Merge branch 'main' into misc_fixes

Author: peri044

.github/scripts/install-torch-tensorrt.sh

@@ -6,7 +6,7 @@ PLATFORM=$(python -c "import sys; print(sys.platform)")
 
 # Install all the dependencies required for Torch-TensorRT
 pip install --pre ${TORCH_TORCHVISION} --index-url ${INDEX_URL}
-pip install --pre -r ${PWD}/tests/py/requirements.txt --use-deprecated legacy-resolver
+pip install --pre -r ${PWD}/tests/py/requirements.txt
 
 # Install Torch-TensorRT
 if [[ ${PLATFORM} == win32 ]]; then

docsrc/RELEASE_CHECKLIST.md

@@ -63,9 +63,8 @@ will result in a minor version bump and significant bug fixes will result in a p
     - Paste in Milestone information and Changelog information into release notes
     - Generate libtorchtrt.tar.gz for the following platforms:
         - x86_64 cxx11-abi
-        - x86_64 pre-cxx11-abi
         - TODO: Add cxx11-abi build for aarch64 when a manylinux container for aarch64 exists
-    - Generate Python packages for Python 3.6/3.7/3.8/3.9 for x86_64
+    - Generate Python packages for supported Python versions for x86_64
         - TODO: Build a manylinux container for aarch64
         - `docker run -it -v$(pwd)/..:/workspace/Torch-TensorRT build_torch_tensorrt_wheel /bin/bash /workspace/Torch-TensorRT/py/build_whl.sh` generates all wheels
             - To build container `docker build -t build_torch_tensorrt_wheel .`

docsrc/getting_started/installation.rst

@@ -203,37 +203,20 @@ To build with debug symbols use the following command
 
 A tarball with the include files and library can then be found in ``bazel-bin``
 
-Pre CXX11 ABI Build
-............................
-
-To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
-
-.. code-block:: shell
-
-    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
-
-A tarball with the include files and library can then be found in ``bazel-bin``
-
-
 .. _abis:
 
 Choosing the Right ABI
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
-which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
-the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
-other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
-libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
-using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
-recommended commands:
+For the old versions, there were two ABI options to compile Torch-TensorRT which were incompatible with each other, 
+pre-cxx11-abi and cxx11-abi. The complexity came from the different distributions of PyTorch. Fortunately, PyTorch 
+has switched to cxx11-abi for all distributions. Below is a table with general pairings of PyTorch distribution 
+sources and the recommended commands:
 
 +-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
 | PyTorch Source                                              | Recommended Python Compilation Command                   | Recommended C++ Compilation Command                                |
 +=============================================================+==========================================================+====================================================================+
-| PyTorch whl file from PyTorch.org                           | python -m pip install .                                  | bazel build //:libtorchtrt -c opt \-\-config pre_cxx11_abi         |
-+-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
-| libtorch-shared-with-deps-*.zip from PyTorch.org            | python -m pip install .                                  | bazel build //:libtorchtrt -c opt \-\-config pre_cxx11_abi         |
+| PyTorch whl file from PyTorch.org                           | python -m pip install .                                  | bazel build //:libtorchtrt -c opt                                  |
 +-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
 | libtorch-cxx11-abi-shared-with-deps-*.zip from PyTorch.org  | python setup.py bdist_wheel                              | bazel build //:libtorchtrt -c opt                                  |
 +-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
@@ -339,10 +322,6 @@ To build natively on aarch64-linux-gnu platform, configure the ``WORKSPACE`` wit
     In the case that you installed with ``sudo pip install`` this will be ``/usr/local/lib/python3.8/dist-packages/torch``.
     In the case you installed with ``pip install --user`` this will be ``$HOME/.local/lib/python3.8/site-packages/torch``.
 
-In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
-PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
-use that library, set the paths to the same path but when you compile make sure to add the flag ``--config=pre_cxx11_abi``
-
 .. code-block:: shell
 
     new_local_repository(
@@ -351,12 +330,6 @@ use that library, set the paths to the same path but when you compile make sure
         build_file = "third_party/libtorch/BUILD"
     )
 
-    new_local_repository(
-        name = "libtorch_pre_cxx11_abi",
-        path = "/usr/local/lib/python3.8/dist-packages/torch",
-        build_file = "third_party/libtorch/BUILD"
-    )
-
 
 Compile C++ Library and Compiler CLI
 ........................................................
@@ -385,6 +358,4 @@ Compile the Python API using the following command from the ``//py`` directory:
 
     python3 setup.py install
 
-If you have a build of PyTorch that uses Pre-CXX11 ABI drop the ``--use-pre-cxx11-abi`` flag
-
 If you are building for Jetpack 4.5 add the ``--jetpack-version 5.0`` flag

docsrc/user_guide/runtime.rst

@@ -22,8 +22,6 @@ link ``libtorchtrt_runtime.so`` in your deployment programs or use ``DL_OPEN`` o
 you can load the runtime with ``torch.ops.load_library("libtorchtrt_runtime.so")``. You can then continue to use
 programs just as you would otherwise via PyTorch API.
 
-.. note:: If you are using the standard distribution of PyTorch in Python on x86, likely you will need the pre-cxx11-abi variant of ``libtorchtrt_runtime.so``, check :ref:`Installation` documentation for more details.
-
 .. note:: If you are linking ``libtorchtrt_runtime.so``, likely using the following flags will help ``-Wl,--no-as-needed -ltorchtrt -Wl,--as-needed`` as there's no direct symbol dependency to anything in the Torch-TensorRT runtime for most Torch-TensorRT runtime applications
 
 An example of how to use ``libtorchtrt_runtime.so`` can be found here: https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example

packaging/driver_upgrade.bat

@@ -1,10 +1,9 @@
-REM Source: https://github.com/pytorch/builder/blob/4e109742d88ff3c85e77d60bc4d90d229d7f6afe/windows/internal/driver_update.bat
-
-set "DRIVER_DOWNLOAD_LINK=https://ossci-windows.s3.amazonaws.com/528.89-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe"
-curl --retry 3 -kL %DRIVER_DOWNLOAD_LINK% --output 528.89-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe
+set WIN_DRIVER_VN=528.89
+set "DRIVER_DOWNLOAD_LINK=https://ossci-windows.s3.amazonaws.com/%WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe"
+curl --retry 3 -kL %DRIVER_DOWNLOAD_LINK% --output %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe
 if errorlevel 1 exit /b 1
 
-start /wait 528.89-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe -s -noreboot
+start /wait %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe -s -noreboot
 if errorlevel 1 exit /b 1
 
-del 528.89-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe || ver > NUL
+del %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe || ver > NUL

pyproject.toml

@@ -87,7 +87,7 @@ dev = [
 torchvision = [
     "torchvision",
 ] #Leaving torchvisions dependency unconstrained so uv can just install something that should work for the torch we have. TV's on PyT makes it hard to put version constrains in
-quantization = ["nvidia-modelopt[deploy,hf,torch]>=0.17.0"]
+quantization = ["nvidia-modelopt[all]>=0.27.1"]
 monitoring-tools = ["rich>=13.7.1"]
 jupyter = ["rich[jupyter]>=13.7.1"]
 distributed = ["tensorrt-llm>=0.16.0"]

tests/py/dynamo/runtime/test_000_compilation_settings.py

@@ -1,6 +1,10 @@
+import unittest
+
+import tensorrt as trt
 import torch
 import torch_tensorrt
 from torch.testing._internal.common_utils import TestCase, run_tests
+from torch_tensorrt.dynamo.utils import is_tegra_platform
 
 from ..testing_utilities import DECIMALS_OF_AGREEMENT
 
@@ -53,6 +57,10 @@ def forward(self, x):
         )
         torch._dynamo.reset()
 
+    @unittest.skipIf(
+        is_tegra_platform() and trt._version_ > "10.8",
+        "DLA is not supported on Jetson platform starting TRT 10.8",
+    )
     def test_dla_args(self):
         class AddSoftmax(torch.nn.Module):
             def forward(self, x):

tests/py/requirements.txt

@@ -10,3 +10,5 @@ pyyaml
 timm>=1.0.3
 flashinfer-python;  python_version < "3.13"
 transformers==4.49.0
+nvidia-modelopt[all]~=0.27.0; python_version >'3.9' and python_version <'3.13'
+--extra-index-url https://pypi.nvidia.com