feat: 添加 GPU 需求分析和综合报告生成脚本
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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124
vsp/qwen3.5-9b/generate_report.py
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124
vsp/qwen3.5-9b/generate_report.py
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"""综合报告生成 - 汇总所有测试结果"""
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import json
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import os
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from datetime import datetime
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def load_json(path):
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"""加载 JSON 文件"""
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if os.path.exists(path):
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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return None
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def generate_report():
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"""生成综合测试报告"""
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results_dir = "vsp/qwen3.5-9b/results"
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speed = load_json(os.path.join(results_dir, "benchmark_speed.json"))
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accuracy = load_json(os.path.join(results_dir, "accuracy_results.json"))
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concurrency = load_json(os.path.join(results_dir, "concurrency_results.json"))
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gpu_req = load_json(os.path.join(results_dir, "gpu_requirements.json"))
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report_lines = [
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"# Qwen3.5-9B 性能测试报告",
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f"\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
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"\n## 1. 测试环境",
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"",
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"| 项目 | 值 |",
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"|------|-----|",
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"| 模型 | Qwen3.5-9B |",
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"| 量化方式 | 4-bit NF4 (bitsandbytes) |",
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]
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if speed and "memory" in speed:
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mem = speed["memory"]
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report_lines.extend([
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f"| GPU | {mem.get('gpu_name', 'N/A')} |",
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f"| GPU 显存 | {mem.get('gpu_total_gb', 'N/A')} GB |",
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f"| 模型显存占用 | {mem.get('gpu_allocated_gb', 'N/A')} GB |",
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f"| 系统内存占用 | {mem.get('ram_used_gb', 'N/A')} GB |",
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])
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# 推理速度
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report_lines.extend(["\n## 2. 推理速度", ""])
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if speed and "speed_benchmark" in speed:
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report_lines.extend([
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"| 测试场景 | 输入tokens | 输出tokens | 耗时(s) | 速度(tokens/s) |",
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"|---------|-----------|-----------|---------|---------------|",
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])
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for r in speed["speed_benchmark"]:
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report_lines.append(
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f"| {r['test_name']} | {r['input_tokens']} | {r['avg_output_tokens']} | {r['avg_time_s']} | {r['avg_tokens_per_sec']} |"
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)
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else:
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report_lines.append("*未运行速度测试*")
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# 精度
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report_lines.extend(["\n## 3. 精度评估", ""])
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if accuracy and "accuracy" in accuracy:
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acc = accuracy["accuracy"]
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report_lines.append(f"**总准确率: {acc['accuracy']}% ({acc['passed']}/{acc['total']})**\n")
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report_lines.extend([
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"| 分类 | 通过/总数 | 准确率 |",
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"|------|---------|--------|",
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])
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for cat, stats in acc.get("category_stats", {}).items():
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rate = stats["passed"] / stats["total"] * 100
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report_lines.append(f"| {cat} | {stats['passed']}/{stats['total']} | {rate:.0f}% |")
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else:
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report_lines.append("*未运行精度测试*")
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# 并发
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report_lines.extend(["\n## 4. 并发性能", ""])
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if concurrency and "concurrency_results" in concurrency:
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report_lines.extend([
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"| 并发数 | 总耗时(s) | 吞吐量(tokens/s) | 平均延迟(s) |",
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"|-------|---------|----------------|-----------|",
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])
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for r in concurrency["concurrency_results"]:
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report_lines.append(
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f"| {r['concurrency']} | {r['total_time_s']} | {r['throughput_tokens_per_sec']} | {r['avg_latency_s']} |"
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)
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report_lines.append(f"\n> 注: {concurrency.get('note', '单GPU串行推理')}")
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else:
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report_lines.append("*未运行并发测试*")
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# GPU 需求
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report_lines.extend(["\n## 5. GPU 算力需求", ""])
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if gpu_req:
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report_lines.extend([
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"| 精度 | 模型大小 | 最低显存 | 推荐显卡 |",
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"|------|---------|---------|---------|",
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])
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for precision, info in gpu_req.get("precision_requirements", {}).items():
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gpus = ", ".join(info["recommended_gpus"][:2])
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report_lines.append(
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f"| {precision} | {info['model_size_gb']}GB | {info['min_vram_gb']}GB | {gpus} |"
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)
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# 结论
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report_lines.extend([
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"\n## 6. 结论与建议",
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"",
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"1. **RTX 3050 8GB 可以运行 Qwen3.5-9B**，但必须使用 4-bit 量化",
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"2. 4-bit 量化后显存占用约 5GB，留有一定余量",
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"3. 单卡推理速度适合开发测试，不适合高并发生产环境",
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"4. 生产部署建议使用 RTX 4090 (FP16) 或 A100 (FP16/BF16) + vLLM",
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"5. 4-bit 量化对简单任务精度影响较小，复杂推理任务可能有一定损失",
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])
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# 保存报告
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report_text = "\n".join(report_lines)
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report_path = os.path.join(results_dir, "REPORT.md")
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with open(report_path, "w", encoding="utf-8") as f:
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f.write(report_text)
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print(report_text)
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print(f"\n\n报告已保存到 {report_path}")
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if __name__ == "__main__":
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os.chdir(os.path.dirname(os.path.abspath(__file__)) + "/../..")
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generate_report()
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90
vsp/qwen3.5-9b/gpu_requirements.py
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vsp/qwen3.5-9b/gpu_requirements.py
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"""GPU 算力需求分析"""
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import json
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import os
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# Qwen3.5-9B 不同精度下的显存需求估算
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GPU_REQUIREMENTS = {
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"model": "Qwen3.5-9B",
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"parameters": "9B",
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"precision_requirements": {
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"FP32": {
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"model_size_gb": 36,
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"min_vram_gb": 40,
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"recommended_gpus": ["A100 80GB", "H100 80GB"],
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"note": "不推荐，显存占用过大",
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},
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"FP16/BF16": {
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"model_size_gb": 18,
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"min_vram_gb": 22,
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"recommended_gpus": ["A100 40GB", "RTX 4090 24GB", "RTX A6000 48GB", "V100 32GB"],
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"note": "标准推理精度，推荐用于生产环境",
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},
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"INT8": {
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"model_size_gb": 9,
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"min_vram_gb": 12,
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"recommended_gpus": ["RTX 4070 Ti 16GB", "RTX 3090 24GB", "T4 16GB", "RTX 4080 16GB"],
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"note": "轻微精度损失，性价比高",
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},
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"INT4 (NF4)": {
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"model_size_gb": 5,
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"min_vram_gb": 8,
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"recommended_gpus": ["RTX 3050 8GB", "RTX 4060 8GB", "RTX 3060 12GB", "RTX 3070 8GB"],
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"note": "适合显存有限的消费级显卡，有一定精度损失",
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},
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},
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"deployment_recommendations": {
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"开发测试": {
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"gpu": "RTX 3050/4060 (8GB)",
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"precision": "INT4",
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"concurrent": 1,
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"cost_estimate": "~2000-3000 RMB (显卡)",
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},
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"小规模部署": {
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"gpu": "RTX 4090 (24GB)",
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"precision": "FP16",
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"concurrent": "2-4",
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"cost_estimate": "~12000-15000 RMB (显卡)",
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},
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"生产环境": {
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"gpu": "A100 40GB / H100 80GB",
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"precision": "FP16/BF16",
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"concurrent": "8-32 (vLLM)",
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"cost_estimate": "~60000-200000 RMB (显卡) 或云服务按需",
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},
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},
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}
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def analyze_gpu_requirements():
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"""输出 GPU 需求分析"""
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print("=" * 60)
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print("Qwen3.5-9B GPU 算力需求分析")
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print("=" * 60)
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for precision, info in GPU_REQUIREMENTS["precision_requirements"].items():
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print(f"\n{precision}:")
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print(f" 模型大小: ~{info['model_size_gb']} GB")
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print(f" 最低显存: {info['min_vram_gb']} GB")
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print(f" 推荐显卡: {', '.join(info['recommended_gpus'])}")
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print(f" 备注: {info['note']}")
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print(f"\n{'='*60}")
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print("部署方案推荐")
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print(f"{'='*60}")
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for scenario, info in GPU_REQUIREMENTS["deployment_recommendations"].items():
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print(f"\n{scenario}:")
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for k, v in info.items():
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print(f" {k}: {v}")
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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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path = os.path.join(output_dir, "gpu_requirements.json")
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with open(path, "w", encoding="utf-8") as f:
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json.dump(GPU_REQUIREMENTS, f, ensure_ascii=False, indent=2)
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print(f"\n结果已保存到 {path}")
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if __name__ == "__main__":
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analyze_gpu_requirements()
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