模型量化对比测试：INT8 vs FLOAT16在不同硬件上的表现

INT8 vs FLOAT16 量化对比测试

测试环境

• GPU: RTX 3090
• CPU: Intel i7-12700K
• PyTorch版本: 2.0.1
• 模型: ResNet50 (预训练模型)

测试代码

import torch
import torch.nn as nn
from torch.quantization import quantize_dynamic, prepare, convert
from torch.utils.data import DataLoader
import time

# 加载模型
model = torchvision.models.resnet50(pretrained=True).eval()

def benchmark_model(model, data_loader, device='cuda'):
    model = model.to(device)
    total_time = 0
    with torch.no_grad():
        for i, (images, _) in enumerate(data_loader):
            images = images.to(device)
            start = time.time()
            output = model(images)
            end = time.time()
            total_time += (end - start)
            if i == 10:  # 只测试前10个batch
                break
    return total_time / 10  # 平均时间

# 准备数据集
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 创建测试数据集
val_dataset = torchvision.datasets.ImageFolder(root='path/to/imagenet/val', transform=transform)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)

# 原始模型测试
original_time = benchmark_model(model, val_loader)
print(f'原始FP32平均时间: {original_time:.4f}s')

# INT8量化
quantized_model = quantize_dynamic(
    model,
    {nn.Linear},
    dtype=torch.qint8
)
int8_time = benchmark_model(quantized_model, val_loader)
print(f'INT8平均时间: {int8_time:.4f}s')

# FLOAT16量化
half_model = model.half()
half_time = benchmark_model(half_model, val_loader)
print(f'FLOAT16平均时间: {half_time:.4f}s')

性能测试结果

硬件适配建议

• RTX 3090: INT8表现最优，精度损失最小
• T4 GPU: 推荐使用FLOAT16，兼容性更好
• CPU推理: FLOAT16优于INT8，因硬件支持更好

实战建议

1. 根据目标硬件选择量化方式
2. 量化后进行精度验证
3. 考虑模型部署场景的权衡