一、QAT量化基本流程

QAT过程可以分解为以下步骤：

1. 定义模型：定义一个浮点模型，就像常规模型一样。
2. 定义量化模型：定义一个与原始模型结构相同但增加了量化操作（如torch.quantization.QuantStub()）和反量化操作（如torch.quantization.DeQuantStub()）的量化模型。
3. 准备数据：准备训练数据并将其量化为适当的位宽。
4. 训练模型：在训练过程中，使用量化模型进行正向和反向传递，并在每个 epoch 或 batch 结束时使用反量化操作计算精度损失。
5. 重新量化：在训练过程中，使用反量化操作重新量化模型参数，并使用新的量化参数继续训练。
6. Fine-tuning：训练结束后，使用fine-tuning技术进一步提高模型的准确率。

二、QAT量化代码示例

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.quantization import QuantStub, DeQuantStub, quantize_dynamic, prepare_qat, convert# 模型
class MyModel(nn.Module):def __init__(self):super(MyModel, self).__init__()# 量化self.quant = QuantStub()self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)self.relu = nn.ReLU(inplace=True)self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(128, 10)# 反量化self.dequant = DeQuantStub()def forward(self, x):# 量化x = self.quant(x)x = self.conv1(x)x = self.relu(x)x = self.conv2(x)x = self.relu(x)x = self.avgpool(x)x = x.view(x.size(0), -1)x = self.fc(x)# 反量化x = self.dequant(x)return x# 数据
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])])
train_data = datasets.CIFAR10(root='./data', train=True, download=True,transform=transform)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=1,shuffle=True, num_workers=0)# 模型 优化器
model = MyModel()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)# Prepare the model
model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
model = prepare_qat(model)# 训练
model.train()
for epoch in range(1):for i, (data, target) in enumerate(train_loader):optimizer.zero_grad()output = model(data)loss = nn.CrossEntropyLoss()(output, target)loss.backward()optimizer.step()if i % 100 == 0:print('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' %(epoch+1, 10, i+1, len(train_loader), loss.item()))# Re-quantize the modelmodel = quantize_dynamic(model, {'': torch.quantization.default_dynamic_qconfig}, dtype=torch.qint8)# 微调
model.eval()
for data, target in train_loader:model(data)
model = convert(model, inplace=True)