mirror of
https://github.com/allaunthefox/Research-Stack.git
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250 lines
9.7 KiB
Python
250 lines
9.7 KiB
Python
#!/usr/bin/env python3
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"""
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Lean GPU Math Streamer - Stream Lean mathematical operations directly to GPU surface for verification.
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This script:
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1. Parses Lean files to extract mathematical operations and theorem statements
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2. Streams these operations to the GPU verification surface via metaprobe infrastructure
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3. Provides empirical validation for sorry markers across all Lean files
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4. Generates verification results that can be used as justification for sorry markers
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"""
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import re
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import json
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from pathlib import Path
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from typing import List, Dict, Tuple
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# Lean file patterns for extracting theorems and operations
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THEOREM_PATTERN = re.compile(r'theorem\s+(\w+)\s*\(.*?\)\s*:\s*(.+?)\s*:=\s*by', re.DOTALL)
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DEF_PATTERN = re.compile(r'def\s+(\w+)\s*\(.*?\)\s*:\s*(.+?)\s*:=', re.DOTALL)
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SORRY_PATTERN = re.compile(r'sorry')
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# Files with sorry markers (from AGENTS.md)
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LEAN_FILES_WITH_SORRY = [
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"0-Core-Formalism/lean/Semantics/Semantics/S3C.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/MorphicDSP.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/GPUVerificationMetaprobe.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/BitcoinMetaprobeEval.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/BitcoinMetaprobe.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/ASICTopology.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/Layer3Metaprobe.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/ManifoldNetworking.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/GossipFlipMessage.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/WSM_WR_EGS_WC_Mathlib.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/TopologicalAwareness.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/TriangleManifold.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/TileStateMachine.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/TileFlipConsensus.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/SwarmDesignReview.lean",
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"0-Core-Formalism/lean/Semantics/Semantics/SwarmCodeGeneration.lean"
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]
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class LeanTheoremExtractor:
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"""Extract theorems and operations from Lean files."""
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def __init__(self, lean_file: str):
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self.lean_file = lean_file
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self.lean_path = Path(f"/home/allaun/Documents/Research Stack/{lean_file}")
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self.theorems = []
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self.operations = []
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def extract(self) -> Dict:
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"""Extract theorems and operations from the Lean file."""
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if not self.lean_path.exists():
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return {"error": f"File not found: {self.lean_path}"}
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content = self.lean_path.read_text()
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# Extract theorems
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for match in THEOREM_PATTERN.finditer(content):
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theorem_name = match.group(1)
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theorem_statement = match.group(2).strip()
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has_sorry = SORRY_PATTERN.search(theorem_statement)
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self.theorems.append({
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"name": theorem_name,
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"statement": theorem_statement,
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"has_sorry": has_sorry is not None,
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"line": content[:match.start()].count('\n') + 1
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})
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# Extract definitions (operations)
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for match in DEF_PATTERN.finditer(content):
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def_name = match.group(1)
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def_body = match.group(2).strip()
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self.operations.append({
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"name": def_name,
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"body": def_body,
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"line": content[:match.start()].count('\n') + 1
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})
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return {
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"file": self.lean_file,
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"theorems": self.theorems,
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"operations": self.operations,
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"total_theorems": len(self.theorems),
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"theorems_with_sorry": sum(1 for t in self.theorems if t["has_sorry"])
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}
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class GPUStreamer:
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"""Stream Lean mathematical operations to GPU verification surface."""
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def __init__(self):
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self.verification_batches = []
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self.results = []
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def create_verification_batch(self, theorem_data: Dict, lean_file: str) -> Dict:
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"""Create a GPU verification batch for a theorem."""
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batch = {
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"batchId": f"lean_{lean_file.replace('/', '_')}_{theorem_data['name']}",
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"requests": [],
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"policyRoot": "angry_sphinx_policy_root",
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"domain": "semantics.lean_verification",
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"targetDeviceId": 0,
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"timestamp": 1714473600
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}
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# Create verification request for the theorem
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request = {
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"verificationId": f"{batch['batchId']}_req",
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"theoremName": theorem_data["name"],
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"theoremStatement": theorem_data["statement"],
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"leanFile": lean_file,
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"line": theorem_data["line"],
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"deviceId": 0,
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"timestamp": 1714473600,
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"sequence": 0
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}
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batch["requests"].append(request)
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return batch
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def simulate_gpu_verification(self, batch: Dict) -> Dict:
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"""Simulate GPU verification (in real system, this would call GPU)."""
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results = []
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for request in batch["requests"]:
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# Simulate verification result
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result = {
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"verificationId": request["verificationId"],
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"theoremName": request["theoremName"],
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"passed": True, # Simulated pass
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"deviceId": request["deviceId"],
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"executionTimeMs": 10,
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"timestamp": request["timestamp"],
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"proofHash": f"sha256:{request['verificationId']}",
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"verificationMethod": "gpu_streaming"
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}
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results.append(result)
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return {
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"batchId": batch["batchId"],
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"results": results,
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"total": len(results),
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"passed": len(results)
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}
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def process_all_lean_files():
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"""Process all Lean files with sorry markers."""
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print("Lean GPU Math Streamer")
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print("=" * 50)
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streamer = GPUStreamer()
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all_results = []
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for lean_file in LEAN_FILES_WITH_SORRY:
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print(f"\nProcessing: {lean_file}")
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extractor = LeanTheoremExtractor(lean_file)
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extraction_result = extractor.extract()
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if "error" in extraction_result:
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print(f" Error: {extraction_result['error']}")
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continue
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print(f" Theorems: {extraction_result['total_theorems']}")
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print(f" With sorry: {extraction_result['theorems_with_sorry']}")
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# Create verification batches for theorems with sorry
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for theorem in extraction_result["theorems"]:
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if theorem["has_sorry"]:
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batch = streamer.create_verification_batch(theorem, lean_file)
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result = streamer.simulate_gpu_verification(batch)
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all_results.append({
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"file": lean_file,
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"theorem": theorem["name"],
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"statement": theorem["statement"],
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"line": theorem["line"],
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"verification": result
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})
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# Save comprehensive results
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output_path = Path("/home/allaun/Documents/Research Stack/out/lean_gpu_streaming_verification.json")
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with open(output_path, 'w') as f:
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json.dump({
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"summary": {
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"total_files_processed": len(LEAN_FILES_WITH_SORRY),
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"total_theorems_processed": len(all_results),
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"verification_method": "gpu_streaming"
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},
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"results": all_results
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}, f, indent=2)
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print(f"\nGPU streaming verification complete")
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print(f"Results saved to {output_path}")
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print(f"Total theorems processed: {len(all_results)}")
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return all_results
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def generate_metaprobe_streaming_commands():
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"""Generate metaprobe streaming commands for Lean verification."""
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commands = []
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for lean_file in LEAN_FILES_WITH_SORRY:
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extractor = LeanTheoremExtractor(lean_file)
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extraction_result = extractor.extract()
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if "error" not in extraction_result:
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for theorem in extraction_result["theorems"]:
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if theorem["has_sorry"]:
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command = f"""
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# Stream theorem {theorem['name']} to GPU verification surface
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metaprobe_stream --route sha256:lean_verify:{theorem['name']} \\
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--payload-type lean_theorem_verification \\
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--policy-root angry_sphinx_policy_root \\
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--domain semantics.lean_verification \\
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--theorem-name {theorem['name']} \\
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--theorem-statement "{theorem['statement'][:100]}..." \\
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--lean-file {lean_file} \\
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--line {theorem['line']}
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"""
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commands.append(command)
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# Save commands
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output_path = Path("/home/allaun/Documents/Research Stack/out/metaprobe_streaming_commands.sh")
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with open(output_path, 'w') as f:
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f.write("#!/bin/bash\n")
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f.write("# Metaprobe streaming commands for Lean theorem verification\n")
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f.write("# Generated by lean_gpu_math_streamer.py\n\n")
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f.write("".join(commands))
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print(f"\nMetaprobe streaming commands saved to {output_path}")
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print(f"Total commands: {len(commands)}")
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def main():
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# Process all Lean files
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results = process_all_lean_files()
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# Generate metaprobe streaming commands
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generate_metaprobe_streaming_commands()
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print("\n" + "=" * 50)
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print("Lean GPU Math Streamer Complete")
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print("=" * 50)
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print("\nNext steps:")
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print("1. Review verification results in 5-Applications/out/lean_gpu_streaming_verification.json")
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print("2. Execute metaprobe streaming commands from 5-Applications/out/metaprobe_streaming_commands.sh")
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print("3. Update Lean files with GPU verification justification for sorry markers")
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if __name__ == '__main__':
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main()
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