Research-Stack/4-Infrastructure/hardware/integrated_prover_pipeline.py
2026-05-05 21:09:48 -05:00

313 lines
10 KiB
Python

#!/usr/bin/env python3
"""
Integrated Prover Pipeline with GPU+FPGA Architecture
Combines:
- GPU workhorse for Q16.16 arithmetic and mass number calculations
- FPGA verifier for hardware verification
- Goedel-Prover-V2 for formal proof generation
- bf4prover for sorry block repair
- bfs_prover for AVM trace auditing
"""
import subprocess
import json
import os
from pathlib import Path
from typing import List, Dict, Tuple
from multiprocessing import Pool, cpu_count
import time
import numpy as np
try:
import cupy as cp
GPU_AVAILABLE = True
except ImportError:
GPU_AVAILABLE = False
# Import from existing modules
import sys
sys.path.append(str(Path(__file__).parent.parent.parent / "scripts"))
sys.path.append(str(Path(__file__).parent.parent.parent / "5-Applications" / "scripts"))
class IntegratedProverPipeline:
"""Unified pipeline combining GPU, FPGA, and multiple provers."""
def __init__(self):
self.base_path = Path("/home/allaun/Documents/Research Stack")
self.gpu_available = GPU_AVAILABLE
self.ollama_url = "http://localhost:11434/api/generate"
def run_goedel_prover(self, lean_file: str) -> Dict:
"""Run Goedel-Prover-V2 on a Lean file."""
goedel_path = self.base_path / "ai-math-discovery-systems" / "Goedel-Prover-V2"
inference_script = goedel_path / "src" / "inference.py"
if not inference_script.exists():
return {
"file": lean_file,
"prover": "Goedel-Prover-V2",
"status": "error",
"error": "Goedel-Prover-V2 inference script not found"
}
try:
result = subprocess.run(
["python", str(inference_script), lean_file],
capture_output=True,
text=True,
timeout=300,
cwd=str(goedel_path)
)
return {
"file": lean_file,
"prover": "Goedel-Prover-V2",
"status": "success" if result.returncode == 0 else "error",
"output": result.stdout,
"error": result.stderr if result.returncode != 0 else None
}
except Exception as e:
return {
"file": lean_file,
"prover": "Goedel-Prover-V2",
"status": "error",
"error": str(e)
}
def run_bf4prover(self, lean_file: str) -> Dict:
"""Run bf4prover on a Lean file."""
bf4prover_script = self.base_path / "scripts" / "bf4prover.py"
if not bf4prover_script.exists():
return {
"file": lean_file,
"prover": "bf4prover",
"status": "error",
"error": "bf4prover script not found"
}
try:
result = subprocess.run(
["python", str(bf4prover_script), lean_file, "--dry-run"],
capture_output=True,
text=True,
timeout=120,
cwd=str(self.base_path)
)
return {
"file": lean_file,
"prover": "bf4prover",
"status": "success" if result.returncode == 0 else "error",
"output": result.stdout,
"error": result.stderr if result.returncode != 0 else None
}
except Exception as e:
return {
"file": lean_file,
"prover": "bf4prover",
"status": "error",
"error": str(e)
}
def run_bfs_prover_audit(self) -> Dict:
"""Run bfs_prover bridge for AVM trace auditing."""
bfs_script = self.base_path / "5-Applications" / "scripts" / "bfs_prover_bridge.py"
if not bfs_script.exists():
return {
"prover": "bfs_prover",
"status": "error",
"error": "bfs_prover bridge script not found"
}
try:
result = subprocess.run(
["python", str(bfs_script)],
capture_output=True,
text=True,
timeout=600,
cwd=str(self.base_path)
)
return {
"prover": "bfs_prover",
"status": "success" if result.returncode == 0 else "error",
"output": result.stdout,
"error": result.stderr if result.returncode != 0 else None
}
except Exception as e:
return {
"prover": "bfs_prover",
"status": "error",
"error": str(e)
}
def run_gpu_fpga_checker(self, lean_file: str) -> Dict:
"""Run the GPU workhorse + FPGA verifier."""
checker_script = self.base_path / "4-Infrastructure" / "hardware" / "gpu_fpga_distributed_checker.py"
if not checker_script.exists():
return {
"file": lean_file,
"prover": "GPU+FPGA",
"status": "error",
"error": "GPU+FPGA checker script not found"
}
try:
# Import and run the checker
import importlib.util
spec = importlib.util.spec_from_file_location("gpu_fpga_checker", checker_script)
checker_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(checker_module)
result = checker_module.GPUFPGAChecker().process_file(lean_file)
return {
"file": lean_file,
"prover": "GPU+FPGA",
"status": result.get('status', 'error'),
"verified": result.get('verified', False),
"details": result.get('verification_details', [])
}
except Exception as e:
return {
"file": lean_file,
"prover": "GPU+FPGA",
"status": "error",
"error": str(e)
}
def classify_file_for_prover(self, lean_file: str) -> str:
"""
Classify which prover should handle this file.
- Files with sorry blocks: bf4prover
- Files needing formal proofs: Goedel-Prover-V2
- Files with Q16.16 arithmetic: GPU+FPGA
- AVM trace files: bfs_prover
"""
file_lower = lean_file.lower()
# Check for sorry blocks
file_path = self.base_path / lean_file
if file_path.exists():
with open(file_path, 'r') as f:
content = f.read()
if 'sorry' in content and 'TODO' in content:
return 'bf4prover'
# Check for Q16.16 arithmetic patterns
if 'mass' in file_lower or 'q16' in file_lower or 'shell' in file_lower:
return 'gpu_fpga'
# Check for theorem/lemma heavy files
if 'theorem' in file_lower or 'lemma' in file_lower:
return 'goedel'
# Default to GPU+FPGA
return 'gpu_fpga'
def process_file(self, lean_file: str) -> Dict:
"""Process a single file with the appropriate prover."""
prover_type = self.classify_file_for_prover(lean_file)
if prover_type == 'bf4prover':
return self.run_bf4prover(lean_file)
elif prover_type == 'goedel':
return self.run_goedel_prover(lean_file)
elif prover_type == 'gpu_fpga':
return self.run_gpu_fpga_checker(lean_file)
else:
return {
"file": lean_file,
"prover": "unknown",
"status": "error",
"error": f"Unknown prover type: {prover_type}"
}
def main():
print("=== Integrated Prover Pipeline with GPU+FPGA Architecture ===")
pipeline = IntegratedProverPipeline()
base_path = pipeline.base_path
os.chdir(base_path)
# Find all Lean files
files = list(base_path.rglob("*.lean"))
filtered = []
for f in files:
path_str = str(f)
if 'archive' not in path_str and 'consolidated' not in path_str and '.changes' not in path_str:
filtered.append(str(f.relative_to(base_path)))
print(f"Found {len(filtered)} Lean files to process")
print(f"GPU available: {pipeline.gpu_available}")
# Process in parallel
start_time = time.time()
num_workers = cpu_count()
print(f"Using {num_workers} worker processes")
# Process a sample first to test
sample_size = min(100, len(filtered))
sample_files = filtered[:sample_size]
print(f"Processing sample of {sample_size} files...")
with Pool(num_workers) as pool:
results = pool.map(pipeline.process_file, sample_files)
# Run bfs_prover audit separately (it's not file-based)
print("\nRunning bfs_prover AVM audit...")
bfs_result = pipeline.run_bfs_prover_audit()
elapsed = time.time() - start_time
# Analyze results
goedel_count = sum(1 for r in results if r.get('prover') == 'Goedel-Prover-V2')
bf4_count = sum(1 for r in results if r.get('prover') == 'bf4prover')
gpu_fpga_count = sum(1 for r in results if r.get('prover') == 'GPU+FPGA')
success_count = sum(1 for r in results if r.get('status') == 'success' or r.get('status') == 'verified')
error_count = sum(1 for r in results if r.get('status') == 'error')
print("\n=== Summary ===")
print(f"Total files processed: {len(results)}")
print(f"Goedel-Prover-V2: {goedel_count}")
print(f"bf4prover: {bf4_count}")
print(f"GPU+FPGA: {gpu_fpga_count}")
print(f"bfs_prover audit: {bfs_result.get('status', 'error')}")
print(f"Successful: {success_count}")
print(f"Errors: {error_count}")
print(f"Elapsed time: {elapsed:.2f} seconds")
print(f"Files per second: {len(results)/elapsed:.2f}")
# Save report
report = {
"total_files": len(results),
"prover_distribution": {
"Goedel-Prover-V2": goedel_count,
"bf4prover": bf4_count,
"GPU+FPGA": gpu_fpga_count,
"bfs_prover": bfs_result.get('status')
},
"success_count": success_count,
"error_count": error_count,
"elapsed_time": elapsed,
"files_per_second": len(results)/elapsed,
"gpu_available": pipeline.gpu_available,
"results": results,
"bfs_audit": bfs_result
}
report_file = base_path / "4-Infrastructure" / "hardware" / "integrated_prover_report.json"
with open(report_file, 'w') as f:
json.dump(report, f, indent=2)
print(f"\nReport saved to: {report_file}")
if __name__ == "__main__":
main()