Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT¶
Overview¶
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.
## 1. Requirements Please install the required dependencies and import these libraries accordingly
[ ]:
!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
[1]:
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
2.1.0
## 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 
## 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 data directory. Data preprocessing is performed using torchvision transforms.
[2]:
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)
Files already downloaded and verified
Files already downloaded and verified
[3]:
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")
Define the VGG model that we are going to perfom QAT on.
[4]:
# CIFAR 10 has 10 classes
model = vgg16(num_classes=len(classes), init_weights=False)
model = model.cuda()
[5]:
# 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"]
[6]:
# 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")
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
## 4. Apply Quantization
quant_modules.initialize() 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, QuantConv2d, QuantLinear and QuantPooling will be called. QuantConv2d basically wraps quantizer nodes around inputs and weights of regular Conv2d. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A QuantConv2d is represented in
pytorch-quantization toolkit as follows.
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
[7]:
quant_modules.initialize()
[8]:
# All the regular conv, FC layers will be converted to their quantozed counterparts due to quant_modules.initialize()
qat_model = vgg16(num_classes=len(classes), init_weights=False)
qat_model = qat_model.cuda()
[9]:
# vgg16_base_ckpt is the checkpoint generated from Step 3 : Training a baseline VGG16 model.
ckpt = torch.load("./vgg16_base_ckpt")
modified_state_dict={}
for key, val in ckpt["model_state_dict"].items():
# Remove 'module.' from the key names
if key.startswith('module'):
modified_state_dict[key[7:]] = val
else:
modified_state_dict[key] = val
# Load the pre-trained checkpoint
qat_model.load_state_dict(modified_state_dict)
opt.load_state_dict(ckpt["opt_state_dict"])
## 5. Model Calibration
The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: max, histogram and entropy. We use max calibration technique as it is simple and effective.
[10]:
def compute_amax(model, **kwargs):
# Load calib result
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
if isinstance(module._calibrator, calib.MaxCalibrator):
module.load_calib_amax()
else:
module.load_calib_amax(**kwargs)
print(F"{name:40}: {module}")
model.cuda()
def collect_stats(model, data_loader, num_batches):
"""Feed data to the network and collect statistics"""
# Enable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
# Feed data to the network for collecting stats
for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):
model(image.cuda())
if i >= num_batches:
break
# Disable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.enable_quant()
module.disable_calib()
else:
module.enable()
def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir):
"""
Feed data to the network and calibrate.
Arguments:
model: classification model
model_name: name to use when creating state files
data_loader: calibration data set
num_calib_batch: amount of calibration passes to perform
calibrator: type of calibration to use (max/histogram)
hist_percentile: percentiles to be used for historgram calibration
out_dir: dir to save state files in
"""
if num_calib_batch > 0:
print("Calibrating model")
with torch.no_grad():
collect_stats(model, data_loader, num_calib_batch)
if not calibrator == "histogram":
compute_amax(model, method="max")
calib_output = os.path.join(
out_dir,
F"{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
else:
for percentile in hist_percentile:
print(F"{percentile} percentile calibration")
compute_amax(model, method="percentile")
calib_output = os.path.join(
out_dir,
F"{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
for method in ["mse", "entropy"]:
print(F"{method} calibration")
compute_amax(model, method=method)
calib_output = os.path.join(
out_dir,
F"{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
[11]:
#Calibrate the model using max calibration technique.
with torch.no_grad():
calibrate_model(
model=qat_model,
model_name="vgg16",
data_loader=training_dataloader,
num_calib_batch=32,
calibrator="max",
hist_percentile=[99.9, 99.99, 99.999, 99.9999],
out_dir="./")
Calibrating model
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WARNING: Logging before flag parsing goes to stderr.
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W1109 04:01:43.528834 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W1109 04:01:43.529163 139704147265344 tensor_quantizer.py:238] Call .cuda() if running on GPU after loading calibrated amax.
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W1109 04:01:43.553105 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([10, 1]).
features.0._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=2.7537 calibrator=MaxCalibrator scale=1.0 quant)
features.0._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0263, 2.7454](64) calibrator=MaxCalibrator scale=1.0 quant)
features.3._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=27.5676 calibrator=MaxCalibrator scale=1.0 quant)
features.3._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0169, 1.8204](64) calibrator=MaxCalibrator scale=1.0 quant)
features.7._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=15.2002 calibrator=MaxCalibrator scale=1.0 quant)
features.7._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0493, 1.3207](128) calibrator=MaxCalibrator scale=1.0 quant)
features.10._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=7.7376 calibrator=MaxCalibrator scale=1.0 quant)
features.10._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0163, 0.9624](128) calibrator=MaxCalibrator scale=1.0 quant)
features.14._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=8.8351 calibrator=MaxCalibrator scale=1.0 quant)
features.14._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0622, 0.8791](256) calibrator=MaxCalibrator scale=1.0 quant)
features.17._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=12.5746 calibrator=MaxCalibrator scale=1.0 quant)
features.17._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0505, 0.5117](256) calibrator=MaxCalibrator scale=1.0 quant)
features.20._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=9.7203 calibrator=MaxCalibrator scale=1.0 quant)
features.20._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0296, 0.5335](256) calibrator=MaxCalibrator scale=1.0 quant)
features.24._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=8.9367 calibrator=MaxCalibrator scale=1.0 quant)
features.24._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0220, 0.3763](512) calibrator=MaxCalibrator scale=1.0 quant)
features.27._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=6.6539 calibrator=MaxCalibrator scale=1.0 quant)
features.27._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0151, 0.1777](512) calibrator=MaxCalibrator scale=1.0 quant)
features.30._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=3.7099 calibrator=MaxCalibrator scale=1.0 quant)
features.30._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0087, 0.1906](512) calibrator=MaxCalibrator scale=1.0 quant)
features.34._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=4.0491 calibrator=MaxCalibrator scale=1.0 quant)
features.34._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0106, 0.1971](512) calibrator=MaxCalibrator scale=1.0 quant)
features.37._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=2.1531 calibrator=MaxCalibrator scale=1.0 quant)
features.37._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0070, 0.2305](512) calibrator=MaxCalibrator scale=1.0 quant)
features.40._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=3.3631 calibrator=MaxCalibrator scale=1.0 quant)
features.40._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0023, 0.4726](512) calibrator=MaxCalibrator scale=1.0 quant)
avgpool._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)
classifier.0._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)
classifier.0._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0026, 0.5320](4096) calibrator=MaxCalibrator scale=1.0 quant)
classifier.3._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=6.6733 calibrator=MaxCalibrator scale=1.0 quant)
classifier.3._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0018, 0.5172](4096) calibrator=MaxCalibrator scale=1.0 quant)
classifier.6._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=9.4352 calibrator=MaxCalibrator scale=1.0 quant)
classifier.6._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)
## 6. Quantization Aware Training
In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an
annealing learning-rate. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. During finetuning with QAT, the quantization is applied as a composition of max, clamp, round and mul ops.
# amax is absolute maximum value for an input
# The upper bound for integer quantization (127 for int8)
max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device)
scale = max_bound / amax
outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound)
tensor_quant function in pytorch_quantization toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network. During inference, we use torch.fake_quantize_per_tensor_affine and torch.fake_quantize_per_channel_affine to perform quantization as this is easier to convert into corresponding TensorRT operators. Please refer to next sections for more details on
how these operators are exported in torchscript and converted in Torch-TensorRT.
[12]:
# Finetune the QAT model for 1 epoch
num_epochs=1
for epoch in range(num_epochs):
adjust_lr(opt, epoch)
print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state["lr"]))
train(qat_model, training_dataloader, crit, opt, epoch)
test_loss, test_acc = test(qat_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': qat_model.state_dict(),
'acc': test_acc,
'opt_state_dict': opt.state_dict(),
'state': state},
ckpt_path="vgg16_qat_ckpt")
Updating learning rate: 0.1
Epoch: [ 1 / 1] LR: 0.100000
Batch: [ 500 | 1563] loss: 2.635
Batch: [ 1000 | 1563] loss: 2.655
Batch: [ 1500 | 1563] loss: 2.646
Test Loss: 0.03291 Test Acc: 82.98%
Checkpoint saved
## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to https://pytorch.org/docs/stable/jit.html. Setting quant_nn.TensorQuantizer.use_fb_fake_quant = True enables the QAT model to use torch.fake_quantize_per_tensor_affine and torch.fake_quantize_per_channel_affine operators instead of tensor_quant function to export quantization operators. In torchscript, they
are represented as aten::fake_quantize_per_tensor_affine and aten::fake_quantize_per_channel_affine.
[13]:
quant_nn.TensorQuantizer.use_fb_fake_quant = True
with torch.no_grad():
data = iter(testing_dataloader)
images, _ = data.next()
jit_model = torch.jit.trace(qat_model, images.to("cuda"))
torch.jit.save(jit_model, "trained_vgg16_qat.jit.pt")
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W1109 04:02:37.268160 139704147265344 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.
/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
inputs, amax.item() / bound, 0,
/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])
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E1109 04:02:37.488416 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; QuantizeLayer and DequantizeLayer. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set
torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug). For example, QuantConv2d layer from pytorch_quantization toolkit is represented as follows in Torchscript
%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)
%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)
%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)
aten::fake_quantize_per_*_affine is converted into QuantizeLayer + DequantizeLayer in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT.
[14]:
qat_model = torch.jit.load("trained_vgg16_qat.jit.pt").eval()
compile_spec = {"inputs": [torch_tensorrt.Input([16, 3, 32, 32])],
"enabled_precisions": torch.int8,
}
trt_mod = torch_tensorrt.compile(qat_model, **compile_spec)
test_loss, test_acc = test(trt_mod, testing_dataloader, crit, 0)
print("VGG QAT accuracy using TensorRT: {:.2f}%".format(100 * test_acc))
WARNING: [Torch-TensorRT] - Cannot infer input type from calcuations in graph for input x.2. Assuming it is Float32. If not, specify input type explicity
WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
WARNING: [Torch-TensorRT TorchScript Conversion Context] - Detected invalid timing cache, setup a local cache instead
VGG QAT accuracy using TensorRT: 82.97%
Performance benchmarking¶
[15]:
import time
import numpy as np
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
# Helper function to benchmark the model
def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):
input_data = torch.randn(input_shape)
input_data = input_data.to("cuda")
if dtype=='fp16':
input_data = input_data.half()
print("Warm up ...")
with torch.no_grad():
for _ in range(nwarmup):
features = model(input_data)
torch.cuda.synchronize()
print("Start timing ...")
timings = []
with torch.no_grad():
for i in range(1, nruns+1):
start_time = time.time()
output = model(input_data)
torch.cuda.synchronize()
end_time = time.time()
timings.append(end_time - start_time)
if i%100==0:
print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))
print("Input shape:", input_data.size())
print("Output shape:", output.shape)
print('Average batch time: %.2f ms'%(np.mean(timings)*1000))
[16]:
benchmark(jit_model, input_shape=(16, 3, 32, 32))
Warm up ...
Start timing ...
Iteration 100/1000, avg batch time 4.83 ms
Iteration 200/1000, avg batch time 4.83 ms
Iteration 300/1000, avg batch time 4.83 ms
Iteration 400/1000, avg batch time 4.83 ms
Iteration 500/1000, avg batch time 4.83 ms
Iteration 600/1000, avg batch time 4.83 ms
Iteration 700/1000, avg batch time 4.83 ms
Iteration 800/1000, avg batch time 4.83 ms
Iteration 900/1000, avg batch time 4.83 ms
Iteration 1000/1000, avg batch time 4.83 ms
Input shape: torch.Size([16, 3, 32, 32])
Output shape: torch.Size([16, 10])
Average batch time: 4.83 ms
[17]:
benchmark(trt_mod, input_shape=(16, 3, 32, 32))
Warm up ...
Start timing ...
Iteration 100/1000, avg batch time 1.87 ms
Iteration 200/1000, avg batch time 1.84 ms
Iteration 300/1000, avg batch time 1.85 ms
Iteration 400/1000, avg batch time 1.83 ms
Iteration 500/1000, avg batch time 1.82 ms
Iteration 600/1000, avg batch time 1.81 ms
Iteration 700/1000, avg batch time 1.81 ms
Iteration 800/1000, avg batch time 1.80 ms
Iteration 900/1000, avg batch time 1.80 ms
Iteration 1000/1000, avg batch time 1.79 ms
Input shape: torch.Size([16, 3, 32, 32])
Output shape: torch.Size([16, 10])
Average batch time: 1.79 ms
## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics
