Add support for JetPack 6.1 build (#3211)

docsrc/getting_started/jetpack.rst

@@ -0,0 +1,119 @@
+.. _Torch_TensorRT_in_JetPack_6.1
+
+Overview
+##################
+
+JetPack 6.1
+---------------------
+Nvida JetPack 6.1 is the latest production release ofJetPack 6.
+With this release it incorporates:
+CUDA 12.6
+TensorRT 10.3
+cuDNN 9.3
+DLFW 24.09
+
+You can find more details for the JetPack 6.1:
+
+    * https://docs.nvidia.com/jetson/jetpack/release-notes/index.html
+    * https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform/index.html
+
+
+Prerequisites
+~~~~~~~~~~~~~~
+
+
+Ensure your jetson developer kit has been flashed with the latest JetPack 6.1. You can find more details on how to flash Jetson board via sdk-manager:
+
+    * https://developer.nvidia.com/sdk-manager
+
+
+check the current jetpack version using
+
+.. code-block:: sh
+
+    apt show nvidia-jetpack
+
+Ensure you have installed JetPack Dev components. This step is required if you need to build on jetson board.
+
+You can only install the dev components that you require: ex, tensorrt-dev would be the meta-package for all TRT development or install everthing.
+
+.. code-block:: sh
+    # install all the nvidia-jetpack dev components
+    sudo apt-get update
+    sudo apt-get install nvidia-jetpack
+
+Ensure you have cuda 12.6 installed(this should be installed automatically from nvidia-jetpack)
+
+.. code-block:: sh
+
+    # check the cuda version
+    nvcc --version
+    # if not installed or the version is not 12.6, install via the below cmd:
+    sudo apt-get update
+    sudo apt-get install cuda-toolkit-12-6
+
+Ensure libcusparseLt.so exists at /usr/local/cuda/lib64/:
+
+.. code-block:: sh
+
+    # if not exist, download and copy to the directory
+    wget https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-sbsa/libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
+    tar xf libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
+    sudo cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/include/* /usr/local/cuda/include/
+    sudo cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/lib/* /usr/local/cuda/lib64/
+
+
+Build torch_tensorrt
+~~~~~~~~~~~~~~
+
+
+Install bazel
+
+.. code-block:: sh
+
+    wget -v https://github.com/bazelbuild/bazelisk/releases/download/v1.20.0/bazelisk-linux-arm64
+    sudo mv bazelisk-linux-arm64 /usr/bin/bazel
+    chmod +x /usr/bin/bazel
+
+Install pip and required python packages:
+    * https://pip.pypa.io/en/stable/installation/
+
+.. code-block:: sh
+
+    # install pip
+    wget https://bootstrap.pypa.io/get-pip.py
+    python get-pip.py
+
+.. code-block:: sh
+
+   # install pytorch from nvidia jetson distribution: https://developer.download.nvidia.com/compute/redist/jp/v61/pytorch
+   python -m pip install torch https://developer.download.nvidia.com/compute/redist/jp/v61/pytorch/torch-2.5.0a0+872d972e41.nv24.08.17622132-cp310-cp310-linux_aarch64.whl
+
+.. code-block:: sh
+
+    # install required python packages
+    python -m pip install -r toolchains/jp_workspaces/requirements.txt
+
+    # if you want to run the test cases, then install the test required python packages
+    python -m pip install -r toolchains/jp_workspaces/test_requirements.txt
+
+
+Build and Install torch_tensorrt wheel file
+
+
+Since torch_tensorrt version has dependencies on torch version. torch version supported by JetPack6.1 is from DLFW 24.08/24.09(torch 2.5.0).
+
+Please make sure to build torch_tensorrt wheel file from source release/2.5 branch
+(TODO: lanl to update the branch name once release/ngc branch is available)
+
+.. code-block:: sh
+
+    cuda_version=$(nvcc --version | grep Cuda | grep release | cut -d ',' -f 2 | sed -e 's/ release //g')
+    export TORCH_INSTALL_PATH=$(python -c "import torch, os; print(os.path.dirname(torch.__file__))")
+    export SITE_PACKAGE_PATH=${TORCH_INSTALL_PATH::-6}
+    export CUDA_HOME=/usr/local/cuda-${cuda_version}/
+    # replace the MODULE.bazel with the jetpack one
+    cat toolchains/jp_workspaces/MODULE.bazel.tmpl | envsubst > MODULE.bazel
+    # build and install torch_tensorrt wheel file
+    python setup.py --use-cxx11-abi install --user
+

docsrc/index.rst

@@ -26,6 +26,7 @@ Getting Started
    :hidden:
 
    getting_started/installation
+   getting_started/jetpack
    getting_started/quick_start
 
 User Guide

setup.py

@@ -156,12 +156,14 @@ def load_dep_info():
             JETPACK_VERSION = "4.6"
         elif version == "5.0":
             JETPACK_VERSION = "5.0"
+        elif version == "6.1":
+            JETPACK_VERSION = "6.1"
 
     if not JETPACK_VERSION:
         warnings.warn(
-            "Assuming jetpack version to be 5.0, if not use the --jetpack-version option"
+            "Assuming jetpack version to be 6.1, if not use the --jetpack-version option"
         )
-        JETPACK_VERSION = "5.0"
+        JETPACK_VERSION = "6.1"
 
     if not CXX11_ABI:
         warnings.warn(
@@ -213,12 +215,15 @@ def build_libtorchtrt_pre_cxx11_abi(
     elif JETPACK_VERSION == "5.0":
         cmd.append("--platforms=//toolchains:jetpack_5.0")
         print("Jetpack version: 5.0")
+    elif JETPACK_VERSION == "6.1":
+        cmd.append("--platforms=//toolchains:jetpack_6.1")
+        print("Jetpack version: 6.1")
 
     if CI_BUILD:
         cmd.append("--platforms=//toolchains:ci_rhel_x86_64_linux")
         print("CI based build")
 
-    print("building libtorchtrt")
+    print(f"building libtorchtrt {cmd=}")
     status_code = subprocess.run(cmd).returncode
 
     if status_code != 0:

toolchains/BUILD

@@ -35,6 +35,15 @@ platform(
     ],
 )
 
+platform(
+    name = "jetpack_6.1",
+    constraint_values = [
+        "@platforms//os:linux",
+        "@platforms//cpu:aarch64",
+        "@//toolchains/jetpack:6.1",
+    ],
+)
+
 platform(
     name = "ci_rhel_x86_64_linux",
     constraint_values = [

toolchains/jetpack/BUILD

@@ -11,3 +11,8 @@ constraint_value(
     name = "4.6",
     constraint_setting = ":jetpack",
 )
+
+constraint_value(
+    name = "6.1",
+    constraint_setting = ":jetpack",
+)

toolchains/jp_workspaces/MODULE.bazel.tmpl

@@ -0,0 +1,61 @@
+module(
+    name = "torch_tensorrt",
+    repo_name = "org_pytorch_tensorrt",
+    version = "${BUILD_VERSION}"
+)
+
+bazel_dep(name = "googletest", version = "1.14.0")
+bazel_dep(name = "platforms", version = "0.0.10")
+bazel_dep(name = "rules_cc", version = "0.0.9")
+bazel_dep(name = "rules_python", version = "0.34.0")
+
+python = use_extension("@rules_python//python/extensions:python.bzl", "python")
+python.toolchain(
+    ignore_root_user_error = True,
+    python_version = "3.11",
+)
+
+bazel_dep(name = "rules_pkg", version = "1.0.1")
+git_override(
+    module_name = "rules_pkg",
+    commit = "17c57f4",
+    remote = "https://github.com/narendasan/rules_pkg",
+)
+
+local_repository = use_repo_rule("@bazel_tools//tools/build_defs/repo:local.bzl", "local_repository")
+
+# External dependency for torch_tensorrt if you already have precompiled binaries.
+local_repository(
+    name = "torch_tensorrt",
+    path = "${SITE_PACKAGE_PATH}/torch_tensorrt",
+)
+
+
+new_local_repository = use_repo_rule("@bazel_tools//tools/build_defs/repo:local.bzl", "new_local_repository")
+
+# CUDA should be installed on the system locally
+new_local_repository(
+    name = "cuda",
+    build_file = "@//third_party/cuda:BUILD",
+    path = "${CUDA_HOME}",
+)
+
+new_local_repository(
+    name = "libtorch",
+    path = "${TORCH_INSTALL_PATH}",
+    build_file = "third_party/libtorch/BUILD",
+)
+
+new_local_repository(
+    name = "libtorch_pre_cxx11_abi",
+    path = "${TORCH_INSTALL_PATH}",
+    build_file = "third_party/libtorch/BUILD"
+)
+
+new_local_repository(
+   name = "tensorrt",
+   path = "/usr/",
+   build_file = "@//third_party/tensorrt/local:BUILD"
+)
+
+

toolchains/jp_workspaces/requirements.txt

@@ -0,0 +1,4 @@
+setuptools==70.2.0
+numpy<2.0.0
+packaging
+pyyaml

toolchains/jp_workspaces/test_requirements.txt

@@ -0,0 +1,9 @@
+expecttest==0.1.6
+networkx==2.8.8
+numpy<2.0.0
+parameterized>=0.2.0
+pytest>=8.2.1
+pytest-xdist>=3.6.1
+pyyaml
+transformers
+# TODO: currently timm torchvision nvidia-modelopt does not have distributions for jetson