chore: cherry-pick FP8

.github/workflows/build-test-linux.yml

@@ -65,6 +65,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-torchscript-fe
@@ -99,6 +100,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-dynamo-converters
@@ -126,6 +128,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-dynamo-fe
@@ -154,6 +157,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-dynamo-serde
@@ -181,6 +185,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-torch-compile-be
@@ -210,6 +215,7 @@ jobs:
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
             post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
     uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
     with:
       job-name: tests-py-dynamo-core
@@ -238,7 +244,9 @@ jobs:
           - repository: pytorch/tensorrt
             package-name: torch_tensorrt
             pre-script: packaging/pre_build_script.sh
-    uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+            post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
+    uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
     with:
       job-name: tests-py-core
       repository: "pytorch/tensorrt"

dev_dep_versions.yml

@@ -1,3 +1,3 @@
 __version__: "2.4.0.dev0"
 __cuda_version__: "12.1"
-__tensorrt_version__: "10.0.1"
+__tensorrt_version__: "10.0.1"

docsrc/index.rst

@@ -114,7 +114,7 @@ Tutorials
    tutorials/_rendered_examples/dynamo/custom_kernel_plugins
    tutorials/_rendered_examples/distributed_inference/data_parallel_gpt2
    tutorials/_rendered_examples/distributed_inference/data_parallel_stable_diffusion
-
+   tutorials/_rendered_examples/dynamo/vgg16_fp8_ptq
 
 Python API Documenation
 ------------------------

examples/dynamo/README.rst

@@ -11,3 +11,4 @@ a number of ways you can leverage this backend to accelerate inference.
 * :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
 * :ref:`torch_compile_stable_diffusion`: Compiling a Stable Diffusion model using ``torch.compile``
 * :ref:`custom_kernel_plugins`: Creating a plugin to use a custom kernel inside TensorRT engines
+* :ref:`vgg16_fp8_ptq`: Compiling a VGG16 model with FP8 and PTQ using ``torch.compile``

examples/dynamo/vgg16_fp8_ptq.py

@@ -0,0 +1,251 @@
+"""
+.. _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.
+"""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+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"
+)
+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",
+)
+
+args = PARSER.parse_args()
+
+model = vgg16(num_classes=10, init_weights=False)
+model = model.cuda()
+
+# %%
+# Load the pre-trained model weights
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+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()
+
+# %%
+# Load training dataset and define loss function for PTQ
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+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
+)
+
+data = iter(training_dataloader)
+images, _ = next(data)
+
+crit = nn.CrossEntropyLoss()
+
+# %%
+# Define Calibration Loop for quantization
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+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))
+
+
+# %%
+# Tune the pre-trained model with FP8 and PTQ
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+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
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# 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
+)
+
+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,
+        )
+
+        # 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()
+
+        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))

examples/int8/training/vgg16/requirements.txt

@@ -4,3 +4,5 @@ nvidia-pyindex
 --extra-index-url https://pypi.nvidia.com
 pytorch-quantization
 tqdm
+nvidia-modelopt
+--extra-index-url https://pypi.nvidia.com

packaging/pre_build_script.sh

@@ -3,7 +3,6 @@
 # Install dependencies
 python3 -m pip install pyyaml
 yum install -y ninja-build gettext
-TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
 wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
     && mv bazelisk-linux-amd64 /usr/bin/bazel \
     && chmod +x /usr/bin/bazel