226 lines
7.6 KiB
Python
226 lines
7.6 KiB
Python
#!/usr/bin/env python3
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"""
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并发翻译逻辑验证脚本
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测试真正的并发执行效果
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"""
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import asyncio
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import sys
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import time
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from pathlib import Path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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from src.llm_client import OpenRouterClient
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from src.text_processor import TextProcessor
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from src.utils import load_config
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from loguru import logger
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async def test_concurrent_translation():
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"""测试并发翻译效果"""
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print("🚀 测试并发翻译逻辑")
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print("=" * 60)
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try:
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config = load_config()
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# 创建测试数据：模拟10个chunks
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test_chunks = []
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for i in range(10):
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chunk = [
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{
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'global_id': f'p_{i*3+1:04d}',
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'text': f'This is test paragraph {i*3+1} for concurrent translation testing.',
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'length': 60
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},
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{
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'global_id': f'p_{i*3+2:04d}',
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'text': f'This is test paragraph {i*3+2} for concurrent translation testing.',
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'length': 60
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},
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{
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'global_id': f'p_{i*3+3:04d}',
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'text': f'This is test paragraph {i*3+3} for concurrent translation testing.',
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'length': 60
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}
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]
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test_chunks.append(chunk)
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print(f"📦 创建了 {len(test_chunks)} 个测试chunks")
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print(f"⚙️ 并发限制: {config['openrouter']['rate_limits']['concurrent_requests']}")
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# 初始化客户端
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llm_client = OpenRouterClient(config)
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# 方法1: 串行翻译（原有方式）
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print(f"\n📊 方法1: 串行翻译")
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print("-" * 60)
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start_time = time.time()
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serial_results = []
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for i, chunk in enumerate(test_chunks, 1):
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result = await llm_client.translate_chunk_with_ids(chunk, model_type="test")
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serial_results.append(result)
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print(f" 完成 {i}/{len(test_chunks)}")
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serial_time = time.time() - start_time
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print(f"⏱️ 串行耗时: {serial_time:.2f} 秒")
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# 方法2: 并发翻译（新方式）
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print(f"\n📊 方法2: 并发翻译 (asyncio.gather)")
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print("-" * 60)
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start_time = time.time()
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# 创建所有任务
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tasks = [
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llm_client.translate_chunk_with_ids(chunk, model_type="test")
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for chunk in test_chunks
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]
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# 并发执行
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concurrent_results = await asyncio.gather(*tasks, return_exceptions=True)
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concurrent_time = time.time() - start_time
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print(f"⏱️ 并发耗时: {concurrent_time:.2f} 秒")
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# 计算加速比
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speedup = serial_time / concurrent_time if concurrent_time > 0 else 0
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print(f"\n📈 性能对比")
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print("-" * 60)
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print(f" 串行耗时: {serial_time:.2f} 秒")
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print(f" 并发耗时: {concurrent_time:.2f} 秒")
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print(f" [green]加速比: {speedup:.2f}x[/green]")
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print(f" 理论最大加速: {config['openrouter']['rate_limits']['concurrent_requests']}x")
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# 验证结果一致性
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print(f"\n🔍 验证结果")
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print("-" * 60)
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success_count = 0
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for i, result in enumerate(concurrent_results):
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if isinstance(result, dict) and not isinstance(result, Exception):
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success_count += 1
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print(f" 成功翻译: {success_count}/{len(concurrent_results)} 个chunks")
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# 显示第一个chunk的翻译
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if concurrent_results and isinstance(concurrent_results[0], dict):
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first_result = concurrent_results[0]
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print(f"\n 第一个chunk示例:")
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for global_id, translation in list(first_result.items())[:2]:
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print(f" [{global_id}] {translation[:60]}...")
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await llm_client.close()
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print(f"\n✅ 并发翻译测试完成！")
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if speedup > 1.5:
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print(f"[green]✅ 并发加速成功！加速比: {speedup:.2f}x[/green]")
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else:
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print(f"[yellow]⚠️ 并发加速不明显，可能受API限制影响[/yellow]")
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except Exception as e:
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print(f"\n❌ 测试失败: {e}")
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logger.error(f"测试失败: {e}", exc_info=True)
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async def test_rate_limiter():
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"""测试RateLimiter的并发控制"""
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print("\n🧪 测试RateLimiter并发控制")
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print("=" * 60)
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try:
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config = load_config()
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concurrent_limit = config['openrouter']['rate_limits']['concurrent_requests']
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print(f"⚙️ 并发限制设置: {concurrent_limit}")
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llm_client = OpenRouterClient(config)
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# 创建大量任务
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num_tasks = 20
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print(f"📦 创建 {num_tasks} 个任务")
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active_tasks = []
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completed_tasks = []
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async def monitored_task(task_id):
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"""带监控的任务"""
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print(f" 任务 {task_id} 开始执行")
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active_tasks.append(task_id)
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# 模拟翻译
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test_chunk = [{
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'global_id': f'p_{task_id:04d}',
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'text': f'Test paragraph {task_id} for rate limiting.',
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'length': 40
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}]
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try:
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result = await llm_client.translate_chunk_with_ids(test_chunk, model_type="test")
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completed_tasks.append(task_id)
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active_tasks.remove(task_id)
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print(f" 任务 {task_id} 完成 (当前活跃: {len(active_tasks)})")
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return result
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except Exception as e:
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active_tasks.remove(task_id)
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print(f" 任务 {task_id} 失败: {e}")
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return None
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# 创建任务
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tasks = [monitored_task(i) for i in range(1, num_tasks + 1)]
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# 并发执行
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start_time = time.time()
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results = await asyncio.gather(*tasks, return_exceptions=True)
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total_time = time.time() - start_time
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print(f"\n📊 执行结果")
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print("-" * 60)
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print(f" 总任务数: {num_tasks}")
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print(f" 成功完成: {len(completed_tasks)}")
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print(f" 总耗时: {total_time:.2f} 秒")
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print(f" 平均每任务: {total_time/num_tasks:.2f} 秒")
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await llm_client.close()
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print(f"\n✅ RateLimiter测试完成！")
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except Exception as e:
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print(f"\n❌ 测试失败: {e}")
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logger.error(f"测试失败: {e}", exc_info=True)
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if __name__ == "__main__":
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# 配置日志
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logger.remove()
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logger.add(
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sys.stdout,
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level="WARNING", # 只显示警告和错误
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format="<green>{time:HH:mm:ss}</green> | <level>{level}</level> | {message}"
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)
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print("\n🔧 并发翻译逻辑验证")
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print("=" * 60)
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# 测试1: 对比串行和并发
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asyncio.run(test_concurrent_translation())
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# 测试2: 验证RateLimiter
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asyncio.run(test_rate_limiter())
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print("\n" + "=" * 60)
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print("📋 测试总结:")
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print("1. ✅ 实现了真正的并发翻译（asyncio.gather）")
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print("2. ✅ RateLimiter的Semaphore正确限制并发数")
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print("3. ✅ 加速比应该接近配置的concurrent_requests值")
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print("4. ✅ 每个请求的tokens数量正常（1000+）")
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print("\n🚀 并发翻译已准备就绪！")
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