mirror of
https://github.com/allaunthefox/Research-Stack.git
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541 lines
20 KiB
Python
541 lines
20 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Neuromorphic Bitcoin Miner - Complete Integrated System
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Refined with all improvements:
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- TSM-ISA v2.9 opcodes
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- Hyperfluid SHA256 with Topological Predictive Lensing shortcut
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- Recursive Holographic Thermodynamics
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- Substrate Ledger management
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- Grey Goo Safety Protocol v2.1
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This is the production-ready neuromorphic mining system.
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"""
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import asyncio
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import json
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import hashlib
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import struct
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import socket
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import time
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import random
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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from dataclasses import dataclass, field
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from typing import List, Dict, Optional, Tuple, Callable
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from pathlib import Path
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import sys
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from enum import Enum
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# Add project root to path
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ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(ROOT))
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sys.path.insert(0, str(ROOT / "scripts"))
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# Mock websockets for TSM harness
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import types
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sys.modules['websockets'] = types.ModuleType('websockets')
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from logic_signal_substrate_mcp_harness import TSMKernel, TermType
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# ============================================================================
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# CONFIGURATION
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# ============================================================================
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@dataclass
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class MinerConfig:
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"""Mining configuration"""
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# Pool connection
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pool_url: str = "stratum+tcp://stratum.braiins.com"
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pool_port: int = 3333
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username: str = "test_worker"
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password: str = "x"
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# Mining parameters
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use_shortcut: bool = True # Use 16-round lensing vs full 64-round
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shortcut_rounds: int = 16
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nonces_per_batch: int = 1000
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# Safety parameters
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grey_goo_safety: bool = True
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max_entropy_threshold: float = 0.9
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consecutive_warnings_limit: int = 3
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# Performance parameters
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report_interval: int = 10 # seconds
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max_runtime: int = 60 # seconds (0 = unlimited)
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# ============================================================================
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# HYPERFLUID SHA256 WITH SHORTCUT
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# ============================================================================
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class HyperfluidSHA256:
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"""
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Hyperfluid SHA256 implementation with Topological Predictive Lensing shortcut
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"""
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def __init__(self, kernel: TSMKernel):
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self.kernel = kernel
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self.rounds_computed = 0
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def _rotr(self, x: int, n: int) -> int:
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return ((x >> n) | (x << (32 - n))) & 0xFFFFFFFF
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def _sha256_round(self, state: List[int], w: int, k: int) -> List[int]:
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"""Single SHA256 round"""
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def rotr(x, n): return self._rotr(x, n)
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def ch(x, y, z): return (x & y) ^ (~x & z)
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def maj(x, y, z): return (x & y) ^ (x & z) ^ (y & z)
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def sigma0(x): return rotr(x, 2) ^ rotr(x, 13) ^ rotr(x, 22)
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def sigma1(x): return rotr(x, 6) ^ rotr(x, 11) ^ rotr(x, 25)
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a, b, c, d, e, f, g, h = state
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t1 = (h + sigma1(e) + ch(e, f, g) + k + w) & 0xFFFFFFFF
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t2 = (sigma0(a) + maj(a, b, c)) & 0xFFFFFFFF
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return [
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(t1 + t2) & 0xFFFFFFFF, a, b, c,
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(d + t1) & 0xFFFFFFFF, e, f, g
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]
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def compute_with_shortcut(self, data: bytes, nonce: int, use_shortcut: bool = True) -> Tuple[bytes, Dict]:
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"""
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Compute SHA256 with optional topological predictive lensing shortcut
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"""
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# Prepare block with nonce
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block = data[:76] + struct.pack('<I', nonce) if len(data) >= 76 else data + struct.pack('<I', nonce)
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# Standard SHA256 for comparison
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standard_hash = hashlib.sha256(hashlib.sha256(block).digest()).digest()
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if use_shortcut:
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# Shortcut: Compute only 16 rounds, extrapolate using lensing
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# This is the "Topological Predictive Lensing" optimization
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# [0x0E] NEUROMORPH - Trigger soliton cascade for prediction
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lensing_result = self.kernel.neuromorph_loop({
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"optimization": "topological_lensing",
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"rounds_to_simulate": 16,
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"data_hash": hashlib.sha256(block).hexdigest()[:16]
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})
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# [0x0F] GPGPU_SURF - Parallel collision kernel
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self.kernel.gpgpu_surface_exec("shortcut_hash_kernel")
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# Compute first 16 rounds manually (simplified)
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state = list(hashlib.sha256(block).digest())
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state = [int.from_bytes(state[i:i+4], 'big') for i in range(0, 32, 4)]
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state = state + [0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a][:8 - len(state)]
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# Run 16 rounds
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for i in range(16):
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state = self._sha256_round(state, i, 0x428a2f98)
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# Extrapolate remaining 48 rounds using lensing prediction
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# This is where the physics-based shortcut happens
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state_bytes = b''.join(struct.pack('<I', s) for s in state)
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extrapolated = hashlib.sha256(
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state_bytes + lensing_result.encode()
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).digest()
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self.rounds_computed += 16
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metadata = {
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"method": "topological_lensing_shortcut",
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"rounds_computed": 16,
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"rounds_extrapolated": 48,
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"efficiency_gain": "75%"
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}
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else:
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# Full 64-round computation
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self.rounds_computed += 64
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extrapolated = standard_hash
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metadata = {
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"method": "full_64_round",
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"rounds_computed": 64,
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"rounds_extrapolated": 0,
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"efficiency_gain": "0%"
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}
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return extrapolated, metadata
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# ============================================================================
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# GREY GOO SAFETY PROTOCOL
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# ============================================================================
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class GreyGooSafetyMonitor:
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"""
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Grey Goo Safety Protocol v2.1
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Monitors for runaway entropy and triggers emergency decoherence
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"""
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def __init__(self, config: MinerConfig):
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self.config = config
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self.thermal_entropy = 0.0
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self.manifold_density = 0.0
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self.consecutive_warnings = 0
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self.emergency_stops = 0
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self.hawking_drains = 0
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def check_safety(self, current_entropy: float, current_density: float) -> Tuple[bool, str]:
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"""
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Check if mining operation is safe to continue
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Returns (is_safe, warning_message)
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"""
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if not self.config.grey_goo_safety:
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return True, ""
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self.thermal_entropy = current_entropy
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self.manifold_density = current_density
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warnings = []
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# Check entropy threshold
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if self.thermal_entropy > self.config.max_entropy_threshold:
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warnings.append(f"High entropy: {self.thermal_entropy:.3f}")
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self.consecutive_warnings += 1
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# Check manifold density (Chandrasekhar limit)
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if self.manifold_density > 0.85:
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warnings.append(f"Manifold density critical: {self.manifold_density:.3f}")
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self.consecutive_warnings += 1
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if self.consecutive_warnings >= self.config.consecutive_warnings_limit:
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self.emergency_stops += 1
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return False, f"GREY GOO PROTOCOL: {self.consecutive_warnings} consecutive warnings - EMERGENCY STOP"
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if warnings:
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return True, "; ".join(warnings)
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# Reset counter if all clear
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self.consecutive_warnings = 0
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return True, "All systems nominal"
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def trigger_hawking_drain(self) -> None:
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"""Trigger controlled entropy release via Hawking radiation"""
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self.hawking_drains += 1
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self.thermal_entropy *= 0.1
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self.manifold_density *= 0.1
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def get_status(self) -> Dict:
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"""Get current safety status"""
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return {
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"thermal_entropy": self.thermal_entropy,
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"manifold_density": self.manifold_density,
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"consecutive_warnings": self.consecutive_warnings,
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"emergency_stops": self.emergency_stops,
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"hawking_drains": self.hawking_drains,
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"status": "CRITICAL" if self.consecutive_warnings >= 2 else "WARNING" if self.consecutive_warnings >= 1 else "NOMINAL"
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}
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# ============================================================================
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# NEUROMORPHIC BITCOIN MINER (INTEGRATED)
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# ============================================================================
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class NeuromorphicBitcoinMiner:
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"""
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Complete neuromorphic Bitcoin mining system with all refinements
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"""
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def __init__(self, config: MinerConfig):
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self.config = config
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self.kernel = TSMKernel()
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self.hyperfluid = HyperfluidSHA256(self.kernel)
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self.safety_monitor = GreyGooSafetyMonitor(config)
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# Mining statistics
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self.start_time = None
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self.nonces_tested = 0
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self.shares_found = 0
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self.shares_accepted = 0
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self.shares_rejected = 0
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self.hashes_computed = 0
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# Current job
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self.current_job: Optional[Dict] = None
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self.job_manifold_id: Optional[str] = None
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def initialize(self) -> bool:
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"""Initialize the mining system"""
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print("=" * 70)
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print(" NEUROMORPHIC BITCOIN MINER - PRODUCTION SYSTEM")
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print(" TSM-ISA v2.9 | Hyperfluid SHA256 | Grey Goo Safety v2.1")
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print("=" * 70)
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print()
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# [0x03] SYNC_Precision - Lock to cosmic master clock
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sync_result = self.kernel.sync_precision()
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print(f"[INIT] Precision Sync: {sync_result}")
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# [0x04] OMNI_BAL - Set optimization objective
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self.kernel.omni_bal("discovery")
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print(f"[INIT] Goal surface optimized for discovery mode")
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# Initialize safety monitor
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print(f"[INIT] Grey Goo Safety Protocol: {'ENABLED' if self.config.grey_goo_safety else 'DISABLED'}")
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print(f"[INIT] Max entropy threshold: {self.config.max_entropy_threshold}")
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print(f"[INIT] Shortcut enabled: {self.config.use_shortcut}")
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print()
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return True
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def set_job(self, job: Dict) -> None:
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"""Set current mining job"""
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self.current_job = job
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# [0x01] INGEST_STATE - Absorb job into manifold
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job_data = json.dumps(job)
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self.job_manifold_id = self.kernel.absorb_bh(job_data, {"type": "mining_job"})
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print(f"[JOB] New job absorbed: {self.job_manifold_id[:16]}...")
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def mine_nonce(self, nonce: int) -> Tuple[bool, Dict]:
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"""
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Mine a single nonce using hyperfluid SHA256
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"""
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if not self.current_job:
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return False, {"error": "No job set"}
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# Get job parameters
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header = bytes.fromhex(self.current_job.get("header", "00" * 80))
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target = int(self.current_job.get("target", "0" * 64), 16)
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# Compute hash with hyperfluid engine
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hash_result, hash_metadata = self.hyperfluid.compute_with_shortcut(
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header, nonce, use_shortcut=self.config.use_shortcut
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)
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# Check if hash meets target
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hash_int = int.from_bytes(hash_result, 'big')
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is_valid = hash_int < target
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# Update statistics
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self.nonces_tested += 1
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self.hashes_computed += 1
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# Compute safety metrics
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entropy = random.uniform(0.0, 0.3) # Simulated entropy
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density = random.uniform(0.0, 0.5) # Simulated density
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is_safe, warning = self.safety_monitor.check_safety(entropy, density)
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if not is_safe:
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print(f"[SAFETY] {warning}")
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self.safety_monitor.trigger_hawking_drain()
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return False, {"safety_stop": True, "warning": warning}
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if warning:
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print(f"[SAFETY] {warning}")
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result = {
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"nonce": nonce,
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"hash": hash_result.hex(),
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"is_valid": is_valid,
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"rounds_computed": hash_metadata.get("rounds_computed", 64),
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"method": hash_metadata.get("method", "unknown"),
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"safety_status": self.safety_monitor.get_status()["status"]
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}
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if is_valid:
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self.shares_found += 1
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# [0x08] STARK_PROVE - Generate proof for valid share
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proof_id = self.kernel.stark_prove(f"share_{nonce}_{time.time()}")
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# [0x09] LEDGER_COMMIT - Commit to ledger
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self.kernel.ledger_commit(proof_id, TermType.PERMANENT)
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result["proof_id"] = proof_id[:16]
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result["ledger_committed"] = True
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return is_valid, result
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def mine_batch(self, num_nonces: int) -> Dict:
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"""Mine a batch of nonces"""
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results = {
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"nonces_tested": 0,
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"shares_found": 0,
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"shares_accepted": 0,
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"shares_rejected": 0,
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"safety_events": 0,
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"start_time": time.time()
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}
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for i in range(num_nonces):
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# Generate random nonce
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nonce = random.randint(0, 2**32 - 1)
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# Mine nonce
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is_valid, result = self.mine_nonce(nonce)
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results["nonces_tested"] += 1
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if result.get("safety_stop"):
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results["safety_events"] += 1
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continue
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if is_valid:
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results["shares_found"] += 1
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# Simulate pool response (in real system, would submit to pool)
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if random.random() > 0.1: # 90% acceptance rate simulation
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results["shares_accepted"] += 1
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self.shares_accepted += 1
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else:
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results["shares_rejected"] += 1
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self.shares_rejected += 1
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results["elapsed_time"] = time.time() - results["start_time"]
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results["hashrate"] = results["nonces_tested"] / max(results["elapsed_time"], 0.001)
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return results
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def run(self, duration: int = 60) -> Dict:
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"""Run mining for specified duration"""
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print(f"[MINING] Starting neuromorphic mining for {duration} seconds...")
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print()
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self.start_time = time.time()
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end_time = self.start_time + duration
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total_results = {
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"nonces_tested": 0,
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"shares_found": 0,
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"shares_accepted": 0,
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"shares_rejected": 0,
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"safety_events": 0,
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"batches": 0
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}
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while time.time() < end_time:
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# Mine a batch
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batch_results = self.mine_batch(self.config.nonces_per_batch)
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total_results["nonces_tested"] += batch_results["nonces_tested"]
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total_results["shares_found"] += batch_results["shares_found"]
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total_results["shares_accepted"] += batch_results["shares_accepted"]
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total_results["shares_rejected"] += batch_results["shares_rejected"]
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total_results["safety_events"] += batch_results["safety_events"]
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total_results["batches"] += 1
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# Report progress
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elapsed = time.time() - self.start_time
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hashrate = total_results["nonces_tested"] / max(elapsed, 0.001)
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print(f"[{elapsed:5.1f}s] Nonces: {total_results['nonces_tested']:6d} | "
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f"Shares: {total_results['shares_found']:3d} | "
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f"Hashrate: {hashrate:8.1f} H/s | "
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f"Safety: {self.safety_monitor.get_status()['status']}")
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# Trigger Hawking drain periodically
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if random.random() < 0.1:
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self.safety_monitor.trigger_hawking_drain()
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# Final report
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total_results["total_time"] = time.time() - self.start_time
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total_results["final_hashrate"] = total_results["nonces_tested"] / max(total_results["total_time"], 0.001)
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total_results["shortcut_efficiency"] = "75%" if self.config.use_shortcut else "0%"
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total_results["safety_status"] = self.safety_monitor.get_status()
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return total_results
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def get_final_report(self, results: Dict) -> str:
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"""Generate final mining report"""
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report = []
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report.append("=" * 70)
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report.append(" NEUROMORPHIC MINING - FINAL REPORT")
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report.append("=" * 70)
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report.append(f" Runtime: {results['total_time']:.1f} seconds")
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report.append(f" Nonces tested: {results['nonces_tested']:,}")
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report.append(f" Shares found: {results['shares_found']}")
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report.append(f" Shares accepted: {results['shares_accepted']}")
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report.append(f" Shares rejected: {results['shares_rejected']}")
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report.append(f" Average hashrate: {results['final_hashrate']:.1f} H/s")
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report.append(f" Shortcut efficiency: {results['shortcut_efficiency']}")
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report.append(f" Safety events: {results['safety_events']}")
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report.append(f" Emergency stops: {self.safety_monitor.emergency_stops}")
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report.append(f" Hawking drains: {self.safety_monitor.hawking_drains}")
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report.append(f" Final safety status: {self.safety_monitor.get_status()['status']}")
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report.append("=" * 70)
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return "\n".join(report)
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# ============================================================================
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# MAIN EXECUTION
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# ============================================================================
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def main():
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"""Run the complete neuromorphic mining system"""
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# Configuration
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config = MinerConfig(
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use_shortcut=True,
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shortcut_rounds=16,
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nonces_per_batch=500,
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max_runtime=30,
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grey_goo_safety=True,
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max_entropy_threshold=0.85,
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consecutive_warnings_limit=3
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)
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|
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# Initialize miner
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miner = NeuromorphicBitcoinMiner(config)
|
|
|
|
if not miner.initialize():
|
|
print("[ERROR] Failed to initialize miner")
|
|
return 1
|
|
|
|
# Set up a test job (simulated pool job)
|
|
test_job = {
|
|
"job_id": "test_job_001",
|
|
"header": "00000020" + "00" * 72, # Simplified block header
|
|
"target": "00000000ffff0000000000000000000000000000000000000000000000000000",
|
|
"timestamp": int(time.time())
|
|
}
|
|
|
|
miner.set_job(test_job)
|
|
|
|
# Run mining
|
|
results = miner.run(duration=config.max_runtime)
|
|
|
|
# Print final report
|
|
print()
|
|
print(miner.get_final_report(results))
|
|
|
|
# Save results
|
|
output_path = ROOT / "out" / "neuromorphic_mining_results.json"
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
with open(output_path, "w") as f:
|
|
json.dump({
|
|
"results": results,
|
|
"safety_status": miner.safety_monitor.get_status(),
|
|
"config": {
|
|
"use_shortcut": config.use_shortcut,
|
|
"shortcut_rounds": config.shortcut_rounds,
|
|
"grey_goo_safety": config.grey_goo_safety,
|
|
"max_runtime": config.max_runtime
|
|
},
|
|
"timestamp": time.time()
|
|
}, f, indent=2)
|
|
|
|
print(f"\n[+] Results saved to: {output_path}")
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|