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<section id="Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT">
<h1>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT" title="Permalink to this headline">¶</a></h1>
<section id="Overview">
<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
<p>Quantization Aware training (QAT) simulates quantization during training by quantizing weights and activation layers. This will help to reduce the loss in accuracy when we convert the network trained in FP32 to INT8 for faster inference. QAT introduces additional nodes in the graph which will be used to learn the dynamic ranges of weights and activation layers. In this notebook, we illustrate the following steps from training to inference of a QAT model in Torch-TensorRT.</p>
<ol class="arabic simple">
<li><p><a class="reference external" href="#1">Requirements</a></p></li>
<li><p><a class="reference external" href="#2">VGG16 Overview</a></p></li>
<li><p><a class="reference external" href="#3">Training a baseline VGG16 model</a></p></li>
<li><p><a class="reference external" href="#4">Apply Quantization</a></p></li>
<li><p><a class="reference external" href="#5">Model calibration</a></p></li>
<li><p><a class="reference external" href="#6">Quantization Aware training</a></p></li>
<li><p><a class="reference external" href="#7">Export to Torchscript</a></p></li>
<li><p><a class="reference external" href="#8">Inference using Torch-TensorRT</a></p></li>
<li><p><a class="reference external" href="#8">References</a></p></li>
</ol>
<p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch_tensorrt
from torch.utils.tensorboard import SummaryWriter
import pytorch_quantization
from pytorch_quantization import nn as quant_nn
from pytorch_quantization import quant_modules
from pytorch_quantization.tensor_quant import QuantDescriptor
from pytorch_quantization import calib
from tqdm import tqdm
print(pytorch_quantization.__version__)
import os
import sys
sys.path.insert(0, "../examples/int8/training/vgg16")
from vgg16 import vgg16
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2.1.0
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<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="78a60edfa3614a2286f59fdac891c4d2" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
<p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# ========== Define Training dataset and dataloaders =============#
training_dataset = datasets.CIFAR10(root='./data',
train=True,
download=True,
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)),
]))
training_dataloader = torch.utils.data.DataLoader(training_dataset,
batch_size=32,
shuffle=True,
num_workers=2)
# ========== Define Testing dataset and dataloaders =============#
testing_dataset = datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]))
testing_dataloader = torch.utils.data.DataLoader(testing_dataset,
batch_size=16,
shuffle=False,
num_workers=2)
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Files already downloaded and verified
Files already downloaded and verified
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def train(model, dataloader, crit, opt, epoch):
# global writer
model.train()
running_loss = 0.0
for batch, (data, labels) in enumerate(dataloader):
data, labels = data.cuda(), labels.cuda(non_blocking=True)
opt.zero_grad()
out = model(data)
loss = crit(out, labels)
loss.backward()
opt.step()
running_loss += loss.item()
if batch % 500 == 499:
print("Batch: [%5d | %5d] loss: %.3f" % (batch + 1, len(dataloader), running_loss / 100))
running_loss = 0.0
def test(model, dataloader, crit, epoch):
global writer
global classes
total = 0
correct = 0
loss = 0.0
class_probs = []
class_preds = []
model.eval()
with torch.no_grad():
for data, labels in dataloader:
data, labels = data.cuda(), labels.cuda(non_blocking=True)
out = model(data)
loss += crit(out, labels)
preds = torch.max(out, 1)[1]
class_probs.append([F.softmax(i, dim=0) for i in out])
class_preds.append(preds)
total += labels.size(0)
correct += (preds == labels).sum().item()
test_probs = torch.cat([torch.stack(batch) for batch in class_probs])
test_preds = torch.cat(class_preds)
return loss / total, correct / total
def save_checkpoint(state, ckpt_path="checkpoint.pth"):
torch.save(state, ckpt_path)
print("Checkpoint saved")
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<p><em>Define the VGG model that we are going to perfom QAT on.</em></p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># CIFAR 10 has 10 classes
model = vgg16(num_classes=len(classes), init_weights=False)
model = model.cuda()
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Declare Learning rate
lr = 0.1
state = {}
state["lr"] = lr
# Use cross entropy loss for classification and SGD optimizer
crit = nn.CrossEntropyLoss()
opt = optim.SGD(model.parameters(), lr=state["lr"], momentum=0.9, weight_decay=1e-4)
# Adjust learning rate based on epoch number
def adjust_lr(optimizer, epoch):
global state
new_lr = lr * (0.5**(epoch // 12)) if state["lr"] > 1e-7 else state["lr"]
if new_lr != state["lr"]:
state["lr"] = new_lr
print("Updating learning rate: {}".format(state["lr"]))
for param_group in optimizer.param_groups:
param_group["lr"] = state["lr"]
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Train the model for 25 epochs to get ~80% accuracy.
num_epochs=25
for epoch in range(num_epochs):
adjust_lr(opt, epoch)
print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state["lr"]))
train(model, training_dataloader, crit, opt, epoch)
test_loss, test_acc = test(model, testing_dataloader, crit, epoch)
print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(test_loss, 100 * test_acc))
save_checkpoint({'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'acc': test_acc,
'opt_state_dict': opt.state_dict(),
'state': state},
ckpt_path="vgg16_base_ckpt")
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Epoch: [ 1 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 13.288
Batch: [ 1000 | 1563] loss: 11.345
Batch: [ 1500 | 1563] loss: 11.008
Test Loss: 0.13388 Test Acc: 13.23%
Epoch: [ 2 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 10.742
Batch: [ 1000 | 1563] loss: 10.311
Batch: [ 1500 | 1563] loss: 10.141
Test Loss: 0.11888 Test Acc: 23.96%
Epoch: [ 3 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.877
Batch: [ 1000 | 1563] loss: 9.821
Batch: [ 1500 | 1563] loss: 9.818
Test Loss: 0.11879 Test Acc: 24.68%
Epoch: [ 4 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.677
Batch: [ 1000 | 1563] loss: 9.613
Batch: [ 1500 | 1563] loss: 9.504
Test Loss: 0.11499 Test Acc: 23.68%
Epoch: [ 5 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.560
Batch: [ 1000 | 1563] loss: 9.536
Batch: [ 1500 | 1563] loss: 9.309
Test Loss: 0.10990 Test Acc: 27.84%
Epoch: [ 6 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.254
Batch: [ 1000 | 1563] loss: 9.234
Batch: [ 1500 | 1563] loss: 9.188
Test Loss: 0.11594 Test Acc: 23.29%
Epoch: [ 7 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.141
Batch: [ 1000 | 1563] loss: 9.110
Batch: [ 1500 | 1563] loss: 9.013
Test Loss: 0.10732 Test Acc: 29.24%
Epoch: [ 8 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.120
Batch: [ 1000 | 1563] loss: 9.086
Batch: [ 1500 | 1563] loss: 8.948
Test Loss: 0.10732 Test Acc: 27.24%
Epoch: [ 9 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.941
Batch: [ 1000 | 1563] loss: 8.997
Batch: [ 1500 | 1563] loss: 9.028
Test Loss: 0.11299 Test Acc: 25.52%
Epoch: [ 10 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.927
Batch: [ 1000 | 1563] loss: 8.837
Batch: [ 1500 | 1563] loss: 8.860
Test Loss: 0.10130 Test Acc: 34.61%
Epoch: [ 11 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.953
Batch: [ 1000 | 1563] loss: 8.738
Batch: [ 1500 | 1563] loss: 8.724
Test Loss: 0.10018 Test Acc: 32.27%
Epoch: [ 12 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.721
Batch: [ 1000 | 1563] loss: 8.716
Batch: [ 1500 | 1563] loss: 8.701
Test Loss: 0.10070 Test Acc: 29.57%
Updating learning rate: 0.05
Epoch: [ 13 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 7.944
Batch: [ 1000 | 1563] loss: 7.649
Batch: [ 1500 | 1563] loss: 7.511
Test Loss: 0.08555 Test Acc: 44.62%
Epoch: [ 14 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 7.057
Batch: [ 1000 | 1563] loss: 6.944
Batch: [ 1500 | 1563] loss: 6.687
Test Loss: 0.08331 Test Acc: 52.27%
Epoch: [ 15 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 6.470
Batch: [ 1000 | 1563] loss: 6.439
Batch: [ 1500 | 1563] loss: 6.126
Test Loss: 0.07266 Test Acc: 58.02%
Epoch: [ 16 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.834
Batch: [ 1000 | 1563] loss: 5.801
Batch: [ 1500 | 1563] loss: 5.622
Test Loss: 0.06340 Test Acc: 65.17%
Epoch: [ 17 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.459
Batch: [ 1000 | 1563] loss: 5.442
Batch: [ 1500 | 1563] loss: 5.314
Test Loss: 0.05945 Test Acc: 67.22%
Epoch: [ 18 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.071
Batch: [ 1000 | 1563] loss: 5.145
Batch: [ 1500 | 1563] loss: 5.063
Test Loss: 0.06567 Test Acc: 64.46%
Epoch: [ 19 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.796
Batch: [ 1000 | 1563] loss: 4.781
Batch: [ 1500 | 1563] loss: 4.732
Test Loss: 0.05374 Test Acc: 71.87%
Epoch: [ 20 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.568
Batch: [ 1000 | 1563] loss: 4.564
Batch: [ 1500 | 1563] loss: 4.484
Test Loss: 0.05311 Test Acc: 71.12%
Epoch: [ 21 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.385
Batch: [ 1000 | 1563] loss: 4.302
Batch: [ 1500 | 1563] loss: 4.285
Test Loss: 0.05080 Test Acc: 74.29%
Epoch: [ 22 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.069
Batch: [ 1000 | 1563] loss: 4.105
Batch: [ 1500 | 1563] loss: 4.096
Test Loss: 0.04807 Test Acc: 75.20%
Epoch: [ 23 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 3.959
Batch: [ 1000 | 1563] loss: 3.898
Batch: [ 1500 | 1563] loss: 3.916
Test Loss: 0.04743 Test Acc: 75.81%
Epoch: [ 24 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 3.738
Batch: [ 1000 | 1563] loss: 3.847
Batch: [ 1500 | 1563] loss: 3.797
Test Loss: 0.04609 Test Acc: 76.42%
Updating learning rate: 0.025
Epoch: [ 25 / 25] LR: 0.025000
Batch: [ 500 | 1563] loss: 2.952
Batch: [ 1000 | 1563] loss: 2.906
Batch: [ 1500 | 1563] loss: 2.735
Test Loss: 0.03466 Test Acc: 82.00%
Checkpoint saved
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<p>## 4. Apply Quantization</p>
<p><code class="docutils literal notranslate"><span class="pre">quant_modules.initialize()</span></code> will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code>, <code class="docutils literal notranslate"><span class="pre">QuantLinear</span></code> and <code class="docutils literal notranslate"><span class="pre">QuantPooling</span></code> will be called. <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> basically wraps quantizer nodes around inputs and weights of regular <code class="docutils literal notranslate"><span class="pre">Conv2d</span></code>. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> is represented in
<code class="docutils literal notranslate"><span class="pre">pytorch-quantization</span></code> toolkit as follows.</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def forward(self, input):
# the actual quantization happens in the next level of the class hierarchy
quant_input, quant_weight = self._quant(input)
if self.padding_mode == 'circular':
expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
(self.padding[0] + 1) // 2, self.padding[0] // 2)
output = F.conv2d(F.pad(quant_input, expanded_padding, mode='circular'),
quant_weight, self.bias, self.stride,
_pair(0), self.dilation, self.groups)
else:
output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,
self.groups)
return output
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>quant_modules.initialize()
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