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317 lines
11 KiB
Python
317 lines
11 KiB
Python
"""
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GeoWeird Lean Bridge - Python bindings for Lean formalization
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Connects thermal_arbiter.py, mass_archivist.py, sovereign_warden to Lean FFI
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"""
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import ctypes
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import numpy as np
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from typing import Optional, List, Tuple
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from dataclasses import dataclass
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from pathlib import Path
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import json
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# Load the Lean FFI shared library
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_LIB_PATH = Path(__file__).parent.parent / "build" / "lib" / "libgeoweirdffi.so"
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try:
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_lib = ctypes.CDLL(str(_LIB_PATH))
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except OSError:
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print(f"[LeanBridge] FFI library not found at {_LIB_PATH}, using mock mode")
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_lib = None
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@dataclass
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class PhaseTransitionSignal:
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"""Signal from Lean phase detector"""
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should_transition: bool
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target_phase: str
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entropy_seed: float
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frozen_isa_hash: str
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@dataclass
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class WitnessOpcode:
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"""WITNESS_* opcode from emergence"""
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opcode_type: str # "BASIC", "EMERGENCE", "COLLIDE", "NOVEL"
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geometry_budget: float
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trust_stamp: float
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recoverability_class: int
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raw_bytes: bytes
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class LeanBridge:
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"""
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Bridge between Python implementation and Lean formalization.
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This class:
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1. Exports Python state to Lean (stress levels, metrics, etc.)
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2. Receives phase transition signals from Lean
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3. Coordinates emergence protocol execution
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4. Persists discovered witnesses via mass_archivist
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"""
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def __init__(
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self,
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thermal_arbiter=None,
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mass_archivist=None,
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warden_bridge=None
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):
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self.thermal = thermal_arbiter
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self.archivist = mass_archivist
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self.warden = warden_bridge
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# Phase transition state
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self.current_phase = "Darwinian"
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self.metric_history: List[Tuple[float, float]] = [] # (metric, load)
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self.emergence_active = False
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self.truth_registry: List[WitnessOpcode] = []
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# Thresholds (match Lean defaults)
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self.thresholds = {
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"min_improvement_rate": 0.001,
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"max_acceleration": 0.0001,
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"sustained_windows": 10,
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"entropy_floor": 0.5
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}
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# Initialize FFI if available
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if _lib:
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self._init_ffi()
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def _init_ffi(self):
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"""Initialize C FFI layer"""
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# Create opaque handles for Python objects
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thermal_handle = ctypes.py_object(self.thermal) if self.thermal else None
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archivist_handle = ctypes.py_object(self.archivist) if self.archivist else None
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warden_handle = ctypes.py_object(self.warden) if self.warden else None
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# Call geoweird_ffi_init
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_lib.geoweird_ffi_init.argtypes = [
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ctypes.py_object, ctypes.py_object, ctypes.py_object
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]
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_lib.geoweird_ffi_init(thermal_handle, archivist_handle, warden_handle)
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def record_epoch(self, metric: float, cognitive_load: float) -> Optional[PhaseTransitionSignal]:
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"""
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Record optimization result and check for phase transition.
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Called by sovereign_warden after each batch attestation.
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Args:
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metric: Optimization metric (e.g., compression ratio)
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cognitive_load: From COGNITIVE_LOAD_FUNCTIONS_SPEC
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Returns:
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PhaseTransitionSignal if transition should occur, None otherwise
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"""
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self.metric_history.append((metric, cognitive_load))
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# Keep only last 1000 points
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if len(self.metric_history) > 1000:
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self.metric_history = self.metric_history[-1000:]
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# Check for diminishing returns
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if len(self.metric_history) >= self.thresholds["sustained_windows"]:
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if self._check_diminishing_returns():
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return self._trigger_emergence()
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return None
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def _check_diminishing_returns(self) -> bool:
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"""Check if improvement has plateaued"""
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recent = self.metric_history[-self.thresholds["sustained_windows"]:]
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metrics = [m for m, _ in recent]
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# Calculate first derivative (improvement rate)
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first_derivs = [metrics[i+1] - metrics[i] for i in range(len(metrics)-1)]
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avg_first = sum(first_derivs) / len(first_derivs)
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# Calculate second derivative (acceleration)
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second_derivs = [first_derivs[i+1] - first_derivs[i] for i in range(len(first_derivs)-1)]
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avg_second = sum(abs(d) for d in second_derivs) / len(second_derivs)
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# Check conditions
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improvement_stalled = avg_first <= self.thresholds["min_improvement_rate"]
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acceleration_collapsed = avg_second < self.thresholds["max_acceleration"]
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return improvement_stalled and acceleration_collapsed
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def _trigger_emergence(self) -> PhaseTransitionSignal:
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"""Trigger emergence phase"""
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self.emergence_active = True
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self.current_phase = "Emergence"
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# Calculate entropy seed from metric history
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entropy = self._calculate_entropy()
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signal = PhaseTransitionSignal(
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should_transition=True,
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target_phase="Emergence",
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entropy_seed=entropy,
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frozen_isa_hash=self._capture_isa_hash()
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)
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print(f"[LeanBridge] Phase transition triggered!")
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print(f" Entropy seed: {entropy:.4f}")
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print(f" Metric history: {len(self.metric_history)} points")
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return signal
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def _calculate_entropy(self) -> float:
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"""Calculate Shannon entropy of metric distribution"""
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if len(self.metric_history) < 2:
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return 0.5
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metrics = np.array([m for m, _ in self.metric_history])
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# Simple entropy estimate from variance
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variance = np.var(metrics)
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return min(1.0, variance / (1.0 + variance))
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def _capture_isa_hash(self) -> str:
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"""Capture current ISA version hash"""
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# In real implementation, hash current opcode set
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import hashlib
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isa_data = json.dumps({"version": "2.0.0", "opcodes": []})
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return hashlib.sha256(isa_data.encode()).hexdigest()[:16]
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def run_emergence(
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self,
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universe_configs: List[dict],
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collision_budget: int = 10000
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) -> List[WitnessOpcode]:
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"""
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Run emergence protocol with multi-universe collision.
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Args:
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universe_configs: List of 5 universe configurations
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collision_budget: Max collisions before stopping
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Returns:
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List of discovered WITNESS_* opcodes
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"""
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if len(universe_configs) != 5:
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raise ValueError("Exactly 5 universes required (Euclidean, Hyperbolic, Spherical, Lorentzian, Custom)")
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print(f"[LeanBridge] Starting emergence with {collision_budget} collision budget")
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discovered = []
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# Generate all pairwise collisions
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pairs = [(i, j) for i in range(5) for j in range(i+1, 5)]
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for collision_id in range(min(collision_budget, len(pairs) * 100)):
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# Select universe pair
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a_idx, b_idx = pairs[collision_id % len(pairs)]
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# Simulate collision (real impl uses Lean formalization)
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witness = self._simulate_collision(
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universe_configs[a_idx],
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universe_configs[b_idx],
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collision_id
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)
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if witness:
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discovered.append(witness)
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self.truth_registry.append(witness)
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# Persist via mass_archivist if available
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if self.archivist:
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self._persist_witness(witness)
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print(f"[LeanBridge] Emergence complete: {len(discovered)} witnesses discovered")
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self.emergence_active = False
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self.current_phase = "Consolidation"
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return discovered
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def _simulate_collision(
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self,
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universe_a: dict,
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universe_b: dict,
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collision_id: int
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) -> Optional[WitnessOpcode]:
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"""Simulate collision between two universes"""
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# Check compatibility
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if universe_a.get("dimension") != universe_b.get("dimension"):
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return None
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# Calculate consensus strength
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curvature_product = universe_a.get("curvature", 0) * universe_b.get("curvature", 0)
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consensus_strength = 0.5 + 0.5 * np.tanh(curvature_product)
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if consensus_strength < 0.49:
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return None # Weak consensus
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# Generate witness
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witness = WitnessOpcode(
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opcode_type="EMERGENCE",
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geometry_budget=min(
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universe_a.get("volume", 1.0),
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universe_b.get("volume", 1.0)
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) * 0.1,
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trust_stamp=consensus_strength,
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recoverability_class=1,
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raw_bytes=f"WITNESS_EMERGENCE_{collision_id}".encode()
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)
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return witness
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def _persist_witness(self, witness: WitnessOpcode) -> bool:
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"""Persist witness via mass_archivist"""
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if not self.archivist:
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return False
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# Wait for rest event
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if hasattr(self.archivist, 'arbiter'):
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if not self.archivist.arbiter.rest_event.wait(timeout=5.0):
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return False
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# Serialize and submit
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data = json.dumps({
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"type": witness.opcode_type,
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"budget": witness.geometry_budget,
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"trust": witness.trust_stamp,
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"recoverability": witness.recoverability_class
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}).encode()
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# Submit to archivist (mock)
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print(f"[LeanBridge] Persisted witness (trust={witness.trust_stamp:.3f})")
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return True
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def export_to_lean(self) -> dict:
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"""Export current state for Lean verification"""
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return {
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"phase": self.current_phase,
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"metric_history": self.metric_history,
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"truth_registry": [
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{
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"type": w.opcode_type,
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"budget": w.geometry_budget,
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"trust": w.trust_stamp
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}
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for w in self.truth_registry
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],
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"thresholds": self.thresholds
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}
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def cleanup(self):
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"""Cleanup FFI resources"""
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if _lib:
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_lib.geoweird_ffi_cleanup()
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# Singleton instance for global access
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_bridge: Optional[LeanBridge] = None
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def init_bridge(thermal=None, archivist=None, warden=None) -> LeanBridge:
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"""Initialize global Lean bridge"""
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global _bridge
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_bridge = LeanBridge(thermal, archivist, warden)
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return _bridge
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def get_bridge() -> Optional[LeanBridge]:
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"""Get global Lean bridge instance"""
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return _bridge
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