26b8876691
Replace per-candidate Anvil+forge-script pipeline with in-process EVM
execution using Foundry's native revm backend, achieving 10-100× speedup
for evolutionary search at scale.
New files:
- onchain/test/FitnessEvaluator.t.sol — Forge test that forks Base once,
deploys the full KRAIKEN stack, then for each candidate uses vm.etch to
inject the compiled optimizer bytecode, UUPS-upgrades the proxy, runs all
attack sequences with in-memory vm.snapshot/revertTo (no RPC overhead),
and emits one {"candidate_id","fitness"} JSON line per candidate.
Skips gracefully when BASE_RPC_URL is unset (CI-safe).
- tools/push3-evolution/revm-evaluator/batch-eval.sh — Wrapper that
transpiles+compiles each candidate sequentially, writes a two-file
manifest (ids.txt + bytecodes.txt), then invokes FitnessEvaluator.t.sol
in a single forge test run and parses the score JSON from stdout.
Modified:
- tools/push3-evolution/evolve.sh — Adds EVAL_MODE env var (anvil|revm).
When EVAL_MODE=revm, batch-scores every candidate in a generation with
one batch-eval.sh call instead of N sequential fitness.sh processes;
scores are looked up from the JSONL output in the per-candidate loop.
Default remains EVAL_MODE=anvil for backward compatibility.
Key design decisions:
- Per-candidate Solidity compilation is unavoidable (each Push3 candidate
produces different Solidity); the speedup is in the evaluation phase.
- vm.snapshot/revertTo in forge test are O(1) memory operations (true
revm), not RPC calls — this is the core speedup vs Anvil.
- recenterAccess is set in bootstrap so TWAP stability checks are bypassed
during attack sequences (mirrors the existing fitness.sh bootstrap).
- Test skips cleanly when BASE_RPC_URL is absent, keeping CI green.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
512 lines
17 KiB
Bash
Executable file
512 lines
17 KiB
Bash
Executable file
#!/usr/bin/env bash
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# =============================================================================
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# evolve.sh — Push3 evolution orchestrator
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#
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# Outer evolutionary loop: generate candidates → score → select → repeat.
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#
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# Usage:
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# ./tools/push3-evolution/evolve.sh \
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# --seed optimizer_v3.push3 \
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# --population 10 \
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# --generations 5 \
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# --mutation-rate 2 \
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# --output evolved/
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#
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# Algorithm:
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# 1. Initialize population: N copies of seed, each with M random mutations.
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# 2. For each generation:
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# a. Score all candidates via fitness.sh
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# b. Log generation stats (min/max/mean fitness, best candidate)
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# c. Select k survivors via tournament selection (k = population/2)
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# d. Generate next population: mutate survivors + crossover pairs
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# 3. Output best candidate as Push3 file.
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# 4. Show diff: original vs evolved (which constants changed, by how much).
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#
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# Output:
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# <output>/
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# generation_0.jsonl {candidate_id, fitness, mutations_applied}
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# generation_1.jsonl
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# ...
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# best.push3 highest-fitness program
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# diff.txt parameter changes vs original
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# evolution.log full run log
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#
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# Environment:
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# ANVIL_FORK_URL Passed through to fitness.sh when Anvil is not running.
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#
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# TSX resolution order: tsx in PATH → node_modules/.bin/tsx → npx tsx.
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# =============================================================================
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set -euo pipefail
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# Foundry tools (forge, cast, anvil)
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export PATH="${HOME}/.foundry/bin:${PATH}"
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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FITNESS_SH="$SCRIPT_DIR/fitness.sh"
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BATCH_EVAL_SH="$SCRIPT_DIR/revm-evaluator/batch-eval.sh"
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MUTATE_CLI="$SCRIPT_DIR/mutate-cli.ts"
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# EVAL_MODE controls which fitness backend is used:
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# anvil (default) — per-candidate Anvil+forge-script pipeline (fitness.sh)
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# revm — in-process revm via FitnessEvaluator.t.sol (batch-eval.sh)
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# Requires BASE_RPC_URL env var. 10-100× faster at scale.
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EVAL_MODE="${EVAL_MODE:-anvil}"
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# =============================================================================
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# Argument parsing
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# =============================================================================
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SEED=""
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POPULATION=10
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GENERATIONS=5
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MUTATION_RATE=2
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OUTPUT_DIR=""
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while [[ $# -gt 0 ]]; do
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case $1 in
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--seed) SEED="$2"; shift 2 ;;
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--population) POPULATION="$2"; shift 2 ;;
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--generations) GENERATIONS="$2"; shift 2 ;;
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--mutation-rate) MUTATION_RATE="$2"; shift 2 ;;
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--output) OUTPUT_DIR="$2"; shift 2 ;;
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*) echo "Unknown option: $1" >&2; exit 2 ;;
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esac
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done
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if [ -z "$SEED" ]; then echo "Error: --seed required" >&2; exit 2; fi
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if [ -z "$OUTPUT_DIR" ]; then echo "Error: --output required" >&2; exit 2; fi
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if [ ! -f "$SEED" ]; then echo "Error: seed file not found: $SEED" >&2; exit 2; fi
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# Validate numeric args
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for _name_val in "population:$POPULATION" "generations:$GENERATIONS" "mutation-rate:$MUTATION_RATE"; do
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_name="${_name_val%%:*}"
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_val="${_name_val##*:}"
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if ! [[ "$_val" =~ ^[0-9]+$ ]] || [ "$_val" -lt 1 ]; then
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echo "Error: --${_name} must be a positive integer (got: $_val)" >&2
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exit 2
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fi
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done
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# Canonicalize paths
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SEED="$(cd "$(dirname "$SEED")" && pwd)/$(basename "$SEED")"
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mkdir -p "$OUTPUT_DIR"
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OUTPUT_DIR="$(cd "$OUTPUT_DIR" && pwd)"
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LOG="$OUTPUT_DIR/evolution.log"
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# =============================================================================
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# Helpers
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# =============================================================================
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log() {
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local msg="[evolve] $*"
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echo "$msg" >&2
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echo "$msg" >> "$LOG"
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}
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fail() {
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log "ERROR: $*"
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exit 2
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}
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# Locate a tsx runner (TypeScript executor for mutate-cli.ts).
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# Tries: tsx in PATH → local node_modules → npx tsx.
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find_tsx_cmd() {
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if command -v tsx &>/dev/null; then
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echo "tsx"
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elif [ -x "$SCRIPT_DIR/node_modules/.bin/tsx" ]; then
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echo "$SCRIPT_DIR/node_modules/.bin/tsx"
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elif command -v npx &>/dev/null; then
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echo "npx tsx"
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else
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return 1
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fi
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}
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# Run the mutate-cli.ts with the given arguments.
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# All mutation operations run from SCRIPT_DIR so relative TS imports resolve.
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run_mutate_cli() {
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(cd "$SCRIPT_DIR" && $TSX_CMD "$MUTATE_CLI" "$@")
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}
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# Integer min/max/mean via python3 (bash arithmetic overflows on wei values).
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py_stats() {
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# Args: space-separated integers on stdin as a Python list literal
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python3 - "$@" <<'PYEOF'
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import sys
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nums = [int(x) for x in sys.stdin.read().split()]
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if not nums:
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print("0 0 0")
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sys.exit(0)
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print(min(nums), max(nums), round(sum(nums) / len(nums)))
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PYEOF
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}
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# Tournament selection: given a scores file (one "idx score filepath" per line),
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# run k tournaments of size 2 and return winner filepaths (one per line).
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py_tournament() {
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local k="$1"
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local scores_file="$2"
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python3 - "$k" "$scores_file" <<'PYEOF'
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import sys, random
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k = int(sys.argv[1])
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entries = []
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with open(sys.argv[2]) as f:
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for line in f:
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parts = line.rstrip('\n').split('\t')
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if len(parts) >= 3:
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entries.append((int(parts[0]), int(parts[1]), parts[2]))
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if not entries:
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sys.exit(1)
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for _ in range(k):
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a = random.choice(entries)
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b = random.choice(entries)
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winner = a if a[1] >= b[1] else b
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print(winner[2])
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PYEOF
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}
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# =============================================================================
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# Tool checks
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# =============================================================================
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for _tool in python3 node; do
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command -v "$_tool" &>/dev/null || fail "$_tool not found in PATH"
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done
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[ -f "$FITNESS_SH" ] || fail "fitness.sh not found at $FITNESS_SH"
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[ -f "$MUTATE_CLI" ] || fail "mutate-cli.ts not found at $MUTATE_CLI"
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[ -x "$FITNESS_SH" ] || chmod +x "$FITNESS_SH"
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if [ "$EVAL_MODE" = "revm" ]; then
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[ -f "$BATCH_EVAL_SH" ] || fail "batch-eval.sh not found at $BATCH_EVAL_SH"
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[ -x "$BATCH_EVAL_SH" ] || chmod +x "$BATCH_EVAL_SH"
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[ -n "${BASE_RPC_URL:-}" ] || fail "EVAL_MODE=revm requires BASE_RPC_URL env var (Base network RPC)"
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command -v forge &>/dev/null || fail "forge not found in PATH (required for EVAL_MODE=revm)"
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elif [ "$EVAL_MODE" != "anvil" ]; then
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fail "Unknown EVAL_MODE '$EVAL_MODE' — must be 'anvil' or 'revm'"
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fi
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TSX_CMD="$(find_tsx_cmd)" || fail \
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"No TypeScript runner found. Install tsx (npm install -g tsx) or ensure npx is in PATH."
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# =============================================================================
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# Work directory — holds all candidate .push3 files across generations
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# =============================================================================
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WORK_DIR="$(mktemp -d)"
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cleanup() { rm -rf "$WORK_DIR"; }
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trap cleanup EXIT
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# =============================================================================
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# Log run header
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# =============================================================================
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log "========================================================"
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log "Push3 Evolution — $(date -u '+%Y-%m-%dT%H:%M:%SZ')"
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log " Seed: $SEED"
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log " Population: $POPULATION"
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log " Generations: $GENERATIONS"
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log " Mutation rate: $MUTATION_RATE"
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log " Output: $OUTPUT_DIR"
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log " TSX: $TSX_CMD"
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log " Eval mode: $EVAL_MODE"
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log "========================================================"
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# =============================================================================
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# Step 1 — Initialize generation 0
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#
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# N copies of the seed, each independently mutated MUTATION_RATE times.
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# =============================================================================
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log ""
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log "=== Initializing population ==="
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GEN_DIR="$WORK_DIR/gen_0"
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mkdir -p "$GEN_DIR"
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for i in $(seq 0 $((POPULATION - 1))); do
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CAND_FILE="$GEN_DIR/candidate_$(printf '%03d' $i).push3"
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MUTATED=$(run_mutate_cli mutate "$SEED" "$MUTATION_RATE") \
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|| fail "Failed to mutate seed for initial candidate $i"
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printf '%s\n' "$MUTATED" > "$CAND_FILE"
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printf '%d\n' "$MUTATION_RATE" > "${CAND_FILE%.push3}.ops"
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done
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log "Initialized ${POPULATION} candidates in gen_0"
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# =============================================================================
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# Step 2 — Evolution loop
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# =============================================================================
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GLOBAL_BEST_FITNESS=-1
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GLOBAL_BEST_GEN=-1
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GLOBAL_BEST_CAND=""
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CURRENT_GEN_DIR="$GEN_DIR"
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for gen in $(seq 0 $((GENERATIONS - 1))); do
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log ""
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log "=== Generation $((gen + 1)) / $GENERATIONS ==="
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JSONL_FILE="$OUTPUT_DIR/generation_${gen}.jsonl"
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SCORES_FILE="$WORK_DIR/scores_gen_${gen}.txt"
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# --- a. Score all candidates ---
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SCORE_VALUES=""
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CAND_COUNT=0
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# In revm mode, batch-score all candidates in one forge test invocation before
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# the per-candidate loop. Scores are written to a temp JSONL file that the
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# loop reads with a fast Python lookup.
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BATCH_SCORES_FILE="$WORK_DIR/batch_scores_gen_${gen}.jsonl"
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if [ "$EVAL_MODE" = "revm" ]; then
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declare -a _BATCH_FILES=()
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for _CF in "$CURRENT_GEN_DIR"/candidate_*.push3; do
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[ -f "$_CF" ] && _BATCH_FILES+=("$_CF")
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done
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if [ "${#_BATCH_FILES[@]}" -gt 0 ]; then
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BATCH_EC=0
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bash "$BATCH_EVAL_SH" "${_BATCH_FILES[@]}" > "$BATCH_SCORES_FILE" 2>/dev/null \
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|| BATCH_EC=$?
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if [ "$BATCH_EC" -eq 2 ]; then
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fail "batch-eval.sh reported an infrastructure error (exit 2) — aborting evolution"
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fi
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log " revm batch scoring complete (exit $BATCH_EC)"
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fi
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fi
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for CAND_FILE in "$CURRENT_GEN_DIR"/candidate_*.push3; do
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[ -f "$CAND_FILE" ] || continue
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CAND_IDX="${CAND_FILE##*candidate_}"
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CAND_IDX="${CAND_IDX%.push3}"
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CID="gen${gen}_c${CAND_IDX}"
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# Read mutations_applied from sidecar; default 0 if missing.
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OPS_FILE="${CAND_FILE%.push3}.ops"
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MUTATIONS_APPLIED=0
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[ -f "$OPS_FILE" ] && MUTATIONS_APPLIED=$(cat "$OPS_FILE")
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SCORE=0
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FITNESS_EC=0
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if [ "$EVAL_MODE" = "revm" ] && [ -f "$BATCH_SCORES_FILE" ]; then
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# Look up pre-computed score from batch-eval.sh output.
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SCORE=$(python3 - "$CID" "$BATCH_SCORES_FILE" <<'PYEOF'
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import json, sys
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cid = sys.argv[1]
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with open(sys.argv[2]) as f:
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for line in f:
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try:
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d = json.loads(line)
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if d.get("candidate_id") == cid:
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print(d["fitness"])
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sys.exit(0)
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except (json.JSONDecodeError, KeyError):
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pass
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print(0)
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PYEOF
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) || SCORE=0
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else
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# Anvil mode (or revm fallback): score candidate individually.
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SCORE=$(bash "$FITNESS_SH" "$CAND_FILE" 2>/dev/null) || FITNESS_EC=$?
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# Exit 2 = infrastructure error (Anvil down, missing tools): abort immediately.
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if [ "$FITNESS_EC" -eq 2 ]; then
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fail "fitness.sh reported an infrastructure error (exit 2) — aborting evolution"
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fi
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fi
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# Validate that score is a non-negative integer; treat any other output as invalid.
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if ! [[ "$SCORE" =~ ^[0-9]+$ ]]; then
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log " $CID: invalid/missing score, using 0"
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SCORE=0
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else
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log " $CID: fitness=$SCORE"
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fi
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# Append to JSONL — use the actual operations recorded for this candidate.
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printf '{"candidate_id":"%s","fitness":%s,"mutations_applied":%d}\n' \
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"$CID" "$SCORE" "$MUTATIONS_APPLIED" >> "$JSONL_FILE"
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# Record index, score, and filepath for selection (tab-delimited so paths with spaces are safe).
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printf '%d\t%s\t%s\n' "$CAND_COUNT" "$SCORE" "$CAND_FILE" >> "$SCORES_FILE"
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SCORE_VALUES="$SCORE_VALUES $SCORE"
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CAND_COUNT=$((CAND_COUNT + 1))
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done
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if [ "$CAND_COUNT" -eq 0 ]; then
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fail "No candidates found in $CURRENT_GEN_DIR"
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fi
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# --- b. Log generation stats ---
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read -r MIN MAX MEAN < <(printf '%s' "$SCORE_VALUES" | py_stats)
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log " Stats: min=$MIN max=$MAX mean=$MEAN candidates=$CAND_COUNT"
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# Find best candidate for this generation (filepath returned directly).
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BEST_FILE_THIS_GEN=$(python3 - "$SCORES_FILE" <<'PYEOF'
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import sys
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entries = []
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with open(sys.argv[1]) as f:
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for line in f:
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parts = line.rstrip('\n').split('\t')
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if len(parts) >= 3:
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entries.append((int(parts[1]), parts[2]))
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if not entries:
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sys.exit(1)
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print(max(entries, key=lambda x: x[0])[1])
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PYEOF
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) || fail "Could not determine best candidate from $SCORES_FILE"
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if [ "$MAX" -gt "$GLOBAL_BEST_FITNESS" ] || [ "$GLOBAL_BEST_FITNESS" -eq -1 ]; then
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GLOBAL_BEST_FITNESS="$MAX"
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GLOBAL_BEST_GEN="$gen"
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GLOBAL_BEST_CAND="$BEST_FILE_THIS_GEN"
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log " New global best: gen=$gen fitness=$GLOBAL_BEST_FITNESS file=$(basename "$BEST_FILE_THIS_GEN")"
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fi
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# Skip next-generation creation after the final generation
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[ "$gen" -eq "$((GENERATIONS - 1))" ] && break
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# --- c. Tournament selection (k = population / 2) ---
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K=$((POPULATION / 2))
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[ "$K" -lt 1 ] && K=1
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SURVIVOR_FILES=()
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while IFS= read -r WIN_FILE; do
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SURVIVOR_FILES+=("$WIN_FILE")
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done < <(py_tournament "$K" "$SCORES_FILE")
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log " Selected ${#SURVIVOR_FILES[@]} survivors via tournament"
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# --- d. Generate next population ---
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NEXT_GEN_DIR="$WORK_DIR/gen_$((gen + 1))"
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mkdir -p "$NEXT_GEN_DIR"
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NEXT_IDX=0
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HALF=$((POPULATION / 2))
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# First half: mutate random survivors
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for _i in $(seq 1 $HALF); do
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SUR="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
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DEST="$NEXT_GEN_DIR/candidate_$(printf '%03d' $NEXT_IDX).push3"
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if MUTATED=$(run_mutate_cli mutate "$SUR" "$MUTATION_RATE" 2>/dev/null); then
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printf '%s\n' "$MUTATED" > "$DEST"
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printf '%d\n' "$MUTATION_RATE" > "${DEST%.push3}.ops"
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else
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# Fallback: copy the survivor as-is to keep population size stable
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cp "$SUR" "$DEST"
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printf '0\n' > "${DEST%.push3}.ops"
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fi
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NEXT_IDX=$((NEXT_IDX + 1))
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done
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# Second half: crossover random survivor pairs
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REMAINING=$((POPULATION - HALF))
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for _i in $(seq 1 $REMAINING); do
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SUR_A="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
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SUR_B="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
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DEST="$NEXT_GEN_DIR/candidate_$(printf '%03d' $NEXT_IDX).push3"
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if CROSSED=$(run_mutate_cli crossover "$SUR_A" "$SUR_B" 2>/dev/null); then
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printf '%s\n' "$CROSSED" > "$DEST"
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printf '0\n' > "${DEST%.push3}.ops"
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else
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# Fallback: mutate one survivor
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if MUTATED=$(run_mutate_cli mutate "$SUR_A" "$MUTATION_RATE" 2>/dev/null); then
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printf '%s\n' "$MUTATED" > "$DEST"
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printf '%d\n' "$MUTATION_RATE" > "${DEST%.push3}.ops"
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else
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cp "$SUR_A" "$DEST"
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printf '0\n' > "${DEST%.push3}.ops"
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fi
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fi
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NEXT_IDX=$((NEXT_IDX + 1))
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done
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||
log " Generated ${NEXT_IDX} candidates for generation $((gen + 1))"
|
||
CURRENT_GEN_DIR="$NEXT_GEN_DIR"
|
||
|
||
done
|
||
|
||
# =============================================================================
|
||
# Step 3 — Output best candidate
|
||
# =============================================================================
|
||
|
||
if [ -z "$GLOBAL_BEST_CAND" ] || [ ! -f "$GLOBAL_BEST_CAND" ]; then
|
||
fail "No valid best candidate recorded — evolution produced no scorable output"
|
||
fi
|
||
|
||
BEST_OUTPUT="$OUTPUT_DIR/best.push3"
|
||
cp "$GLOBAL_BEST_CAND" "$BEST_OUTPUT"
|
||
log ""
|
||
log "Best candidate → $BEST_OUTPUT"
|
||
log " Fitness: $GLOBAL_BEST_FITNESS (generation $GLOBAL_BEST_GEN)"
|
||
|
||
# =============================================================================
|
||
# Step 4 — Diff: original vs evolved constants
|
||
# =============================================================================
|
||
|
||
DIFF_OUTPUT="$OUTPUT_DIR/diff.txt"
|
||
|
||
python3 - "$SEED" "$BEST_OUTPUT" > "$DIFF_OUTPUT" <<'PYEOF'
|
||
import sys, re
|
||
|
||
def extract_ints(path):
|
||
"""Extract all large integer literals (≥6 digits) from a Push3 file."""
|
||
text = open(path).read()
|
||
text = re.sub(r';;[^\n]*', '', text) # strip comments
|
||
return [int(m) for m in re.findall(r'\b(\d{6,})\b', text)]
|
||
|
||
seed_path, best_path = sys.argv[1], sys.argv[2]
|
||
orig = extract_ints(seed_path)
|
||
best = extract_ints(best_path)
|
||
|
||
print(f"=== Push3 Evolution Diff ===")
|
||
print(f"Seed: {seed_path}")
|
||
print(f"Best: {best_path}")
|
||
print()
|
||
|
||
changed = 0
|
||
for i, (o, b) in enumerate(zip(orig, best)):
|
||
if o != b:
|
||
pct = (b - o) / o * 100 if o != 0 else float('inf')
|
||
print(f" const[{i:3d}]: {o:>25d} → {b:>25d} (Δ={b - o:+d}, {pct:+.2f}%)")
|
||
changed += 1
|
||
|
||
if len(orig) != len(best):
|
||
added = len(best) - len(orig)
|
||
if added > 0:
|
||
for i, val in enumerate(best[len(orig):]):
|
||
print(f" const[{len(orig) + i:3d}]: {'(new)':>25s} → {val:>25d}")
|
||
else:
|
||
print(f" ({-added} constant(s) removed from end)")
|
||
|
||
print()
|
||
if changed == 0 and len(orig) == len(best):
|
||
print("No constant changes — evolution applied structural mutations only.")
|
||
else:
|
||
total = min(len(orig), len(best))
|
||
print(f"Summary: {changed} of {total} constant(s) changed.")
|
||
PYEOF
|
||
|
||
log "Diff written to $DIFF_OUTPUT"
|
||
log ""
|
||
cat "$DIFF_OUTPUT" >&2
|
||
|
||
log "========================================================"
|
||
log "Evolution complete."
|
||
log " Generations run: $GENERATIONS"
|
||
log " Best fitness: $GLOBAL_BEST_FITNESS"
|
||
log " Best from gen: $GLOBAL_BEST_GEN"
|
||
log " Output directory: $OUTPUT_DIR"
|
||
log "========================================================"
|