pytorch的Backward过程用时太长问题及解决

在PyTorch中，当我们使用反向传播算法进行模型训练时，有时会遇到Backward过程用时太长的问题。这个问题可能会导致训练时间过长，甚至无法完成训练。本文将提供一个完整的攻略，介绍如何解决这个问题。我们将提供两个示例，分别是使用梯度累积和使用半精度训练。

示例1：使用梯度累积

梯度累积是一种解决Backward过程用时太长问题的方法。它的基本思想是将一个batch的数据分成多个小batch，每个小batch计算一次梯度，然后将这些梯度累加起来，最后再进行一次参数更新。以下是一个示例，展示如何使用梯度累积解决Backward过程用时太长问题。

1. 导入库

import torch
import torch.nn as nn
import torch.optim as optim

2. 定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)

3. 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

4. 训练模型

num_epochs = 10
batch_size = 64
accumulation_steps = 4
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(trainloader, 0):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()

        if (i+1) % accumulation_steps == 0:
            optimizer.step()
            optimizer.zero_grad()

        running_loss += loss.item()
        if i % 2000 == 1999:    # 每2000个小批量数据打印一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

示例2：使用半精度训练

半精度训练是另一种解决Backward过程用时太长问题的方法。它的基本思想是使用半精度浮点数进行计算，从而减少计算量和内存占用。以下是一个示例，展示如何使用半精度训练解决Backward过程用时太长问题。

1. 导入库

import torch
import torch.nn as nn
import torch.optim as optim
from torch.cuda.amp import autocast, GradScaler

2. 定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)

3. 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
scaler = GradScaler()

4. 训练模型

num_epochs = 10
batch_size = 64
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(trainloader, 0):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        with autocast():
            outputs = model(inputs)
            loss = criterion(outputs, labels)

        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

        running_loss += loss.item()
        if i % 2000 == 1999:    # 每2000个小批量数据打印一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')