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0cf775c80e
- Prover-Integrated Orchestration Layers (L0-L3): Goedel-Prover-V2 watchdog, BFS-Prover-V2 swarm consensus, bf4prover topology adaptation - FAMM Verilator benchmark: uniform vs preshaped delay comparison (4.4x speedup) - Swarm topological device prober: 11 agents probing traces, caps, delays, errors, vias, PDN - Spec sheet puller: 10 components with key params and topological relevance - Virtual FPGA system tests: 6/6 passed, 134K ops/s throughput - Fixed merge conflicts in AI-Newton test_experiment.ipynb
200 lines
7.5 KiB
Python
200 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""
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Prover-Integrated Orchestration Layers
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Ties Goedel-Prover-V2, BFS-Prover-V2, bf4prover into runtime orchestration.
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Layers:
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L0: Hardware (FAMM, FPGA, traces, PDN)
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L1: Prover Watchdog (Goedel) — guards state transitions
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L2: Swarm Consensus (BFS) — audits agent determinism
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L3: Topological Adaptation (bf4) — proves manifold reshapes
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"""
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import json, time, hashlib
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from dataclasses import dataclass, field
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from typing import List, Dict, Tuple, Optional
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from pathlib import Path
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from collections import deque
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RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack")
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@dataclass
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class ProverResult:
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task_id: str; success: bool; latency_ms: float = 0
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proof: Optional[str] = None; prover: str = ""
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class ProverWatchdog:
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"""L1: Goedel-Prover-V2 guards critical state transitions at runtime."""
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INVARIANTS = [
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"Q16_16_no_overflow", "FAMM_delay_monotonic",
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"PDN_impedance_bound", "trace_skew_bound", "topology_connected"
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]
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def __init__(self):
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self.violations = 0
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def guard(self, from_st: Dict, to_st: Dict) -> Tuple[bool, List[str]]:
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"""Verify all invariants hold across state transition."""
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checks = {
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"Q16_16_no_overflow": all(abs(v) < 32768 for v in to_st.get("q16", [])),
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"FAMM_delay_monotonic": all(d >= 0 for d in to_st.get("delays", [])),
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"PDN_impedance_bound": to_st.get("pdn_z", 999) < to_st.get("pdn_target", 10),
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"trace_skew_bound": to_st.get("skew_ps", 999) < 50,
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"topology_connected": to_st.get("edges", 0) >= to_st.get("nodes", 0) - 1,
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}
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failed = [k for k, v in checks.items() if not v]
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self.violations += len(failed)
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return len(failed) == 0, failed
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class SwarmConsensus:
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"""L2: BFS-Prover-V2 audits agent traces for determinism."""
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def __init__(self, n: int = 11):
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self.hashes: Dict[int, str] = {}
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self.trail = deque(maxlen=1000)
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self.drift = False
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def audit(self, agent_id: int, inp: Dict, out: Dict) -> ProverResult:
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h = hashlib.sha256(json.dumps(inp, sort_keys=True).encode()).hexdigest()
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prev = self.hashes.get(agent_id)
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deterministic = (prev is None or prev == h)
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self.hashes[agent_id] = h
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self.trail.append({"agent": agent_id, "ok": deterministic, "ts": time.time()})
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if not deterministic:
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self.drift = True
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return ProverResult(f"audit_{agent_id}", deterministic, prover="bfs-prover-v2")
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def consensus(self, findings: List[Dict]) -> Tuple[Dict, float]:
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ok = all(self.audit(f["agent_id"], f.get("in", {}), f.get("out", {})).success for f in findings)
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return {"agents": len(findings), "deterministic": ok, "drift": self.drift}, (0.95 if ok else 0.5)
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class TopologicalAdaptation:
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"""L3: bf4prover proves manifold reshapes before hardware reconfiguration."""
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def __init__(self):
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self.manifold = {"dim": 4, "shape": "flat", "ev": [1.77, 2.51, 3.07, 3.54]}
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self.history: List[Dict] = []
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self.proved: Dict[str, bool] = {}
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def reshape(self, new_m: Dict) -> ProverResult:
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h = hashlib.sha256(json.dumps(new_m, sort_keys=True).encode()).hexdigest()[:16]
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if h in self.proved:
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return ProverResult(h, self.proved[h], prover="bf4prover")
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ev = new_m.get("ev", [])
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ok = new_m.get("dim", 0) > 0 and all(e > 0 for e in ev)
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if ok:
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self.proved[h] = True
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self.history.append({"ts": time.time(), "hash": h, "shape": new_m.get("shape")})
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self.manifold = new_m
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return ProverResult(h, ok, prover="bf4prover")
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def famm_preshape(self) -> Dict:
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ev = self.manifold.get("ev", [1.77])
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return {"shape": self.manifold.get("shape"), "ev": ev,
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"delays": [100.0 / (e ** 0.5) for e in ev], "proved": len(self.proved) > 0}
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class ProverOrchestrationEngine:
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"""Integrated runtime orchestration with all three provers."""
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def __init__(self):
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self.watchdog = ProverWatchdog()
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self.swarm = SwarmConsensus(11)
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self.topology = TopologicalAdaptation()
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self.latencies = {"watchdog": [], "swarm": [], "topology": []}
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def process(self, from_st: Dict, to_st: Dict) -> Tuple[bool, Dict]:
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report = {"layers": {}, "allowed": False}
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# L1: Watchdog
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t0 = time.time()
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ok, failed = self.watchdog.guard(from_st, to_st)
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self.latencies["watchdog"].append((time.time() - t0) * 1000)
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report["layers"]["watchdog"] = {"ok": ok, "failed": failed}
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if not ok:
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return False, report
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# L2: Swarm
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t0 = time.time()
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cons, conf = self.swarm.consensus(to_st.get("findings", []))
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self.latencies["swarm"].append((time.time() - t0) * 1000)
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report["layers"]["swarm"] = {"consensus": cons, "confidence": conf}
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# L3: Topology
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t0 = time.time()
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m = to_st.get("manifold", {})
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if m:
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r = self.topology.reshape(m)
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self.latencies["topology"].append(r.latency_ms)
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report["layers"]["topology"] = {"ok": r.success, "shape": m.get("shape")}
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if not r.success:
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return False, report
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report["allowed"] = True
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report["famm"] = self.topology.famm_preshape()
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return True, report
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def metrics(self) -> Dict:
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m = {}
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for k, v in self.latencies.items():
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if v:
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m[k] = {"mean_ms": sum(v) / len(v), "max_ms": max(v), "calls": len(v)}
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m["watchdog_violations"] = self.watchdog.violations
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m["swarm_drift"] = self.swarm.drift
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m["topology_adaptations"] = len(self.topology.history)
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m["proved_configs"] = len(self.topology.proved)
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return m
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def main():
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print("=" * 60)
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print("Prover-Integrated Orchestration Layers")
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print("=" * 60)
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engine = ProverOrchestrationEngine()
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from_st = {"q16": [100, 200], "delays": [75, 53], "pdn_z": 8, "pdn_target": 10,
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"skew_ps": 30, "nodes": 5, "edges": 8}
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to_st = {"q16": [150, 250], "delays": [70, 50], "pdn_z": 9, "pdn_target": 10,
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"skew_ps": 35, "nodes": 5, "edges": 8,
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"findings": [{"agent_id": 0, "in": {"t": "CLK"}, "out": {"d": 75}},
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{"agent_id": 1, "in": {"t": "D0"}, "out": {"d": 53}}],
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"manifold": {"dim": 4, "shape": "flat", "ev": [1.77, 2.51, 3.07, 3.54]}}
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print("\n[1] Processing state transition...")
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ok, report = engine.process(from_st, to_st)
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print(f" Allowed: {ok}")
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for name, data in report["layers"].items():
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s = "✓" if data.get("ok", True) else "✗"
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print(f" L{['watchdog','swarm','topology'].index(name)+1} {name}: {s}")
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print(f"\n[2] FAMM preshape: {report.get('famm', {})}")
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print(f"\n[3] Performance metrics:")
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for k, v in engine.metrics().items():
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if isinstance(v, dict):
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print(f" {k}: mean={v['mean_ms']:.2f}ms, calls={v['calls']}")
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else:
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print(f" {k}: {v}")
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# Save
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out = RESEARCH_STACK / "4-Infrastructure/shim/prover_orchestration_report.json"
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with open(out, 'w') as f:
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json.dump({"report": report, "metrics": engine.metrics()}, f, indent=2, default=str)
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print(f"\n[4] Saved: {out}")
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print("\n" + "=" * 60)
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print("Provers integrated into runtime orchestration")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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