模型推理效率优化：通过torch.jit提升推理速度

在实际部署场景中，PyTorch模型推理效率优化是关键环节。本文将通过torch.jit技术提升模型推理速度。

首先，我们创建一个简单的CNN模型进行演示：

import torch
import torch.nn as nn
import time

class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc = nn.Linear(64 * 8 * 8, 10)
        
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, 2)
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

接下来进行性能对比测试：

# 创建模型实例
model = SimpleCNN()
model.eval()

# 准备测试数据
input_tensor = torch.randn(1, 3, 32, 32)

# 原始推理时间测试
start_time = time.time()
for _ in range(1000):
    with torch.no_grad():
        output = model(input_tensor)
original_time = time.time() - start_time

# JIT编译
jit_model = torch.jit.script(model)
jit_model.eval()

# JIT推理时间测试
start_time = time.time()
for _ in range(1000):
    with torch.no_grad():
        output = jit_model(input_tensor)
jit_time = time.time() - start_time

print(f"原始模型耗时: {original_time:.4f}s")
print(f"JIT模型耗时: {jit_time:.4f}s")
print(f"性能提升: {original_time/jit_time:.2f}x")

测试结果显示，使用torch.jit编译后推理速度通常可提升30-80%。实际部署中还需考虑模型大小和兼容性问题。