模型部署测试流程规范

1. 测试环境准备

# 安装必要的依赖包
pip install torch torchvision transformers accelerate
pip install onnxruntime onnx
pip install torch-tensorrt  # NVIDIA GPU使用

2. 模型量化测试流程

2.1 动态量化测试

import torch
from torch.quantization import quantize_dynamic

def test_dynamic_quantization(model, test_data):
    # 动态量化
    quantized_model = quantize_dynamic(
        model,
        {torch.nn.Linear},
        dtype=torch.qint8
    )
    
    # 性能测试
    import time
    start = time.time()
    for data in test_data:
        _ = quantized_model(data)
    end = time.time()
    print(f'动态量化推理时间: {end-start:.4f}s')
    return quantized_model

2.2 静态量化测试

import torch.quantization as quantization

def test_static_quantization(model, calib_data):
    # 设置量化配置
    model.eval()
    quantization.prepare(model, inplace=True)
    
    # 校准数据推理
    with torch.no_grad():
        for data in calib_data:
            model(data)
    
    # 转换为量化模型
    quantization.convert(model, inplace=True)
    return model

3. 模型剪枝测试

from torch.nn.utils import prune

def test_pruning(model, pruning_ratio=0.3):
    # 全局剪枝
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            prune.l1_unstructured(module, name='weight', amount=pruning_ratio)
    
    # 评估剪枝后性能
    model.eval()
    with torch.no_grad():
        total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print(f'剪枝后参数量: {total_params}')
    return model

4. 部署测试验证

4.1 性能指标记录

• 推理时间(ms)
• 内存占用(MB)
• 准确率保持率

4.2 复现步骤

1. 原始模型推理
2. 量化/剪枝处理
3. 部署测试环境搭建
4. 性能对比验证
5. 结果记录与分析