339 lines
11 KiB
Python
Executable File
339 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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FHE HNSW并行测试脚本 - 多进程版本
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测试密文态HNSW实现的稳定性和准确率
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支持多进程并行执行，充分利用服务器CPU资源
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运行100次，记录≥90%准确率的成功率和结果长度分布
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"""
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import subprocess
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import json
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import time
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import os
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import multiprocessing as mp
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from pathlib import Path
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from collections import defaultdict
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# 正确答案
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CORRECT_ANSWER = [93, 94, 90, 27, 87, 50, 47, 40, 78, 28]
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def calculate_accuracy(result, correct):
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"""计算准确率：匹配元素数量 / 总元素数量"""
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matches = len(set(result) & set(correct))
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return matches / len(correct) * 100
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def run_single_test(test_id, data_bit_width="12", timeout_minutes=30):
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"""运行单次FHE HNSW测试（供多进程调用）"""
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# 为每个进程创建独立的输出文件
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output_file = f"./test_fhe_output_{test_id}_{os.getpid()}.jsonl"
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cmd = [
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"./enc",
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"--algorithm",
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"hnsw",
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"--data-bit-width",
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data_bit_width,
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"--predictions",
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output_file,
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]
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start_time = time.time()
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try:
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result = subprocess.run(
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cmd, capture_output=True, text=True, timeout=timeout_minutes * 60
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)
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test_time = time.time() - start_time
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if result.returncode != 0:
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return {
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"test_id": test_id,
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"result": None,
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"error": f"Command failed: {result.stderr[:200]}",
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"time_minutes": test_time / 60,
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}
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# 读取结果
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if not Path(output_file).exists():
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return {
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"test_id": test_id,
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"result": None,
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"error": "Output file not found",
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"time_minutes": test_time / 60,
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}
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with open(output_file, "r") as f:
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output = json.load(f)
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answer = output["answer"]
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# 清理临时文件
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Path(output_file).unlink(missing_ok=True)
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# 计算准确率
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accuracy = calculate_accuracy(answer, CORRECT_ANSWER)
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return {
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"test_id": test_id,
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"result": answer,
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"length": len(answer),
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"accuracy": accuracy,
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"success": accuracy >= 90,
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"time_minutes": test_time / 60,
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"error": None,
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}
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except subprocess.TimeoutExpired:
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# 清理临时文件
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Path(output_file).unlink(missing_ok=True)
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return {
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"test_id": test_id,
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"result": None,
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"error": "timeout",
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"time_minutes": timeout_minutes,
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}
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except Exception as e:
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# 清理临时文件
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Path(output_file).unlink(missing_ok=True)
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return {
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"test_id": test_id,
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"result": None,
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"error": str(e),
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"time_minutes": (time.time() - start_time) / 60,
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}
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def print_progress(completed, total, start_time):
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"""打印进度信息"""
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if completed == 0:
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return
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elapsed = time.time() - start_time
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avg_time = elapsed / completed
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remaining_time = avg_time * (total - completed)
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print(
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f"\r🔄 进度: {completed}/{total} ({completed/total*100:.1f}%) "
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f"| 已用时: {elapsed/3600:.1f}h | 预计剩余: {remaining_time/3600:.1f}h",
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end="",
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flush=True,
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)
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def main():
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"""主测试函数"""
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print("🚀 FHE HNSW 并行批量测试脚本")
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print(f"🎯 正确答案: {CORRECT_ANSWER}")
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print("📊 运行100次测试，记录准确率和结果长度分布")
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print("🔧 支持多进程并行执行")
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# 检查enc二进制文件是否存在
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if not Path("./enc").exists():
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print("\n❌ 找不到 ./enc 二进制文件，请先编译项目:")
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print(" cargo build --release --bin enc")
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return
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# 获取CPU核心数并设置进程数
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cpu_cores = mp.cpu_count()
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# 考虑到FHE运算的内存密集性，使用核心数的一半避免内存不足
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num_processes = 8
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timeout_minutes = 45 # 增加超时时间到45分钟
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print(f"💻 使用 {num_processes} 个并行进程")
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print(f"⏰ 单次测试超时时间: {timeout_minutes} 分钟")
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print(
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f"⏱️ 预计总时间: {100 * 15 / num_processes / 60:.1f}-{100 * 25 / num_processes / 60:.1f} 小时"
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)
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print()
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print("=" * 80)
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print("🔬 开始FHE HNSW并行测试 (data-bit-width=12)")
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print("=" * 80)
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start_time = time.time()
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# 创建进程池并执行测试
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test_ids = list(range(1, 101)) # 1-100
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with mp.Pool(processes=num_processes) as pool:
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# 创建异步任务
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async_results = []
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for test_id in test_ids:
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async_result = pool.apply_async(
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run_single_test, (test_id, "12", timeout_minutes)
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)
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async_results.append(async_result)
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# 收集结果并显示进度
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results = []
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completed = 0
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print_progress(completed, len(test_ids), start_time)
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for async_result in async_results:
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try:
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result = async_result.get(
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timeout=timeout_minutes * 60 + 60
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) # 额外1分钟缓冲
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results.append(result)
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completed += 1
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print_progress(completed, len(test_ids), start_time)
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except mp.TimeoutError:
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# 进程级别超时
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results.append(
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{
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"test_id": len(results) + 1,
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"result": None,
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"error": "process_timeout",
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"time_minutes": timeout_minutes,
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}
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)
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completed += 1
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print_progress(completed, len(test_ids), start_time)
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print() # 换行
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total_elapsed = time.time() - start_time
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# 分析结果
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print("\n" + "=" * 80)
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print("📈 测试结果分析")
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print("=" * 80)
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# 统计变量
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valid_results = [r for r in results if r["result"] is not None]
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success_count = sum(1 for r in valid_results if r["success"])
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length_distribution = defaultdict(int)
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error_distribution = defaultdict(int)
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# 统计错误类型
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for r in results:
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if r["error"]:
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error_type = r["error"]
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if "timeout" in error_type.lower():
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error_distribution["timeout"] += 1
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elif "failed" in error_type.lower():
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error_distribution["failed"] += 1
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else:
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error_distribution["other"] += 1
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else:
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length_distribution[r["length"]] += 1
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# 基本统计
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total_tests = len(results)
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valid_tests = len(valid_results)
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if valid_tests == 0:
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print("❌ 没有有效的测试结果")
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return
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success_rate = success_count / valid_tests * 100
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avg_accuracy = sum(r["accuracy"] for r in valid_results) / valid_tests
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avg_length = sum(r["length"] for r in valid_results) / valid_tests
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avg_time_per_test = sum(r["time_minutes"] for r in results) / total_tests
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print(f"总测试次数: {total_tests}")
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print(f"有效测试次数: {valid_tests}")
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print(f"成功次数 (≥90%准确率): {success_count}")
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print(f"成功率: {success_rate:.1f}%")
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print(f"平均准确率: {avg_accuracy:.1f}%")
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print(f"平均结果长度: {avg_length:.1f}")
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print(f"平均每次测试时间: {avg_time_per_test:.1f}分钟")
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print(f"总测试时间: {total_elapsed/3600:.1f}小时")
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print(f"并行加速比: {100 * avg_time_per_test / 60 / (total_elapsed/3600):.1f}x")
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# 错误统计
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if error_distribution:
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print("\n❌ 错误分布:")
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for error_type, count in error_distribution.items():
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print(f" {error_type}: {count}次")
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# 结果长度分布
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if length_distribution:
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print("\n📊 结果长度分布:")
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for length in sorted(length_distribution.keys()):
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count = length_distribution[length]
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percentage = count / valid_tests * 100
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bar = "█" * (count // 2) if count > 0 else ""
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print(f" 长度 {length:2d}: {count:3d}次 ({percentage:5.1f}%) {bar}")
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# 结论
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print()
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if success_rate >= 50:
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print("✅ 测试通过! FHE HNSW实现稳定性良好")
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print(f" - 成功率: {success_rate:.1f}% (≥50%)")
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print(f" - 平均准确率: {avg_accuracy:.1f}%")
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if avg_length >= 9.5:
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print(f" - 平均结果长度: {avg_length:.1f} (接近10)")
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else:
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print(f" - 平均结果长度: {avg_length:.1f} (需要改进)")
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else:
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print("❌ 测试未通过，需要进一步优化")
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print(f" - 成功率: {success_rate:.1f}% (<50%)")
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print(f" - 平均准确率: {avg_accuracy:.1f}%")
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# 保存详细结果
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report_file = "fhe_hnsw_parallel_test_report.json"
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with open(report_file, "w") as f:
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json.dump(
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{
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"summary": {
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"total_tests": total_tests,
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"valid_tests": valid_tests,
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"success_count": success_count,
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"success_rate": success_rate,
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"avg_accuracy": avg_accuracy,
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"avg_length": avg_length,
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"avg_time_minutes": avg_time_per_test,
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"total_time_hours": total_elapsed / 3600,
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"num_processes": num_processes,
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"cpu_cores": cpu_cores,
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"speedup": 100 * avg_time_per_test / 60 / (total_elapsed / 3600),
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},
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"length_distribution": dict(length_distribution),
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"error_distribution": dict(error_distribution),
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"detailed_results": results,
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"correct_answer": CORRECT_ANSWER,
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},
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f,
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indent=2,
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)
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print(f"\n📁 详细报告已保存到: {report_file}")
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# 显示最好和最坏的几个结果
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if valid_results:
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print("\n🏆 最高准确率的5个结果:")
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best_results = sorted(valid_results, key=lambda x: x["accuracy"], reverse=True)[
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:5
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]
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for i, r in enumerate(best_results, 1):
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print(
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f" {i}. 测试#{r['test_id']:3d}: 准确率{r['accuracy']:5.1f}% 长度{r['length']:2d} ({r['time_minutes']:4.1f}分钟)"
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)
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print("\n⚠️ 最低准确率的5个结果:")
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worst_results = sorted(valid_results, key=lambda x: x["accuracy"])[:5]
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for i, r in enumerate(worst_results, 1):
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print(
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f" {i}. 测试#{r['test_id']:3d}: 准确率{r['accuracy']:5.1f}% 长度{r['length']:2d} ({r['time_minutes']:4.1f}分钟)"
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)
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if __name__ == "__main__":
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try:
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# 设置多进程启动方法（Linux上默认是fork，但spawn更安全）
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mp.set_start_method("spawn", force=True)
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main()
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except KeyboardInterrupt:
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print("\n\n⏹️ 测试被用户中断")
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# 清理可能的临时文件
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for f in Path(".").glob("test_fhe_output_*.jsonl"):
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f.unlink(missing_ok=True)
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except Exception as e:
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print(f"\n💥 程序异常: {e}")
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# 清理可能的临时文件
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for f in Path(".").glob("test_fhe_output_*.jsonl"):
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f.unlink(missing_ok=True)
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