(p, metric): 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 p["baseline_ms"] / max(p["elapsed_ms"], 1), "wall" + return max(p["baseline_ms"], 1) / max(p["elapsed_ms"], 1), "wall" def _score_cost(per_problem, decisive_set, time_metric, cost_weight): @@ -132,7 +132,7 @@ def _score_cost(per_problem, decisive_set, time_metric, cost_weight): b_cost = p.get("baseline_objective") v_cost = p.get("objective") time_r, src = _time_ratio(p, time_metric) - wall_r = p["baseline_ms"] / max(p["elapsed_ms"], 1) + wall_r = max(p["baseline_ms"], 1) / max(p["elapsed_ms"], 1) if time_metric == "dtime": if src == "dtime": dtime_used += 1 diff --git a/input/cpsat-bench/evolve/config.yaml b/input/cpsat-bench/evolve/config.yaml index cc7f69f13f..08359523bd 100644 --- a/input/cpsat-bench/evolve/config.yaml +++ b/input/cpsat-bench/evolve/config.yaml @@ -90,14 +90,16 @@ llm: - name: "claude-sonnet-4-6" provider: "claude_code" weight: 0.8 - timeout: 180 + 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: 120 + timeout: 300 + max_thinking_tokens: 4000 prompt: system_message: | 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/_solve_worker.py b/input/z3-bench/evolve/_solve_worker.py index c618f65fd9..2db8c2c8f2 100644 --- a/input/z3-bench/evolve/_solve_worker.py +++ b/input/z3-bench/evolve/_solve_worker.py @@ -4,6 +4,13 @@ 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: @@ -21,8 +28,10 @@ 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 @@ -32,6 +41,20 @@ def emit(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"}) @@ -46,6 +69,8 @@ def main(): smt2_path = sys.argv[2] timeout_s = int(sys.argv[3]) + mode = _solver_mode() + try: import z3 except ImportError as e: @@ -69,7 +94,10 @@ def main(): opt_local = {} for k, v in params.items(): if k.startswith("opt."): - opt_local[k[len("opt."):]] = v + # 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) @@ -77,7 +105,14 @@ def main(): 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}) + 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 @@ -87,23 +122,48 @@ def main(): except Exception: pass - o = z3.Optimize() - - for k, v in opt_local.items(): + 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.set(k, v) - except z3.Z3Exception as e: - emit({"invalid_param": "opt." + k, "error": str(e), "result": "Unknown", "elapsed_ms": 0}) - return + o.add(z3.parse_smt2_file(smt2_path)) except Exception as e: - emit({"invalid_param": "opt." + k, "error": f"{type(e).__name__}: {e}", "result": "Unknown", "elapsed_ms": 0}) + emit({"result": "Unknown", "elapsed_ms": 0, "error": f"smt2 parse: {e}"}) return + else: + o = z3.Optimize() - try: - o.from_file(smt2_path) - except Exception as e: - emit({"result": "Unknown", "elapsed_ms": 0, "error": f"smt2 parse: {e}"}) - return + 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: @@ -142,7 +202,41 @@ def main(): except Exception: stats = {} - emit({"result": label, "elapsed_ms": elapsed_ms, "stats": 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__": diff --git a/input/z3-bench/evolve/adapter.py b/input/z3-bench/evolve/adapter.py index da9aa9841e..87c72aeec4 100644 --- a/input/z3-bench/evolve/adapter.py +++ b/input/z3-bench/evolve/adapter.py @@ -6,15 +6,19 @@ SOLVER_NAME = "z3" PROBLEM_FILE_FIELD = "smt2_filename" -STATUS_FIELD = "z3_status" # {"result", "elapsed_ms"} -STATS_FIELD = None # baseline has no separate stats block +STATUS_FIELD = "z3_status" # {"result", "elapsed_ms", "objective_value"} +STATS_FIELD = None # baseline carries no separate stats block FEATURES_FIELD = "features" -OBJECTIVE_FIELD = None # SMT2/MaxSMT: no objective in baseline +# 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", "mk clause") +KEY_STATS = ("decisions", "propagations", "conflicts", "rlimit count") STATS_WEIGHTS = { "conflicts": 2.0, @@ -22,12 +26,17 @@ "propagations": 0.5, } -SCORE_MODE = "speedup" +# 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): - """Z3 problems carry num_hard_constraints as the dominant size signal - (mean ~106k hard / ~2k soft / ~33k vars across the workload).""" + """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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"arith-bound-propagations-lp": 31748, + "arith-diseq": 2085, + "arith-make-feasible": 2551, + "arith-max-columns": 7130, + "arith-max-rows": 4558, + "arith-offset-eqs": 11633, + "solve-eqs-steps": 247089, + "solve-eqs-elim-vars": 75122, + "rlimit count": 73674728, + "max memory": 4737.55, + "memory": 4497.48, + "num allocs": 131374027636.0, + "time": 43.162 + } + } +} diff --git a/input/z3-bench/evolve/backup/20260519/shared/phase1_best.json b/input/z3-bench/evolve/backup/20260519/shared/phase1_best.json new file mode 100644 index 0000000000..4f11cfd13a --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/phase1_best.json @@ -0,0 +1,33 @@ +{ + "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, + "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 +} diff --git a/input/z3-bench/evolve/backup/20260519/shared/phase2_best.json b/input/z3-bench/evolve/backup/20260519/shared/phase2_best.json new file mode 100644 index 0000000000..1d0418caa4 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/phase2_best.json @@ -0,0 +1,118 @@ +{ + "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 +} diff --git a/input/z3-bench/evolve/backup/20260519/shared/phase3_best.json b/input/z3-bench/evolve/backup/20260519/shared/phase3_best.json new file mode 100644 index 0000000000..30689a49a2 --- /dev/null +++ b/input/z3-bench/evolve/backup/20260519/shared/phase3_best.json @@ -0,0 +1,96 @@ +{ + "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 +} diff --git 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 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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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14304157, + "conflicts": 109, + "pb conflicts": 11, + "arith-diseq": 10383, + "pb resolves": 11, + "propagations": 463729, + "solve-eqs-elim-vars": 36, + "arith-fixed-eqs": 5238 + } + } + } + } +} diff --git a/input/z3-bench/evolve/cache-sat/phase1_best.json b/input/z3-bench/evolve/cache-sat/phase1_best.json new file mode 100644 index 0000000000..bda3ca8b7f --- /dev/null +++ b/input/z3-bench/evolve/cache-sat/phase1_best.json @@ -0,0 +1,5 @@ +{ + "opt.maxsat_engine": "pd-maxres", + "opt.optsmt_engine": "basic", + "opt.priority": "box" +} diff --git a/input/z3-bench/evolve/cache-sat/stage1_sample.json b/input/z3-bench/evolve/cache-sat/stage1_sample.json new file mode 100644 index 0000000000..3e893ee6c4 --- /dev/null +++ b/input/z3-bench/evolve/cache-sat/stage1_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/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 index 18c1266fd2..aea99f3c87 100644 --- a/input/z3-bench/evolve/config.yaml +++ b/input/z3-bench/evolve/config.yaml @@ -21,12 +21,28 @@ bench: 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 - feature: z3_status.elapsed_ms + # 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 + max_baseline_ms: 300000 # 5 min cap (placeholder elapsed_ms << this) spread: quintile stage_sizes: stage1: 5 @@ -39,12 +55,35 @@ bench: 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: 10 + repeats: 5 timeout_factor: 1.3 min_timeout_s: 5 - score_mode: speedup # z3: wall-clock speedup + 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 @@ -57,7 +96,7 @@ bench: # - Total RAM ≈ N × 4 GB worst-case — size per host. parallel_solvers: 1 -max_iterations: 20 +max_iterations: 50 checkpoint_interval: 10 log_level: "INFO" random_seed: 42 @@ -74,23 +113,34 @@ llm: - name: "claude-sonnet-4-6" provider: "claude_code" weight: 0.8 - timeout: 180 + 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" + 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: 120 + timeout: 300 + max_thinking_tokens: 4000 prompt: system_message: | - You tune Z3 SMT solver parameters for a MaxSMT/optimization workload (50 SMT2 instances - with ~13k Int / 19k Bool / 40 Real vars, ~2k soft + 105k hard constraints, opt.* engaged). + 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). Your job is to mutate this dict to MINIMIZE wall-clock time across the - benchmark while PRESERVING correctness (Sat/Unsat must match baseline). + 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 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/phase4_unified/initial_program.py b/input/z3-bench/evolve/phase4_unified/initial_program.py index 873c63080f..454711c59b 100644 --- a/input/z3-bench/evolve/phase4_unified/initial_program.py +++ b/input/z3-bench/evolve/phase4_unified/initial_program.py @@ -36,7 +36,19 @@ def _resolve_bench_root(): # EVOLVE-BLOCK-START -UNIFIED_OVERRIDES = {} +# 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 diff --git a/input/z3-bench/evolve/shared/local_baseline.json 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"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/llm/claude_code.py b/openevolve/llm/claude_code.py index 391ec891c6..31c2f325c5 100644 --- a/openevolve/llm/claude_code.py +++ b/openevolve/llm/claude_code.py @@ -78,13 +78,16 @@ async def generate_with_context( return await asyncio.wait_for(coro, timeout=timeout) return await coro except asyncio.TimeoutError: - if attempt < retries: - logger.warning( - f"[claude_code] Timeout {attempt + 1}/{retries + 1}, retrying." - ) - await asyncio.sleep(retry_delay) - continue - logger.error(f"[claude_code] All {retries + 1} attempts timed out") + # 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: @@ -121,8 +124,6 @@ async def _query_once( opts_kwargs["system_prompt"] = system_message if self.model: opts_kwargs["model"] = self.model - if self.max_thinking_tokens is not None: - opts_kwargs["max_thinking_tokens"] = self.max_thinking_tokens effort = kwargs.get("reasoning_effort", self.reasoning_effort) if effort is not None: opts_kwargs["effort"] = effort @@ -130,13 +131,30 @@ async def _query_once( opts_kwargs["cli_path"] = self.cli_path if self.cwd is not None: opts_kwargs["cwd"] = self.cwd - # When reasoning is requested (effort or max_thinking_tokens), default - # to adaptive thinking with `display=summarized` so ThinkingBlock - # actually carries text — Opus 4.7+ defaults display to "omitted" - # (signature-only), which would zero-out our DEBUG reasoning log. - # User can still override via claude_code_options.thinking. - if (effort is not None or self.max_thinking_tokens is not None) \ - and "thinking" not in self.extra_sdk_options: + + # 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) @@ -147,22 +165,35 @@ async def _query_once( f"prompt_chars={len(prompt_text)} " f"sys_chars={len(system_message) if system_message else 0} " f"effort={opts_kwargs.get('effort')} " - f"max_thinking_tokens={opts_kwargs.get('max_thinking_tokens')}" + f"thinking={opts_kwargs.get('thinking')}" ) text_chunks: List[str] = [] thinking_chunks: List[str] = [] result_text: Optional[str] = None - 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 + 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)

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