16337
682063abf1
- 模型加载改为 bitsandbytes 4-bit NF4 量化，device_map={"":0} 纯 GPU
- 关闭 Qwen3.5 thinking 模式 (enable_thinking=False)
- 精度从 60% 提升到 90%，推理速度 1-2 tokens/s
- GPU 显存 7.13GB/8GB，输出质量正常
- 更新所有测试结果和综合报告
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
"""并发压测 - 测试不同并发数下的性能表现"""
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import json
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import os
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import sys
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import glob
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import time
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import torch
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import threading
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from datetime import datetime
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from model_utils import load_model, apply_chat
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def single_inference(model, tokenizer, prompt, lock, max_tokens=64):
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"""单次推理（线程安全）"""
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messages = [{"role": "user", "content": prompt}]
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text = apply_chat(tokenizer, messages)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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input_len = inputs["input_ids"].shape[1]
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t0 = time.perf_counter()
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with lock: # GPU 推理需要串行（单 GPU）
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_tokens,
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do_sample=False,
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)
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elapsed = time.perf_counter() - t0
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output_len = outputs.shape[1] - input_len
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return {
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"time_s": elapsed,
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"output_tokens": output_len,
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"tokens_per_sec": output_len / elapsed if elapsed > 0 else 0,
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}
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def test_concurrency(model, tokenizer):
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"""测试不同并发数下的表现"""
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print("=" * 60)
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print("并发压测")
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print("=" * 60)
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prompts = [
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"什么是人工智能？",
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"请解释量子计算。",
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"Python的优点是什么？",
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"深度学习和机器学习的区别？",
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"什么是自然语言处理？",
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"解释一下GPT的工作原理。",
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"什么是强化学习？",
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"云计算的优势有哪些？",
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]
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concurrency_levels = [1, 2, 4, 8]
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lock = threading.Lock()
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results = []
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for n_concurrent in concurrency_levels:
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print(f"\n--- 并发数: {n_concurrent} ---")
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test_prompts = (prompts * ((n_concurrent // len(prompts)) + 1))[:n_concurrent]
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t0 = time.perf_counter()
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futures_results = []
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with ThreadPoolExecutor(max_workers=n_concurrent) as executor:
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futures = [
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executor.submit(single_inference, model, tokenizer, p, lock)
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for p in test_prompts
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]
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for f in as_completed(futures):
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futures_results.append(f.result())
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total_time = time.perf_counter() - t0
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total_tokens = sum(r["output_tokens"] for r in futures_results)
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avg_latency = sum(r["time_s"] for r in futures_results) / len(futures_results)
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throughput = total_tokens / total_time
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result = {
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"concurrency": n_concurrent,
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"total_time_s": round(total_time, 2),
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"total_tokens": total_tokens,
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"throughput_tokens_per_sec": round(throughput, 1),
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"avg_latency_s": round(avg_latency, 2),
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"requests_completed": len(futures_results),
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}
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results.append(result)
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print(f" 总耗时: {result['total_time_s']}s")
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print(f" 总 tokens: {result['total_tokens']}")
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print(f" 吞吐量: {result['throughput_tokens_per_sec']} tokens/s")
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print(f" 平均延迟: {result['avg_latency_s']}s")
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# 保存
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output_dir = "vsp/qwen3.5-9b/results"
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os.makedirs(output_dir, exist_ok=True)
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report = {
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"timestamp": datetime.now().isoformat(),
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"model": "Qwen3.5-9B",
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"quantization": "4-bit NF4",
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"note": "单GPU串行推理，并发测试主要体现请求排队效果",
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"concurrency_results": results,
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}
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path = os.path.join(output_dir, "concurrency_results.json")
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with open(path, "w", encoding="utf-8") as f:
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json.dump(report, f, ensure_ascii=False, indent=2)
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print(f"\n结果已保存到 {path}")
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return results
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if __name__ == "__main__":
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os.chdir(os.path.dirname(os.path.abspath(__file__)) + "/../..")
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model, tokenizer = load_model()
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test_concurrency(model, tokenizer)
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