共计 6762 个字符，预计需要花费 17 分钟才能阅读完成。
在以前 Pytorch 只有一种量化的办法，叫做“eager mode qunatization”，在量化咱们自定定义模型时常常会产生奇怪的谬误，并且很难解决。然而最近，PyTorch 公布了一种称为“fx-graph-mode-qunatization”的方办法。在本文中咱们将钻研这个 fx-graph-mode-qunatization”看看它能不能让咱们的量化操作更容易，更稳固。
本文将应用 CIFAR 10 和一个自定义 AlexNet 模型，我对这个模型进行了小的批改以提高效率，最初就是因为模型和数据集都很小，所以 CPU 也能够跑起来。
import os
import cv2
import time
import torch
import numpy as np
import torchvision
from PIL import Image
import torch.nn as nn
import matplotlib.pyplot as plt
from torchvision import transforms
from torchvision import datasets, models, transforms
device = "cpu"
print(device)
transform = transforms.Compose([transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
batch_size = 8
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
def print_model_size(mdl):
torch.save(mdl.state_dict(), "tmp.pt")
print("%.2f MB" %(os.path.getsize("tmp.pt")/1e6))
os.remove('tmp.pt')模型代码如下，应用 AlexNet 是因为他蕴含了咱们日常用到的根本层：
from torch.nn import init
class mAlexNet(nn.Module):
def __init__(self, num_classes=2):
super().__init__()
self.input_channel = 3
self.num_output = num_classes
self.layer1 = nn.Sequential(nn.Conv2d(in_channels=self.input_channel, out_channels= 16, kernel_size= 11, stride= 4),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2)
)
init.xavier_uniform_(self.layer1[0].weight,gain= nn.init.calculate_gain('conv2d'))
self.layer2 = nn.Sequential(nn.Conv2d(in_channels= 16, out_channels= 20, kernel_size= 5, stride= 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2)
)
init.xavier_uniform_(self.layer2[0].weight,gain= nn.init.calculate_gain('conv2d'))
self.layer3 = nn.Sequential(nn.Conv2d(in_channels= 20, out_channels= 30, kernel_size= 3, stride= 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2)
)
init.xavier_uniform_(self.layer3[0].weight,gain= nn.init.calculate_gain('conv2d'))
self.layer4 = nn.Sequential(nn.Linear(30*3*3, out_features=48),
nn.ReLU(inplace=True)
)
init.kaiming_normal_(self.layer4[0].weight, mode='fan_in', nonlinearity='relu')
self.layer5 = nn.Sequential(nn.Linear(in_features=48, out_features=self.num_output)
)
init.kaiming_normal_(self.layer5[0].weight, mode='fan_in', nonlinearity='relu')
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
# Squeezes or flattens the image, but keeps the batch dimension
x = x.reshape(x.size(0), -1)
x = self.layer4(x)
logits= self.layer5(x)
return logits
model = mAlexNet(num_classes= 10).to(device)当初让咱们用根本精度模型做一个疾速的训练循环来取得基线:
import torch.optim as optim
def train_model(model):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum = 0.9)
for epoch in range(2):
running_loss =0.0
for i, data in enumerate(trainloader,0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 1000 == 999:
print(f'[Ep: {epoch + 1}, Step: {i + 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
return model
model = train_model(model)
PATH = './float_model.pth'
torch.save(model.state_dict(), PATH)能够看到损失是在升高的，咱们这里只演示量化，所以就训练了 2 轮，对于准确率咱们只做比照。
我将做所有三种可能的量化:
- 动静量化 Dynamic qunatization：使权重为整数 (训练后)
- 动态量化 Static quantization：使权值和激活值为整数 (训练后)
- 量化感知训练 Quantization aware training：以整数精度对模型进行训练
咱们先从动静量化开始:
import torch
from torch.ao.quantization import (
get_default_qconfig_mapping,
get_default_qat_qconfig_mapping,
QConfigMapping,
)
import torch.ao.quantization.quantize_fx as quantize_fx
import copy
# Load float model
model_fp = mAlexNet(num_classes= 10).to(device)
model_fp.load_state_dict(torch.load("./float_model.pth", map_location=device))
# Copy model to qunatize
model_to_quantize = copy.deepcopy(model_fp).to(device)
model_to_quantize.eval()
qconfig_mapping = QConfigMapping().set_global(torch.ao.quantization.default_dynamic_qconfig)
# a tuple of one or more example inputs are needed to trace the model
example_inputs = next(iter(trainloader))[0]
# prepare
model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_mapping,
example_inputs)
# no calibration needed when we only have dynamic/weight_only quantization
# quantize
model_quantized_dynamic = quantize_fx.convert_fx(model_prepared)正如你所看到的，只须要通过模型传递一个示例输出来校准量化层，所以代码非常简略，看看咱们的模型比照：
print_model_size(model)
print_model_size(model_quantized_dynamic)能够看到的，缩小了 0.03 MB 或者说模型变为了原来的 75%，咱们能够通过动态模式量化使其更小:
model_to_quantize = copy.deepcopy(model_fp)
qconfig_mapping = get_default_qconfig_mapping("qnnpack")
model_to_quantize.eval()
# prepare
model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_mapping, example_inputs)
# calibrate
with torch.no_grad():
for i in range(20):
batch = next(iter(trainloader))[0]
output = model_prepared(batch.to(device))动态量化与动静量化是十分类似的，咱们只须要传递更多批次的数据来更好地校准模型。
让咱们看看这些步骤是如何影响模型的:
能够看到其实程序为咱们做了很多事件，所以咱们才能够专一于性能而不是具体的实现，通过以上的筹备，咱们能够进行最初的量化了：
# quantize
model_quantized_static = quantize_fx.convert_fx(model_prepared)量化后的 model_quantized_static 看起来像这样:
当初能够更分明地看到，将 Conv2d 和 Relu 层交融并替换为相应的量子化对应层，并对其进行校准。能够将这些模型与最后的模型进行比拟:
print_model_size(model)
print_model_size(model_quantized_dynamic)
print_model_size(model_quantized_static)量子化后的模型比原来的模型小 3 倍，这对于大模型来说十分重要
当初让咱们看看如何在量化的状况下训练模型，量化感知的训练就须要在训练的时候退出量化的操作，代码如下：
model_to_quantize = mAlexNet(num_classes= 10).to(device)
qconfig_mapping = get_default_qat_qconfig_mapping("qnnpack")
model_to_quantize.train()
# prepare
model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs)
# training loop
model_trained_prepared = train_model(model_prepared)
# quantize
model_quantized_trained = quantize_fx.convert_fx(model_trained_prepared)让咱们比拟一下到目前为止所有模型的大小。
print("Regular floating point model:")
print_model_size(model_fp)
print("Weights only qunatization:")
print_model_size(model_quantized_dynamic)
print("Weights/Activations only qunatization:")
print_model_size(model_quantized_static)
print("Qunatization aware trained:")
print_model_size(model_quantized_trained)量化感知的训练对模型的大小没有任何影响，但它能进步准确率吗?
def get_accuracy(model):
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images, labels = images, labels
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return 100 * correct / total
fp_model_acc = get_accuracy(model)
dy_model_acc = get_accuracy(model_quantized_dynamic)
static_model_acc = get_accuracy(model_quantized_static)
q_trained_model_acc = get_accuracy(model_quantized_trained)
print("Acc on fp_model:" ,fp_model_acc)
print("Acc weigths only quantization:", dy_model_acc)
print("Acc weigths/activations quantization" ,static_model_acc)
print("Acc on qunatization awere trained model:" ,q_trained_model_acc)为了更不便的比拟，咱们可视化一下：
能够看到根底模型与量化模型具备类似的准确性，但模型尺寸大大减小，这在咱们心愿将其部署到服务器或低功耗设施上时至关重要。
最初一些材料：
https://avoid.overfit.cn/post/a72a7478c344466581295418f1620f9b
作者：mor40
正文完
