pytorch量化方法总结
参考文章：
https://zhuanlan.zhihu.com/p/144025236
https://zhuanlan.zhihu.com/p/352060808
https://pytorch.org/docs/stable/quantization.html
量化方法
-
Post Training Dynamic Quantization：这是最简单的一种量化方法，Post Training指的是在浮点模型训练收敛之后进行量化操作，其中weight被提前量化，而activation在前向推理过程中被动态量化，即每次都要根据实际运算的浮点数据范围每一层计算一次scale和zero_point，然后进行量化；
-
Post Training Static Quantization：第一种不是很常见，一般说的Post Training Quantization指的其实是这种静态的方法，而且这种方法是最常用的，其中weight跟上述一样也是被提前量化好的，然后activation也会基于之前校准过程中记录下的固定的scale和zero_point进行量化，整个过程不存在量化参数(scale和zero_point)的再计算；
-
Quantization Aware Training：对于一些模型在浮点训练+量化过程中精度损失比较严重的情况，就需要进行量化感知训练，即在训练过程中模拟量化过程，数据虽然都是表示为float32，但实际的值的间隔却会受到量化参数的限制。
量化目前支持两个后端：fbgemm（用于 x86， https://github.com/pytorch/FBGEMM）和 qnnpack（用于 ARM QNNPACK 库 https://github.com/pytorch/QNNPACK）。
FBGEMM编译安装
编译需要安装mkl库或者cblas库
以mkl库为例：
1.去以下网站下载intel mkl库
https://software.intel.com/content/www/us/en/develop/articles/oneapi-standalone-components.html#onemkl
2.可以临时配置一下库的路径
export LD_LIBRARY_PATH=/home/xywang/intel/oneapi/mkl/2021.2.0/lib/intel64/:$LD_LIBRARY_PATH
export PATH=/home/xywang/intel/oneapi/mkl/2021.2.0/bin:$PATH
3.编译FBGEMM
git clone --recursive https://github.com/pytorch/FBGEMM.git
cd FBGEMM
mkdir build && cd build
cmake ..
make
4.支持FBGEMM应该是要源码编译torch，否则会报错：quantized engine FBGEMM is not supported
测试速度demo
简单测试一下速度，qnnpack作为后端可能比较慢，因为会提示你硬件不支持：
[W NNPACK.cpp:80] Could not initialize NNPACK! Reason: Unsupported hardware.
import os
import time
import torch.nn as nn
from torch.quantization import QuantStub, DeQuantStub
backend = 'qnnpack'
# backend = 'fbgemm'
import torch
torch.backends.quantized.engine = backend
class DownBlockQ(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.quant_input = QuantStub()
self.dequant_output = DeQuantStub()
self.conv1 = nn.Conv2d(in_ch, in_ch, 4, stride=2, padding=1, groups=in_ch)
self.bn1 = nn.BatchNorm2d(in_ch)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(in_ch, out_ch, 1)
self.bn2 = nn.BatchNorm2d(out_ch)
self.relu2 = nn.ReLU()
def forward(self, x):
# x = self.quant_input(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
# x = self.dequant_output(x)
return x
def fuse_model(self):
torch.quantization.fuse_modules(self, ['conv1', 'bn1', 'relu1'], inplace=True)
torch.quantization.fuse_modules(self, ['conv2', 'bn2', 'relu2'], inplace=True)
class Model(nn.Module):
def __init__(self, filters=22):
super().__init__()
self.quant_input = QuantStub()
self.dequant_output = DeQuantStub()
self.db1 = DownBlockQ(filters * 1, filters * 2) # 128
self.db2 = DownBlockQ(filters * 2, filters * 4) # 64
self.db3 = DownBlockQ(filters * 4, filters * 8) # 32
def forward(self, x):
x = self.quant_input(x)
x = self.db1(x)
x = self.db2(x)
x = self.db3(x)
x = self.dequant_output(x)
return x
def fuse_model(model):
if hasattr(model, 'fuse_model'):
model.fuse_model()
for p in list(model.modules())[1:]:
fuse_model(p)
def print_size_of_model(model):
torch.save(model.state_dict(), "temp.p")
print('Size (MB):', os.path.getsize("temp.p") / 1e6)
os.remove('temp.p')
def benchmark(func, iters=10, *args):
t1 = time.time()
for _ in range(iters):
res = func(*args)
print(f'{((time.time() - t1) / iters):.6f} sec')
return res
def quantize():
dummy = torch.rand(1, 22, 256, 256)
# model = DownBlockQ(22 * 1, 22 * 2)
model = Model(filters=22)
model = model.eval()
print("Before quantization")
print_size_of_model(model)
benchmark(model, 20, dummy)
# print(model)
fuse_model(model)
model.qconfig = torch.quantization.get_default_qconfig(backend)
# print(model.qconfig)
torch.quantization.prepare(model, inplace=True)
torch.quantization.convert(model, inplace=True)
# print(model)
print("After quantization")
print_size_of_model(model)
benchmark(model, 20, dummy)
# torch.jit.script(model).save('models/model_scripted.pt')
if __name__ == '__main__':
quantize()
感知量化训练cifar10分类demo
感知量化训练需要修改网络（插入量化和反量化节点)，目前这个版本比较复杂，pytorch新出的fx模块对量化比较友好，参考：https://www.zhihu.com/question/447721553/answer/1838733801，以后有时间再试一下。
resnet.py
# resnet.py
# Modified from
# https://github.com/pytorch/vision/blob/release/0.8.0/torchvision/models/resnet.py
import torch
from torch import Tensor
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional
__all__ = [
'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2',
'wide_resnet101_2'
]
model_urls = {
'resnet18':
'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34':
'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50':
'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101':
'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152':
'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
'resnext50_32x4d':
'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d':
'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
'wide_resnet50_2':
'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
'wide_resnet101_2':
'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}
def conv3x3(in_planes: int,
out_planes: int,
stride: int = 1,
groups: int = 1,
dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError(
'BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError(
"Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
# Rename relu to relu1
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
self.skip_add = nn.quantized.FloatFunctional()
# Remember to use two independent ReLU for layer fusion.
self.relu2 = nn.ReLU(inplace=True)
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
# Use FloatFunctional for addition for quantization compatibility
# out += identity
# out = torch.add(identity, out)
out = self.skip_add.add(identity, out)
out = self.relu2(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu1 = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.skip_add = nn.quantized.FloatFunctional()
self.relu2 = nn.ReLU(inplace=True)
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
# out += identity
# out = torch.add(identity, out)
out = self.skip_add.add(identity, out)
out = self.relu2(out)
return out
class ResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(
replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight,
0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight,
0) # type: ignore[arg-type]
def _make_layer(self,
block: Type[Union[BasicBlock, Bottleneck]],
planes: int,
blocks: int,
stride: int = 1,
dilate: bool = False) -> nn.Sequential:
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _resnet(arch: str, block: Type[Union[BasicBlock,
Bottleneck]], layers: List[int],
pretrained: bool, progress: bool, **kwargs: Any) -> ResNet:
model = ResNet(block, layers, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model
def resnet18(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
**kwargs)
def resnet34(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet50(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet101(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained,
progress, **kwargs)
def resnet152(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained,
progress, **kwargs)
def resnext50_32x4d(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 4
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained,
progress, **kwargs)
def resnext101_32x8d(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" `_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained,
progress, **kwargs)
def wide_resnet50_2(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" `_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained,
progress, **kwargs)
def wide_resnet101_2(pretrained: bool = False,
progress: bool = True,
**kwargs: Any) -> ResNet:
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" `_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained,
progress, **kwargs)
train.py
import os
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import time
import copy
import numpy as np
from resnet import resnet18
torch.backends.quantized.engine = 'qnnpack'
def set_random_seeds(random_seed=0):
torch.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(random_seed)
random.seed(random_seed)
def prepare_dataloader(num_workers=8, train_batch_size=128, eval_batch_size=256):
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
# transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
train_set = torchvision.datasets.CIFAR10(
root="/data/xywang/dataset/", train=True, download=False, transform=train_transform)
# We will use test set for validation and test in this project.
# Do not use test set for validation in practice!
test_set = torchvision.datasets.CIFAR10(
root="/data/xywang/dataset/", train=False, download=False, transform=test_transform)
train_sampler = torch.utils.data.RandomSampler(train_set)
test_sampler = torch.utils.data.SequentialSampler(test_set)
train_loader = torch.utils.data.DataLoader(
dataset=train_set, batch_size=train_batch_size,
sampler=train_sampler, num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(
dataset=test_set, batch_size=eval_batch_size,
sampler=test_sampler, num_workers=num_workers)
return train_loader, test_loader
def evaluate_model(model, test_loader, device, criterion=None):
model.eval()
model.to(device)
running_loss = 0
running_corrects = 0
for inputs, labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
if criterion is not None:
loss = criterion(outputs, labels).item()
else:
loss = 0
# statistics
running_loss += loss * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
eval_loss = running_loss / len(test_loader.dataset)
# eval_accuracy = running_corrects / len(test_loader.dataset)
eval_accuracy = torch.true_divide(running_corrects, len(test_loader.dataset))
return eval_loss, eval_accuracy
def train_model(model, train_loader, test_loader, device, learning_rate=1e-1, num_epochs=200):
# The training configurations were not carefully selected.
criterion = nn.CrossEntropyLoss()
model.to(device)
# It seems that SGD optimizer is better than Adam optimizer for ResNet18 training on CIFAR10.
optimizer = optim.SGD(model.parameters(), lr=learning_rate,
momentum=0.9, weight_decay=1e-4)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=500)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[100, 150], gamma=0.1, last_epoch=-1)
# optimizer = optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
# Evaluation
model.eval()
eval_loss, eval_accuracy = evaluate_model(
model=model, test_loader=test_loader, device=device, criterion=criterion)
print("Epoch: {:02d} Eval Loss: {:.3f} Eval Acc: {:.3f}".format(-1,
eval_loss, eval_accuracy))
for epoch in range(num_epochs):
# Training
model.train()
running_loss = 0
running_corrects = 0
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
train_loss = running_loss / len(train_loader.dataset)
# train_accuracy = running_corrects / len(train_loader.dataset)
train_accuracy = torch.true_divide(running_corrects,len(train_loader.dataset))
# Evaluation
model.eval()
eval_loss, eval_accuracy = evaluate_model(
model=model, test_loader=test_loader, device=device, criterion=criterion)
# Set learning rate scheduler
scheduler.step()
print("Epoch: {:03d} Train Loss: {:.3f} Train Acc: {:.3f} Eval Loss: {:.3f} Eval Acc: {:.3f}".format(
epoch, train_loss, train_accuracy, eval_loss, eval_accuracy))
return model
def calibrate_model(model, loader, device=torch.device("cpu:0")):
model.to(device)
model.eval()
for inputs, labels in loader:
inputs = inputs.to(device)
labels = labels.to(device)
_ = model(inputs)
def measure_inference_latency(model, device, input_size=(1, 3, 32, 32), num_samples=100):
model.to(device)
model.eval()
x = torch.rand(size=input_size).to(device)
start_time = time.time()
for _ in range(num_samples):
_ = model(x)
end_time = time.time()
elapsed_time = end_time - start_time
elapsed_time_ave = elapsed_time / num_samples
return elapsed_time_ave
def save_model(model, model_dir, model_filename):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_filepath = os.path.join(model_dir, model_filename)
torch.save(model.state_dict(), model_filepath)
def load_model(model, model_filepath, device):
model.load_state_dict(torch.load(model_filepath, map_location=device))
return model
def save_torchscript_model(model, model_dir, model_filename):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_filepath = os.path.join(model_dir, model_filename)
torch.jit.save(torch.jit.script(model), model_filepath)
def load_torchscript_model(model_filepath, device):
model = torch.jit.load(model_filepath, map_location=device)
return model
def create_model(num_classes=10):
# The number of channels in ResNet18 is divisible by 8.
# This is required for fast GEMM integer matrix multiplication.
# model = torchvision.models.resnet18(pretrained=False)
model = resnet18(num_classes=num_classes, pretrained=False)
# We would use the pretrained ResNet18 as a feature extractor.
# for param in model.parameters():
# param.requires_grad = False
# Modify the last FC layer
# num_features = model.fc.in_features
# model.fc = nn.Linear(num_features, 10)
return model
class QuantizedResNet18(nn.Module):
def __init__(self, model_fp32):
super(QuantizedResNet18, self).__init__()
# QuantStub converts tensors from floating point to quantized.
# This will only be used for inputs.
self.quant = torch.quantization.QuantStub()
# DeQuantStub converts tensors from quantized to floating point.
# This will only be used for outputs.
self.dequant = torch.quantization.DeQuantStub()
# FP32 model
self.model_fp32 = model_fp32
def forward(self, x):
# manually specify where tensors will be converted from floating
# point to quantized in the quantized model
x = self.quant(x)
x = self.model_fp32(x)
# manually specify where tensors will be converted from quantized
# to floating point in the quantized model
x = self.dequant(x)
return x
def model_equivalence(model_1, model_2, device, rtol=1e-05, atol=1e-08, num_tests=100, input_size=(1, 3, 32, 32)):
model_1.to(device)
model_2.to(device)
for _ in range(num_tests):
x = torch.rand(size=input_size).to(device)
y1 = model_1(x).detach().cpu().numpy()
y2 = model_2(x).detach().cpu().numpy()
if np.allclose(a=y1, b=y2, rtol=rtol, atol=atol, equal_nan=False) == False:
print("Model equivalence test sample failed: ")
print(y1)
print(y2)
return False
return True
def main():
random_seed = 0
num_classes = 10
cuda_device = torch.device("cuda:0")
cpu_device = torch.device("cpu:0")
model_dir = "saved_models"
model_filename = "resnet18_cifar10.pt"
quantized_model_filename = "resnet18_quantized_cifar10.pt"
model_filepath = os.path.join(model_dir, model_filename)
quantized_model_filepath = os.path.join(
model_dir, quantized_model_filename)
set_random_seeds(random_seed=random_seed)
# Create an untrained model.
model = create_model(num_classes=num_classes)
train_loader, test_loader = prepare_dataloader(
num_workers=8, train_batch_size=128, eval_batch_size=256)
# Train model.
if not os.path.exists(model_filepath):
print("Training Model...")
model = train_model(model=model, train_loader=train_loader, test_loader=test_loader,
device=cuda_device, learning_rate=1e-1, num_epochs=200)
# Save model.
save_model(model=model, model_dir=model_dir, model_filename=model_filename)
else:
print('FP32 model already trained, directly load it.')
# Load a pretrained model.
model = load_model(
model=model, model_filepath=model_filepath, device=cuda_device)
# Move the model to CPU since static quantization does not support CUDA currently.
model.to(cpu_device)
# Make a copy of the model for layer fusion
fused_model = copy.deepcopy(model)
model.train()
# The model has to be switched to training mode before any layer fusion.
# Otherwise the quantization aware training will not work correctly.
fused_model.train()
# Fuse the model in place rather manually.
fused_model = torch.quantization.fuse_modules(
fused_model, [["conv1", "bn1", "relu"]], inplace=True)
for module_name, module in fused_model.named_children():
if "layer" in module_name:
for basic_block_name, basic_block in module.named_children():
torch.quantization.fuse_modules(
basic_block, [["conv1", "bn1", "relu1"], ["conv2", "bn2"]], inplace=True)
for sub_block_name, sub_block in basic_block.named_children():
if sub_block_name == "downsample":
torch.quantization.fuse_modules(
sub_block, [["0", "1"]], inplace=True)
# Print FP32 model.
#print(model)
# Print fused model.
#print(fused_model)
# Model and fused model should be equivalent.
model.eval()
fused_model.eval()
assert model_equivalence(model_1=model, model_2=fused_model, device=cpu_device, rtol=1e-03, atol=1e-06,
num_tests=100, input_size=(1, 3, 32, 32)), "Fused model is not equivalent to the original model!"
# Prepare the model for quantization aware training. This inserts observers in
# the model that will observe activation tensors during calibration.
quantized_model = QuantizedResNet18(model_fp32=fused_model)
# Using un-fused model will fail.
# Because there is no quantized layer implementation for a single batch normalization layer.
# quantized_model = QuantizedResNet18(model_fp32=model)
# Select quantization schemes from
# https://pytorch.org/docs/stable/quantization-support.html
quantization_config = torch.quantization.get_default_qconfig("qnnpack")
# Custom quantization configurations
# quantization_config = torch.quantization.default_qconfig
# quantization_config = torch.quantization.QConfig(activation=torch.quantization.MinMaxObserver.with_args(dtype=torch.quint8), weight=torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric))
quantized_model.qconfig = quantization_config
# Print quantization configurations
print(quantized_model.qconfig)
# https://pytorch.org/docs/stable/_modules/torch/quantization/quantize.html#prepare_qat
torch.quantization.prepare_qat(quantized_model, inplace=True)
# # Use training data for calibration.
print("Training QAT Model...")
quantized_model.train()
train_model(model=quantized_model, train_loader=train_loader,
test_loader=test_loader, device=cuda_device, learning_rate=1e-3, num_epochs=10)
quantized_model.to(cpu_device)
# Using high-level static quantization wrapper
# The above steps, including torch.quantization.prepare, calibrate_model, and torch.quantization.convert, are also equivalent to
#quantized_model = torch.quantization.quantize_qat(model=quantized_model, run_fn=train_model, run_args=[train_loader, test_loader, cuda_device], mapping=None, inplace=False)
quantized_model = torch.quantization.convert(quantized_model, inplace=True)
quantized_model.eval()
# Print quantized model.
print(quantized_model)
# Save quantized model.
save_torchscript_model(model=quantized_model, model_dir=model_dir,
model_filename=quantized_model_filename)
# Load quantized model.
quantized_jit_model = load_torchscript_model(
model_filepath=quantized_model_filepath, device=cpu_device)
_, fp32_eval_accuracy = evaluate_model(
model=model, test_loader=test_loader, device=cpu_device, criterion=None)
_, int8_eval_accuracy = evaluate_model(
model=quantized_jit_model, test_loader=test_loader, device=cpu_device, criterion=None)
# Skip this assertion since the values might deviate a lot.
# assert model_equivalence(model_1=model, model_2=quantized_jit_model, device=cpu_device, rtol=1e-01, atol=1e-02, num_tests=100, input_size=(1,3,32,32)), "Quantized model deviates from the original model too much!"
print("FP32 evaluation accuracy: {:.3f}".format(fp32_eval_accuracy))
print("INT8 evaluation accuracy: {:.3f}".format(int8_eval_accuracy))
fp32_cpu_inference_latency = measure_inference_latency(
model=model, device=cpu_device, input_size=(1, 3, 32, 32), num_samples=100)
int8_cpu_inference_latency = measure_inference_latency(
model=quantized_model, device=cpu_device, input_size=(1, 3, 32, 32), num_samples=100)
int8_jit_cpu_inference_latency = measure_inference_latency(
model=quantized_jit_model, device=cpu_device, input_size=(1, 3, 32, 32), num_samples=100)
fp32_gpu_inference_latency = measure_inference_latency(
model=model, device=cuda_device, input_size=(1, 3, 32, 32), num_samples=100)
print(
"FP32 CPU Inference Latency: {:.2f} ms / sample".format(fp32_cpu_inference_latency * 1000))
print(
"FP32 CUDA Inference Latency: {:.2f} ms / sample".format(fp32_gpu_inference_latency * 1000))
print(
"INT8 CPU Inference Latency: {:.2f} ms / sample".format(int8_cpu_inference_latency * 1000))
print("INT8 JIT CPU Inference Latency: {:.2f} ms / sample".format(
int8_jit_cpu_inference_latency * 1000))
if __name__ == "__main__":
main()