diff --git a/docs/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt b/docs/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt
index 9dfdff54f2..6e5917ae7b 100644
--- a/docs/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt
+++ b/docs/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt
@@ -148,14 +148,14 @@ a number of ways you can leverage this backend to accelerate inference.
.. raw:: html
-
+
.. only:: html
- .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_vgg16_fp8_ptq_thumb.png
+ .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_vgg16_ptq_thumb.png
:alt:
- :ref:`sphx_glr_tutorials__rendered_examples_dynamo_vgg16_fp8_ptq.py`
+ :ref:`sphx_glr_tutorials__rendered_examples_dynamo_vgg16_ptq.py`
.. raw:: html
@@ -195,6 +195,6 @@ a number of ways you can leverage this backend to accelerate inference.
/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example
/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
- /tutorials/_rendered_examples/dynamo/vgg16_fp8_ptq
+ /tutorials/_rendered_examples/dynamo/vgg16_ptq
/tutorials/_rendered_examples/dynamo/custom_kernel_plugins
diff --git a/docs/_sources/tutorials/_rendered_examples/dynamo/vgg16_ptq.rst.txt b/docs/_sources/tutorials/_rendered_examples/dynamo/vgg16_ptq.rst.txt
new file mode 100644
index 0000000000..3224e138ca
--- /dev/null
+++ b/docs/_sources/tutorials/_rendered_examples/dynamo/vgg16_ptq.rst.txt
@@ -0,0 +1,363 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/_rendered_examples/dynamo/vgg16_ptq.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+ .. note::
+ :class: sphx-glr-download-link-note
+
+ :ref:`Go to the end `
+ to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo_vgg16_ptq.py:
+
+
+.. _vgg16_ptq:
+
+Deploy Quantized Models using Torch-TensorRT
+======================================================
+
+Here we demonstrate how to deploy a model quantized to INT8 or FP8 using the Dynamo frontend of Torch-TensorRT
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-13
+
+Imports and Model Definition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 13-125
+
+.. code-block:: python
+
+
+ import argparse
+
+ 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
+
+
+ class VGG(nn.Module):
+ def __init__(self, layer_spec, num_classes=1000, init_weights=False):
+ super(VGG, self).__init__()
+
+ 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
+
+ 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()
+
+ 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)
+
+ def forward(self, x):
+ x = self.features(x)
+ x = self.avgpool(x)
+ x = torch.flatten(x, 1)
+ x = self.classifier(x)
+ return x
+
+
+ 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)
+
+
+ PARSER = argparse.ArgumentParser(
+ description="Load pre-trained VGG model and then tune with FP8 and PTQ. For having a pre-trained VGG model, please refer to https://github.com/pytorch/TensorRT/tree/main/examples/int8/training/vgg16"
+ )
+ 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",
+ )
+ PARSER.add_argument(
+ "--quantize-type",
+ default="int8",
+ type=str,
+ help="quantization type, currently supported int8 or fp8 for PTQ",
+ )
+ args = PARSER.parse_args()
+
+ model = vgg16(num_classes=10, init_weights=False)
+ model = model.cuda()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-128
+
+Load the pre-trained model weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-145
+
+.. code-block:: python
+
+
+ ckpt = torch.load(args.ckpt)
+ weights = ckpt["model_state_dict"]
+
+ if torch.cuda.device_count() > 1:
+ from collections import OrderedDict
+
+ new_state_dict = OrderedDict()
+ for k, v in weights.items():
+ name = k[7:] # remove `module.`
+ new_state_dict[name] = v
+ weights = new_state_dict
+
+ model.load_state_dict(weights)
+ # Don't forget to set the model to evaluation mode!
+ model.eval()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 146-148
+
+Load training dataset and define loss function for PTQ
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-175
+
+.. code-block:: python
+
+
+ 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,
+ drop_last=True,
+ )
+
+ data = iter(training_dataloader)
+ images, _ = next(data)
+
+ crit = nn.CrossEntropyLoss()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 176-178
+
+Define Calibration Loop for quantization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 178-196
+
+.. code-block:: python
+
+
+
+ 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()
+
+ print("PTQ Loss: {:.5f} Acc: {:.2f}%".format(loss / total, 100 * correct / total))
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 197-199
+
+Tune the pre-trained model with FP8 and PTQ
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 199-207
+
+.. code-block:: python
+
+ if args.quantize_type == "int8":
+ quant_cfg = mtq.INT8_DEFAULT_CFG
+ elif args.quantize_type == "fp8":
+ 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
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 208-210
+
+Inference
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 210-275
+
+.. code-block:: python
+
+
+ # 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)),
+ ]
+ ),
+ )
+
+ testing_dataloader = torch.utils.data.DataLoader(
+ testing_dataset,
+ batch_size=args.batch_size,
+ shuffle=False,
+ num_workers=2,
+ drop_last=True,
+ ) # set drop_last=True to drop the last incomplete batch for static shape `torchtrt.dynamo.compile()`
+
+ with torch.no_grad():
+ with export_torch_mode():
+ # Compile the model with Torch-TensorRT Dynamo backend
+ input_tensor = images.cuda()
+ # torch.export.export() failed due to RuntimeError: Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode()
+ from torch.export._trace import _export
+
+ exp_program = _export(model, (input_tensor,))
+ if args.quantize_type == "int8":
+ enabled_precisions = {torch.int8}
+ elif args.quantize_type == "fp8":
+ enabled_precisions = {torch.float8_e4m3fn}
+ trt_model = torchtrt.dynamo.compile(
+ exp_program,
+ inputs=[input_tensor],
+ enabled_precisions=enabled_precisions,
+ min_block_size=1,
+ debug=False,
+ )
+ # You can also use torch compile path to compile the model with Torch-TensorRT:
+ # trt_model = torch.compile(model, backend="tensorrt")
+
+ # Inference compiled Torch-TensorRT model over the testing dataset
+ total = 0
+ correct = 0
+ loss = 0.0
+ class_probs = []
+ class_preds = []
+ for data, labels in testing_dataloader:
+ data, labels = data.cuda(), labels.cuda(non_blocking=True)
+ out = trt_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)
+ test_loss = loss / total
+ test_acc = correct / total
+ print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(test_loss, 100 * test_acc))
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 0.000 seconds)
+
+
+.. _sphx_glr_download_tutorials__rendered_examples_dynamo_vgg16_ptq.py:
+
+.. only:: html
+
+ .. container:: sphx-glr-footer sphx-glr-footer-example
+
+
+
+
+ .. container:: sphx-glr-download sphx-glr-download-python
+
+ :download:`Download Python source code: vgg16_ptq.py `
+
+ .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+ :download:`Download Jupyter notebook: vgg16_ptq.ipynb `
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery `_
diff --git a/docs/_sources/tutorials/_rendered_examples/index.rst.txt b/docs/_sources/tutorials/_rendered_examples/index.rst.txt
index 70947d5651..f68c1fb417 100644
--- a/docs/_sources/tutorials/_rendered_examples/index.rst.txt
+++ b/docs/_sources/tutorials/_rendered_examples/index.rst.txt
@@ -164,14 +164,14 @@ a number of ways you can leverage this backend to accelerate inference.
.. raw:: html
-
ifargs.quantize_type=="int8":
+ quant_cfg=mtq.INT8_DEFAULT_CFG
+elifargs.quantize_type=="fp8":
+ 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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