深度学习模型部署测试方案

深度学习模型部署测试方案

在大模型推理加速实践中，部署测试是确保性能优化效果的关键环节。以下是一个可复现的测试方案。

测试环境准备

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install onnxruntime-gpu
pip install transformers

模型量化测试示例

import torch
import torch.nn as nn
from torch.quantization import quantize_dynamic

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(768, 512)
        self.layer2 = nn.Linear(512, 256)
        self.layer3 = nn.Linear(256, 10)
    
    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = torch.relu(self.layer2(x))
        return self.layer3(x)

# 量化模型
model = SimpleModel()
quantized_model = quantize_dynamic(
    model, 
    {nn.Linear}, 
    dtype=torch.qint8
)

性能测试代码

import time
import torch

def benchmark_model(model, input_tensor, iterations=100):
    model.eval()
    with torch.no_grad():
        # 预热
        for _ in range(10):
            _ = model(input_tensor)
        
        # 测试
        start_time = time.time()
        for _ in range(iterations):
            _ = model(input_tensor)
        end_time = time.time()
        
        avg_time = (end_time - start_time) / iterations
        return avg_time

# 测试原始模型与量化模型
input_tensor = torch.randn(1, 768)
original_time = benchmark_model(model, input_tensor)
quantized_time = benchmark_model(quantized_model, input_tensor)

print(f"原始模型平均耗时: {original_time:.6f}s")
print(f"量化模型平均耗时: {quantized_time:.6f}s")

剪枝测试方案

from torch.nn.utils import prune

# 对线性层进行结构化剪枝
prune.l1_unstructured(model.layer1, name='weight', amount=0.3)
prune.ln_structured(model.layer2, name='weight', amount=0.4, n=2, p=1)

结果分析

通过上述测试可量化模型在不同优化策略下的性能提升，为实际部署决策提供数据支撑。