增加L0训练阶段的MCTS部分
This commit is contained in:
hisatri
1
tools/__init__.py
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1
tools/__init__.py
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# Deep2048 工具包
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356
tools/benchmark.py
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356
tools/benchmark.py
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"""
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Deep2048 快速基准测试工具
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自动测试不同配置的性能，找出最优的线程数和参数设置
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"""
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import time
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import torch
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import multiprocessing as mp
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from typing import Dict, List, Tuple, Optional
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import json
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from pathlib import Path
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import argparse
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from game import Game2048
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from mcts import PureMCTS
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from training_data import TrainingDataManager
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class QuickBenchmark:
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"""快速基准测试工具"""
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def __init__(self, output_dir: str = "results/benchmark"):
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"""
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初始化基准测试
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Args:
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output_dir: 结果输出目录
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"""
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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# 系统信息
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self.cpu_count = mp.cpu_count()
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self.cuda_available = torch.cuda.is_available()
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print(f"系统信息:")
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print(f" CPU核心数: {self.cpu_count}")
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print(f" CUDA可用: {self.cuda_available}")
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if self.cuda_available:
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print(f" CUDA设备: {torch.cuda.get_device_name()}")
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def test_thread_performance(self, simulations: int = 200) -> Dict[int, Dict]:
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"""
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测试不同线程数的性能
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Args:
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simulations: 每次测试的模拟次数
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Returns:
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线程数 -> 性能指标的字典
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"""
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print(f"\n=== 线程性能测试 ({simulations} 模拟) ===")
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# 测试的线程数配置
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thread_configs = [1, 2, 4]
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if self.cpu_count >= 8:
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thread_configs.append(8)
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if self.cpu_count >= 16:
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thread_configs.append(16)
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results = {}
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for num_threads in thread_configs:
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print(f"\n测试 {num_threads} 线程...")
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# 创建MCTS
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mcts = PureMCTS(
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c_param=1.414,
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max_simulation_depth=80,
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num_threads=num_threads
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)
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# 运行多次测试取平均值
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times = []
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scores = []
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for run in range(3): # 3次运行
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game = Game2048(height=3, width=3, seed=42 + run)
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start_time = time.time()
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best_action, root = mcts.search(game, simulations)
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elapsed_time = time.time() - start_time
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times.append(elapsed_time)
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if root:
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# 计算平均子节点价值作为质量指标
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avg_value = sum(child.average_value for child in root.children.values()) / len(root.children) if root.children else 0
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scores.append(avg_value)
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else:
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scores.append(0)
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# 计算统计指标
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avg_time = sum(times) / len(times)
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avg_score = sum(scores) / len(scores)
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sims_per_sec = simulations / avg_time
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# 计算效率（每核心每秒模拟数）
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efficiency = sims_per_sec / num_threads
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# 计算相对于单线程的加速比
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if num_threads == 1:
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baseline_speed = sims_per_sec
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speedup = 1.0
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else:
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speedup = sims_per_sec / baseline_speed if 'baseline_speed' in locals() else 1.0
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results[num_threads] = {
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'avg_time': avg_time,
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'sims_per_sec': sims_per_sec,
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'efficiency': efficiency,
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'speedup': speedup,
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'avg_score': avg_score,
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'times': times
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}
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print(f" 平均时间: {avg_time:.3f}秒")
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print(f" 模拟速度: {sims_per_sec:.1f} 次/秒")
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print(f" 效率: {efficiency:.1f} 模拟/秒/核心")
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print(f" 加速比: {speedup:.2f}x")
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return results
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def test_simulation_depth(self, num_threads: int = None) -> Dict[int, Dict]:
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"""
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测试不同模拟深度的影响
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Args:
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num_threads: 线程数，None表示使用最优线程数
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Returns:
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深度 -> 性能指标的字典
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"""
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if num_threads is None:
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num_threads = min(4, self.cpu_count)
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print(f"\n=== 模拟深度测试 ({num_threads} 线程) ===")
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depths = [50, 80, 120, 200]
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results = {}
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for depth in depths:
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print(f"\n测试深度 {depth}...")
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mcts = PureMCTS(
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c_param=1.414,
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max_simulation_depth=depth,
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num_threads=num_threads
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)
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game = Game2048(height=3, width=3, seed=42)
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start_time = time.time()
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best_action, root = mcts.search(game, 150) # 固定模拟次数
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elapsed_time = time.time() - start_time
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sims_per_sec = 150 / elapsed_time
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avg_value = sum(child.average_value for child in root.children.values()) / len(root.children) if root and root.children else 0
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results[depth] = {
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'time': elapsed_time,
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'sims_per_sec': sims_per_sec,
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'avg_value': avg_value
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}
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print(f" 时间: {elapsed_time:.3f}秒")
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print(f" 速度: {sims_per_sec:.1f} 次/秒")
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print(f" 平均价值: {avg_value:.1f}")
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return results
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def test_board_sizes(self, num_threads: int = None) -> Dict[str, Dict]:
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"""
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测试不同棋盘大小的性能
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Args:
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num_threads: 线程数
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Returns:
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棋盘大小 -> 性能指标的字典
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"""
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if num_threads is None:
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num_threads = min(4, self.cpu_count)
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print(f"\n=== 棋盘大小测试 ({num_threads} 线程) ===")
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board_sizes = [(3, 3), (4, 4), (3, 4), (4, 3)]
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results = {}
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for height, width in board_sizes:
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size_key = f"{height}x{width}"
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print(f"\n测试 {size_key} 棋盘...")
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mcts = PureMCTS(
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c_param=1.414,
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max_simulation_depth=80,
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num_threads=num_threads
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)
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game = Game2048(height=height, width=width, seed=42)
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start_time = time.time()
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best_action, root = mcts.search(game, 100)
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elapsed_time = time.time() - start_time
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sims_per_sec = 100 / elapsed_time
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valid_moves = len(game.get_valid_moves())
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results[size_key] = {
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'time': elapsed_time,
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'sims_per_sec': sims_per_sec,
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'valid_moves': valid_moves,
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'board_cells': height * width
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}
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print(f" 时间: {elapsed_time:.3f}秒")
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print(f" 速度: {sims_per_sec:.1f} 次/秒")
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print(f" 有效动作: {valid_moves}")
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return results
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def find_optimal_config(self) -> Dict:
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"""
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找到最优配置
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Returns:
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最优配置字典
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"""
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print("\n=== 寻找最优配置 ===")
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# 测试线程性能
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thread_results = self.test_thread_performance(200)
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# 找到最优线程数（基于效率和绝对速度的平衡）
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best_thread_score = 0
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best_threads = 1
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for threads, result in thread_results.items():
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# 综合评分：速度 * 0.7 + 效率 * 0.3
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score = result['sims_per_sec'] * 0.7 + result['efficiency'] * 0.3
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if score > best_thread_score:
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best_thread_score = score
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best_threads = threads
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print(f"\n最优线程数: {best_threads}")
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print(f" 速度: {thread_results[best_threads]['sims_per_sec']:.1f} 模拟/秒")
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print(f" 效率: {thread_results[best_threads]['efficiency']:.1f} 模拟/秒/核心")
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print(f" 加速比: {thread_results[best_threads]['speedup']:.2f}x")
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# 测试其他参数
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depth_results = self.test_simulation_depth(best_threads)
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board_results = self.test_board_sizes(best_threads)
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# 推荐配置
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optimal_config = {
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'recommended_threads': best_threads,
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'recommended_depth': 80, # 平衡性能和质量
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'recommended_board_size': (3, 3), # L0阶段推荐
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'performance_summary': {
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'best_speed': thread_results[best_threads]['sims_per_sec'],
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'best_efficiency': thread_results[best_threads]['efficiency'],
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'speedup': thread_results[best_threads]['speedup']
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},
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'system_info': {
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'cpu_cores': self.cpu_count,
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'cuda_available': self.cuda_available
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}
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}
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return optimal_config
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def run_full_benchmark(self) -> Dict:
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"""运行完整基准测试"""
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print("Deep2048 快速基准测试")
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print("=" * 50)
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start_time = time.time()
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# 运行所有测试
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results = {
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'timestamp': time.time(),
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'system_info': {
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'cpu_cores': self.cpu_count,
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'cuda_available': self.cuda_available
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},
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'thread_performance': self.test_thread_performance(200),
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'optimal_config': self.find_optimal_config()
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}
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total_time = time.time() - start_time
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results['benchmark_time'] = total_time
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# 保存结果
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result_file = self.output_dir / f"benchmark_results_{int(time.time())}.json"
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with open(result_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, indent=2, ensure_ascii=False)
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print(f"\n基准测试完成! 用时: {total_time:.1f}秒")
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print(f"结果已保存到: {result_file}")
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return results
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def print_recommendations(self, results: Dict):
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"""打印配置推荐"""
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config = results['optimal_config']
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print("\n" + "=" * 50)
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print("🚀 性能优化推荐")
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print("=" * 50)
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print(f"推荐线程数: {config['recommended_threads']}")
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print(f"推荐模拟深度: {config['recommended_depth']}")
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print(f"推荐棋盘大小: {config['recommended_board_size']}")
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print(f"\n预期性能:")
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print(f" 模拟速度: {config['performance_summary']['best_speed']:.1f} 次/秒")
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print(f" CPU效率: {config['performance_summary']['best_efficiency']:.1f} 模拟/秒/核心")
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print(f" 多线程加速: {config['performance_summary']['speedup']:.2f}x")
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print(f"\n配置示例:")
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print(f"```python")
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print(f"mcts = PureMCTS(")
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print(f" c_param=1.414,")
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print(f" max_simulation_depth={config['recommended_depth']},")
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print(f" num_threads={config['recommended_threads']}")
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print(f")")
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print(f"```")
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def main():
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"""主函数"""
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parser = argparse.ArgumentParser(description="Deep2048快速基准测试")
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parser.add_argument("--output", "-o", default="results/benchmark", help="输出目录")
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parser.add_argument("--quick", action="store_true", help="快速测试模式")
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args = parser.parse_args()
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benchmark = QuickBenchmark(args.output)
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if args.quick:
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# 快速测试模式
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print("快速测试模式")
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thread_results = benchmark.test_thread_performance(100)
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# 简单推荐
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best_threads = max(thread_results.keys(), key=lambda k: thread_results[k]['sims_per_sec'])
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print(f"\n快速推荐: 使用 {best_threads} 线程")
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print(f"预期速度: {thread_results[best_threads]['sims_per_sec']:.1f} 模拟/秒")
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else:
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# 完整基准测试
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results = benchmark.run_full_benchmark()
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benchmark.print_recommendations(results)
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if __name__ == "__main__":
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main()
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280
tools/cleanup.py
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280
tools/cleanup.py
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"""
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项目清理工具
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清理临时文件、旧数据目录和不必要的文件
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"""
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import os
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import shutil
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from pathlib import Path
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import argparse
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class ProjectCleaner:
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"""项目清理器"""
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def __init__(self, project_root: Path = None):
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"""
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初始化清理器
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Args:
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project_root: 项目根目录
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"""
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self.project_root = project_root or Path(__file__).parent.parent
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# 要清理的目录模式
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self.temp_dirs = [
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# 旧命名的数据目录
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"*_data",
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"*_logs",
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"*_checkpoints",
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"training_data",
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"demo_training_data",
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"test_mcts_data",
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"benchmark_training_data",
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"gameplay_training_data",
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"demo_mcts_training",
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"test_batch_data",
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"test_l0_data",
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# Python缓存
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"__pycache__",
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".pytest_cache",
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# 临时文件
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"*.tmp",
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"*.temp",
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"*.bak",
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"*.backup",
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]
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# 要清理的文件模式
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self.temp_files = [
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"*.pyc",
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"*.pyo",
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"*.log",
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"*.pkl",
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"*.pickle",
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"*.prof",
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"*.profile",
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"mcts_*.png",
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]
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def scan_cleanup_targets(self) -> dict:
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"""扫描需要清理的目标"""
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targets = {
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'directories': [],
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'files': [],
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'total_size': 0
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}
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# 扫描目录
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for pattern in self.temp_dirs:
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for path in self.project_root.glob(pattern):
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if path.is_dir():
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size = self._get_dir_size(path)
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targets['directories'].append({
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'path': path,
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'size': size,
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'pattern': pattern
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})
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targets['total_size'] += size
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# 扫描文件
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for pattern in self.temp_files:
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for path in self.project_root.rglob(pattern):
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if path.is_file():
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size = path.stat().st_size
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targets['files'].append({
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'path': path,
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'size': size,
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'pattern': pattern
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})
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targets['total_size'] += size
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return targets
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def _get_dir_size(self, path: Path) -> int:
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"""计算目录大小"""
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total_size = 0
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try:
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for item in path.rglob('*'):
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if item.is_file():
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total_size += item.stat().st_size
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except (OSError, PermissionError):
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pass
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return total_size
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def _format_size(self, size_bytes: int) -> str:
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"""格式化文件大小"""
|
||||
for unit in ['B', 'KB', 'MB', 'GB']:
|
||||
if size_bytes < 1024:
|
||||
return f"{size_bytes:.1f} {unit}"
|
||||
size_bytes /= 1024
|
||||
return f"{size_bytes:.1f} TB"
|
||||
|
||||
def preview_cleanup(self) -> dict:
|
||||
"""预览清理操作"""
|
||||
targets = self.scan_cleanup_targets()
|
||||
|
||||
print("🔍 扫描清理目标...")
|
||||
print("=" * 50)
|
||||
|
||||
if targets['directories']:
|
||||
print(f"📁 目录 ({len(targets['directories'])} 个):")
|
||||
for item in targets['directories']:
|
||||
rel_path = item['path'].relative_to(self.project_root)
|
||||
size_str = self._format_size(item['size'])
|
||||
print(f" {rel_path} ({size_str})")
|
||||
|
||||
if targets['files']:
|
||||
print(f"\n📄 文件 ({len(targets['files'])} 个):")
|
||||
# 按大小排序，显示前10个最大的文件
|
||||
sorted_files = sorted(targets['files'], key=lambda x: x['size'], reverse=True)
|
||||
for item in sorted_files[:10]:
|
||||
rel_path = item['path'].relative_to(self.project_root)
|
||||
size_str = self._format_size(item['size'])
|
||||
print(f" {rel_path} ({size_str})")
|
||||
|
||||
if len(targets['files']) > 10:
|
||||
print(f" ... 还有 {len(targets['files']) - 10} 个文件")
|
||||
|
||||
total_size_str = self._format_size(targets['total_size'])
|
||||
print(f"\n💾 总大小: {total_size_str}")
|
||||
|
||||
return targets
|
||||
|
||||
def clean_targets(self, targets: dict, dry_run: bool = False) -> dict:
|
||||
"""执行清理操作"""
|
||||
results = {
|
||||
'cleaned_dirs': 0,
|
||||
'cleaned_files': 0,
|
||||
'freed_space': 0,
|
||||
'errors': []
|
||||
}
|
||||
|
||||
action = "预览" if dry_run else "清理"
|
||||
print(f"\n🧹 {action}清理操作...")
|
||||
print("=" * 50)
|
||||
|
||||
# 清理目录
|
||||
for item in targets['directories']:
|
||||
try:
|
||||
if not dry_run:
|
||||
shutil.rmtree(item['path'])
|
||||
|
||||
rel_path = item['path'].relative_to(self.project_root)
|
||||
size_str = self._format_size(item['size'])
|
||||
print(f"{'[预览]' if dry_run else '✅'} 删除目录: {rel_path} ({size_str})")
|
||||
|
||||
results['cleaned_dirs'] += 1
|
||||
results['freed_space'] += item['size']
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"删除目录失败 {item['path']}: {e}"
|
||||
results['errors'].append(error_msg)
|
||||
print(f"❌ {error_msg}")
|
||||
|
||||
# 清理文件
|
||||
for item in targets['files']:
|
||||
try:
|
||||
if not dry_run:
|
||||
item['path'].unlink()
|
||||
|
||||
rel_path = item['path'].relative_to(self.project_root)
|
||||
size_str = self._format_size(item['size'])
|
||||
print(f"{'[预览]' if dry_run else '✅'} 删除文件: {rel_path} ({size_str})")
|
||||
|
||||
results['cleaned_files'] += 1
|
||||
results['freed_space'] += item['size']
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"删除文件失败 {item['path']}: {e}"
|
||||
results['errors'].append(error_msg)
|
||||
print(f"❌ {error_msg}")
|
||||
|
||||
return results
|
||||
|
||||
def clean_project(self, dry_run: bool = False, interactive: bool = True) -> dict:
|
||||
"""清理项目"""
|
||||
print("🧹 Deep2048 项目清理工具")
|
||||
print("=" * 50)
|
||||
|
||||
# 扫描目标
|
||||
targets = self.preview_cleanup()
|
||||
|
||||
if targets['total_size'] == 0:
|
||||
print("\n✨ 项目已经很干净了！")
|
||||
return {'cleaned_dirs': 0, 'cleaned_files': 0, 'freed_space': 0, 'errors': []}
|
||||
|
||||
# 交互式确认
|
||||
if interactive and not dry_run:
|
||||
total_size_str = self._format_size(targets['total_size'])
|
||||
response = input(f"\n❓ 确定要清理这些文件吗？(将释放 {total_size_str}) [y/N]: ")
|
||||
if response.lower() not in ['y', 'yes']:
|
||||
print("❌ 清理操作已取消")
|
||||
return {'cleaned_dirs': 0, 'cleaned_files': 0, 'freed_space': 0, 'errors': []}
|
||||
|
||||
# 执行清理
|
||||
results = self.clean_targets(targets, dry_run)
|
||||
|
||||
# 显示结果
|
||||
print(f"\n📊 清理结果:")
|
||||
print(f" 清理目录: {results['cleaned_dirs']} 个")
|
||||
print(f" 清理文件: {results['cleaned_files']} 个")
|
||||
print(f" 释放空间: {self._format_size(results['freed_space'])}")
|
||||
|
||||
if results['errors']:
|
||||
print(f" 错误: {len(results['errors'])} 个")
|
||||
for error in results['errors']:
|
||||
print(f" {error}")
|
||||
|
||||
if not dry_run and results['freed_space'] > 0:
|
||||
print(f"\n✅ 清理完成！")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
parser = argparse.ArgumentParser(description="Deep2048项目清理工具")
|
||||
parser.add_argument("--dry-run", action="store_true", help="预览模式，不实际删除文件")
|
||||
parser.add_argument("--yes", "-y", action="store_true", help="自动确认，不询问")
|
||||
parser.add_argument("--project-root", help="项目根目录路径")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 确定项目根目录
|
||||
if args.project_root:
|
||||
project_root = Path(args.project_root)
|
||||
else:
|
||||
project_root = Path(__file__).parent.parent
|
||||
|
||||
if not project_root.exists():
|
||||
print(f"❌ 项目根目录不存在: {project_root}")
|
||||
return 1
|
||||
|
||||
# 创建清理器
|
||||
cleaner = ProjectCleaner(project_root)
|
||||
|
||||
try:
|
||||
# 执行清理
|
||||
results = cleaner.clean_project(
|
||||
dry_run=args.dry_run,
|
||||
interactive=not args.yes
|
||||
)
|
||||
|
||||
return 0 if not results['errors'] else 1
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n❌ 用户中断清理操作")
|
||||
return 1
|
||||
except Exception as e:
|
||||
print(f"❌ 清理过程中出现错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
Reference in New Issue
Block a user