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__version__: "2.4.0.dev0" | ||
__cuda_version__: "12.1" | ||
__tensorrt_version__: "10.0.1" | ||
__tensorrt_version__: "10.0.1" |
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""" | ||
.. _vgg16_fp8_ptq: | ||
Torch Compile VGG16 with FP8 and PTQ | ||
====================================================== | ||
This script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a VGG16 model with FP8 and PTQ. | ||
""" | ||
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# %% | ||
# Imports and Model Definition | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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import argparse | ||
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import modelopt.torch.quantization as mtq | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import torch_tensorrt as torchtrt | ||
import torchvision.datasets as datasets | ||
import torchvision.transforms as transforms | ||
from modelopt.torch.quantization.utils import export_torch_mode | ||
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class VGG(nn.Module): | ||
def __init__(self, layer_spec, num_classes=1000, init_weights=False): | ||
super(VGG, self).__init__() | ||
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layers = [] | ||
in_channels = 3 | ||
for l in layer_spec: | ||
if l == "pool": | ||
layers.append(nn.MaxPool2d(kernel_size=2, stride=2)) | ||
else: | ||
layers += [ | ||
nn.Conv2d(in_channels, l, kernel_size=3, padding=1), | ||
nn.BatchNorm2d(l), | ||
nn.ReLU(), | ||
] | ||
in_channels = l | ||
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self.features = nn.Sequential(*layers) | ||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | ||
self.classifier = nn.Sequential( | ||
nn.Linear(512 * 1 * 1, 4096), | ||
nn.ReLU(), | ||
nn.Dropout(), | ||
nn.Linear(4096, 4096), | ||
nn.ReLU(), | ||
nn.Dropout(), | ||
nn.Linear(4096, num_classes), | ||
) | ||
if init_weights: | ||
self._initialize_weights() | ||
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def _initialize_weights(self): | ||
for m in self.modules(): | ||
if isinstance(m, nn.Conv2d): | ||
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | ||
if m.bias is not None: | ||
nn.init.constant_(m.bias, 0) | ||
elif isinstance(m, nn.BatchNorm2d): | ||
nn.init.constant_(m.weight, 1) | ||
nn.init.constant_(m.bias, 0) | ||
elif isinstance(m, nn.Linear): | ||
nn.init.normal_(m.weight, 0, 0.01) | ||
nn.init.constant_(m.bias, 0) | ||
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def forward(self, x): | ||
x = self.features(x) | ||
x = self.avgpool(x) | ||
x = torch.flatten(x, 1) | ||
x = self.classifier(x) | ||
return x | ||
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def vgg16(num_classes=1000, init_weights=False): | ||
vgg16_cfg = [ | ||
64, | ||
64, | ||
"pool", | ||
128, | ||
128, | ||
"pool", | ||
256, | ||
256, | ||
256, | ||
"pool", | ||
512, | ||
512, | ||
512, | ||
"pool", | ||
512, | ||
512, | ||
512, | ||
"pool", | ||
] | ||
return VGG(vgg16_cfg, num_classes, init_weights) | ||
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PARSER = argparse.ArgumentParser( | ||
description="Load pre-trained VGG model and then tune with FP8 and PTQ" | ||
) | ||
PARSER.add_argument( | ||
"--ckpt", type=str, required=True, help="Path to the pre-trained checkpoint" | ||
) | ||
PARSER.add_argument( | ||
"--batch-size", | ||
default=128, | ||
type=int, | ||
help="Batch size for tuning the model with PTQ and FP8", | ||
) | ||
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args = PARSER.parse_args() | ||
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model = vgg16(num_classes=10, init_weights=False) | ||
model = model.cuda() | ||
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# %% | ||
# Load the pre-trained model weights | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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ckpt = torch.load(args.ckpt) | ||
weights = ckpt["model_state_dict"] | ||
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if torch.cuda.device_count() > 1: | ||
from collections import OrderedDict | ||
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new_state_dict = OrderedDict() | ||
for k, v in weights.items(): | ||
name = k[7:] # remove `module.` | ||
new_state_dict[name] = v | ||
weights = new_state_dict | ||
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model.load_state_dict(weights) | ||
# Don't forget to set the model to evaluation mode! | ||
model.eval() | ||
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# %% | ||
# Load training dataset and define loss function for PTQ | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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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=args.batch_size, shuffle=True, num_workers=2 | ||
) | ||
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data = iter(training_dataloader) | ||
images, _ = next(data) | ||
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crit = nn.CrossEntropyLoss() | ||
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# %% | ||
# Define Calibration Loop for quantization | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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def calibrate_loop(model): | ||
# calibrate over the training dataset | ||
total = 0 | ||
correct = 0 | ||
loss = 0.0 | ||
for data, labels in training_dataloader: | ||
data, labels = data.cuda(), labels.cuda(non_blocking=True) | ||
out = model(data) | ||
loss += crit(out, labels) | ||
preds = torch.max(out, 1)[1] | ||
total += labels.size(0) | ||
correct += (preds == labels).sum().item() | ||
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print("PTQ Loss: {:.5f} Acc: {:.2f}%".format(loss / total, 100 * correct / total)) | ||
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# %% | ||
# Tune the pre-trained model with FP8 and PTQ | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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quant_cfg = mtq.FP8_DEFAULT_CFG | ||
# PTQ with in-place replacement to quantized modules | ||
mtq.quantize(model, quant_cfg, forward_loop=calibrate_loop) | ||
# model has FP8 qdq nodes at this point | ||
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# %% | ||
# Inference | ||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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# Load the testing dataset | ||
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)), | ||
] | ||
), | ||
) | ||
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testing_dataloader = torch.utils.data.DataLoader( | ||
testing_dataset, batch_size=args.batch_size, shuffle=False, num_workers=2 | ||
) | ||
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with torch.no_grad(): | ||
with export_torch_mode(): | ||
# Compile the model with Torch-TensorRT Dynamo backend | ||
input_tensor = images.cuda() | ||
exp_program = torch.export.export(model, (input_tensor,)) | ||
trt_model = torchtrt.dynamo.compile( | ||
exp_program, | ||
inputs=[input_tensor], | ||
enabled_precisions={torch.float8_e4m3fn}, | ||
min_block_size=1, | ||
debug=False, | ||
) | ||
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# Inference compiled Torch-TensorRT model over the testing dataset | ||
total = 0 | ||
correct = 0 | ||
loss = 0.0 | ||
class_probs = [] | ||
class_preds = [] | ||
model.eval() | ||
for data, labels in testing_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() | ||
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test_probs = torch.cat([torch.stack(batch) for batch in class_probs]) | ||
test_preds = torch.cat(class_preds) | ||
test_loss = loss / total | ||
test_acc = correct / total | ||
print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(test_loss, 100 * test_acc)) |
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