remove cudnn dependencies

Author: zewenli98

.github/workflows/docker_builder.yml

@@ -44,18 +44,16 @@ jobs:
         username: ${{ github.actor }}
         password: ${{ secrets.GITHUB_TOKEN }}
 
-    # Automatically detect TensorRT and cuDNN default versions for Torch-TRT build
+    # Automatically detect TensorRT default versions for Torch-TRT build
     - name: Build Docker image
       env:
         DOCKER_TAG: ${{ env.DOCKER_REGISTRY }}/${{ steps.fix_slashes.outputs.container_name }}
       run: |
         python3 -m pip install pyyaml
         TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
         echo "TRT VERSION = ${TRT_VERSION}"
-        CUDNN_VERSION=$(python3 -c "import versions; versions.cudnn_version()")
-        echo "CUDNN VERSION = ${CUDNN_VERSION}"
 
-        DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=$TRT_VERSION --build-arg CUDNN_VERSION=$CUDNN_VERSION -f docker/Dockerfile --tag $DOCKER_TAG .
+        DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=$TRT_VERSION -f docker/Dockerfile --tag $DOCKER_TAG .
 
     - name: Push Docker image
       env:

README.md

@@ -19,7 +19,7 @@ Torch-TensorRT is distributed in the ready-to-run NVIDIA [NGC PyTorch Container]
 
 ## Building a docker container for Torch-TensorRT
 
-We provide a `Dockerfile` in `docker/` directory. It expects a PyTorch NGC container as a base but can easily be modified to build on top of any container that provides, PyTorch, CUDA, cuDNN and TensorRT. The dependency libraries in the container can be found in the <a href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html">release notes</a>.
+We provide a `Dockerfile` in `docker/` directory. It expects a PyTorch NGC container as a base but can easily be modified to build on top of any container that provides, PyTorch, CUDA, and TensorRT. The dependency libraries in the container can be found in the <a href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html">release notes</a>.
 
 Please follow this instruction to build a Docker container.
 
@@ -152,14 +152,13 @@ bash ./compile.sh
 You need to start by having CUDA installed on the system, LibTorch will automatically be pulled for you by bazel,
 then you have two options.
 
-#### 1. Building using cuDNN & TensorRT tarball distributions
+#### 1. Building using TensorRT tarball distributions
 
 > This is recommended so as to build Torch-TensorRT hermetically and insures any bugs are not caused by version issues
 
 > Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your `$LD_LIBRARY_PATH`
 
-1. You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
-   - https://developer.nvidia.com/cudnn
+1. You need to download the tarball distributions of TensorRT from the NVIDIA website.
    - https://developer.nvidia.com/tensorrt
 2. Place these files in a directory (the directories `third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu]` exist for this purpose)
 3. Compile using:
@@ -168,25 +167,16 @@ then you have two options.
 bazel build //:libtorchtrt --compilation_mode opt --distdir third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu]
 ```
 
-#### 2. Building using locally installed cuDNN & TensorRT
+#### 2. Building using locally installed TensorRT
 
 > If you find bugs and you compiled using this method please disclose you used this method in the issue
 > (an `ldd` dump would be nice too)
 
-1. Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+1. Install TensorRT and CUDA on the system before starting to compile.
 2. In `WORKSPACE` comment out
 
 ```py
 # Downloaded distributions to use with --distdir
-http_archive(
-    name = "cudnn",
-    urls = ["<URL>",],
-
-    build_file = "@//third_party/cudnn/archive:BUILD",
-    sha256 = "<TAR SHA256>",
-    strip_prefix = "cuda"
-)
-
 http_archive(
     name = "tensorrt",
     urls = ["<URL>",],
@@ -201,12 +191,6 @@ and uncomment
 
 ```py
 # Locally installed dependencies
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",

dev_dep_versions.yml

@@ -1,4 +1,3 @@
 __version__: "2.4.0.dev0"
 __cuda_version__: "12.1"
-__cudnn_version__: "8.9"
 __tensorrt_version__: "10.0.1"

docker/Dockerfile

@@ -8,9 +8,6 @@ ENV BASE_IMG=nvidia/cuda:12.1.1-devel-ubuntu22.04
 ARG TENSORRT_VERSION
 ENV TENSORRT_VERSION=${TENSORRT_VERSION}
 RUN test -n "$TENSORRT_VERSION" || (echo "No tensorrt version specified, please use --build-arg TENSORRT_VERSION=x.y to specify a version." && exit 1)
-ARG CUDNN_VERSION
-ENV CUDNN_VERSION=${CUDNN_VERSION}
-RUN test -n "$CUDNN_VERSION" || (echo "No cudnn version specified, please use --build-arg CUDNN_VERSION=x.y to specify a version." && exit 1)
 
 ARG PYTHON_VERSION=3.10
 ENV PYTHON_VERSION=${PYTHON_VERSION}
@@ -35,13 +32,12 @@ RUN wget -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-instal
 RUN pyenv install -v ${PYTHON_VERSION}
 RUN pyenv global ${PYTHON_VERSION}
 
-# Install CUDNN + TensorRT + dependencies
+# Install TensorRT + dependencies
 RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
 RUN mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
 RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/7fa2af80.pub
 RUN add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
 RUN apt-get update
-RUN apt-get install -y libcudnn8=${CUDNN_VERSION}* libcudnn8-dev=${CUDNN_VERSION}*
 
 RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
 RUN add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"

docker/README.md

@@ -3,7 +3,7 @@
 * Use `Dockerfile` to build a container which provides the exact development environment that our master branch is usually tested against.
 
 * The `Dockerfile` currently uses <a href="https://github.com/bazelbuild/bazelisk">Bazelisk</a> to select the Bazel version, and uses the exact library versions of Torch and CUDA listed in <a href="https://github.com/pytorch/TensorRT#dependencies">dependencies</a>.
-  * The desired versions of CUDNN and TensorRT must be specified as build-args, with major and minor versions as in: `--build-arg TENSORRT_VERSION=a.b --build-arg CUDNN_VERSION=x.y`
+  * The desired version of TensorRT must be specified as build-args, with major and minor versions as in: `--build-arg TENSORRT_VERSION=a.b`
   * [**Optional**] The desired base image be changed by explicitly setting a base image, as in `--build-arg BASE_IMG=nvidia/cuda:11.8.0-devel-ubuntu22.04`, though this is optional
   * [**Optional**] Additionally, the desired Python version can be changed by explicitly setting a version, as in `--build-arg PYTHON_VERSION=3.10`, though this is optional as well.
 
@@ -17,14 +17,14 @@ Note: By default the container uses the `pre-cxx11-abi` version of Torch + Torch
 
 ### Instructions
 
-- The example below uses CUDNN 8.9 and TensorRT 8.6
+- The example below uses TensorRT 8.6
 - See <a href="https://github.com/pytorch/TensorRT#dependencies">dependencies</a> for a list of current default dependencies.
 
 > From root of Torch-TensorRT repo
 
 Build:
 ```
-DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=8.6 --build-arg CUDNN_VERSION=8.9 -f docker/Dockerfile -t torch_tensorrt:latest .
+DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=8.6 -f docker/Dockerfile -t torch_tensorrt:latest .
 ```
 
 Run:

docker/WORKSPACE.docker

@@ -67,12 +67,6 @@ new_local_repository(
 # Locally installed dependencies (use in cases of custom dependencies or aarch64)
 ####################################################################################
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",

docker/WORKSPACE.ngc

@@ -69,12 +69,6 @@ new_local_repository(
     build_file = "third_party/libtorch/BUILD"
 )
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
     name = "tensorrt",
     path = "/usr/",

docsrc/getting_started/getting_started_with_windows.rst

@@ -11,7 +11,6 @@ Prerequisite:
 * LibTorch
 * TensorRT
 * CUDA
-* cuDNN
 
 
 Build configuration
@@ -90,27 +89,24 @@ Building With Visual Studio Code
       > Also allows using Build Tools to develop and test Open Source Dependencies, to the minor extend of ensuring compatibility with Build Tools
 
 3. Install CUDA (e.g. 11.7.1)
-4. Install cuDNN (e.g. 8.5.0.96)
 
-    - Set ``cuDNN_ROOT_DIR``
-
-5. Install `TensorRT` (e.g 8.5.1.7)
+4. Install `TensorRT` (e.g 8.5.1.7)
 
     - Set ``TensorRT_ROOT``
     - Add ``TensorRT_ROOT\lib`` to ``PATH``
 
-6. Install "libtorch-win-shared-with-deps-latest.zip"
+5. Install "libtorch-win-shared-with-deps-latest.zip"
 
     - Select build targeting the appropriate CUDA version
     - Set ``Torch_DIR``
     - Add ``Torch_DIR\lib`` to ``PATH``
 
-7. Clone TensorRT repo
-8. Install C++ and CMake Tools extensions from MS
+6. Clone TensorRT repo
+7. Install C++ and CMake Tools extensions from MS
 
     - Change build to ``RelWithDebInfo``
 
-9. Update ``.vscode\settings.json``
+8. Update ``.vscode\settings.json``
 
     - Clean, configure, build
 
@@ -137,10 +133,6 @@ e.g. /.vscode/settings.json
                 "type": "FILEPATH",
                 "value": "X:\\path\\to\\tensorrt"
             },
-            "cuDNN_ROOT_DIR": {
-                "type": "FILEPATH",
-                "value": "X:\\path\\to\\cudnn"
-            },
             "CMAKE_CUDA_FLAGS": "-allow-unsupported-compiler"
         },
         "cmake.buildDirectory": "${workspaceFolder}/torch_tensorrt_build"

docsrc/getting_started/installation.rst

@@ -87,33 +87,21 @@ Dependencies for Compilation
     * Specify your CUDA version here if not the version used in the branch being built: https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L46
 
 
-* The correct **LibTorch**, **cuDNN** and **TensorRT** versions will be pulled down for you by bazel.
+* The correct **LibTorch** and **TensorRT** versions will be pulled down for you by bazel.
 
     NOTE: By default bazel will pull the latest nightly from pytorch.org. For building main, this is usually sufficient however if there is a specific PyTorch you are targeting,
     edit these locations with updated URLs/paths:
 
     * https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L53C1-L53C1
 
 
-* **cuDNN and TensorRT** are not required to be installed on the system to build Torch-TensorRT, in fact this is preferable to ensure reproducable builds. If versions other than the default are needed
-  point the WORKSPACE file to the URL of the tarball or download the tarballs for cuDNN and TensorRT from https://developer.nvidia.com and update the paths in the WORKSPACE file here https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L71
+* **TensorRT** is not required to be installed on the system to build Torch-TensorRT, in fact this is preferable to ensure reproducable builds. If versions other than the default are needed
+  point the WORKSPACE file to the URL of the tarball or download the tarball for TensorRT from https://developer.nvidia.com and update the paths in the WORKSPACE file here https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L71
 
     For example:
 
     .. code-block:: python
 
-        http_archive(
-            name = "cudnn",
-            build_file = "@//third_party/cudnn/archive:BUILD",
-            sha256 = "<CUDNN SHA256>", # Optional but recommended
-            strip_prefix = "cudnn-linux-x86_64-<CUDNN VERSION>_<CUDA VERSION>-archive",
-            urls = [
-                "https://developer.nvidia.com/downloads/compute/cudnn/<CUDNN DOWNLOAD PATH>",
-                # OR
-                "file:///<ABSOLUTE PATH TO FILE>/cudnn-linux-x86_64-<CUDNN VERSION>_<CUDA VERSION>-archive.tar.xz"
-            ],
-        )
-
         http_archive(
             name = "tensorrt",
             build_file = "@//third_party/tensorrt/archive:BUILD",
@@ -128,7 +116,7 @@ Dependencies for Compilation
 
     Remember at runtime, these libraries must be added to your ``LD_LIBRARY_PATH`` explicity
 
-If you have a local version of cuDNN and TensorRT installed, this can be used as well by commenting out the above lines and uncommenting the following lines https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L114C1-L124C3
+If you have a local version of TensorRT installed, this can be used as well by commenting out the above lines and uncommenting the following lines https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L114C1-L124C3
 
 
 Building the Package
@@ -228,7 +216,7 @@ Begin by installing CMake.
 
 A few useful CMake options include:
 
-    * CMake finders for TensorRT and cuDNN are provided in `cmake/Modules`. In order for CMake to use them, pass
+    * CMake finders for TensorRT are provided in `cmake/Modules`. In order for CMake to use them, pass
       `-DCMAKE_MODULE_PATH=cmake/Modules` when configuring the project with CMake.
     * Libtorch provides its own CMake finder. In case CMake doesn't find it, pass the path to your install of
       libtorch with `-DTorch_DIR=<path to libtorch>/share/cmake/Torch`

notebooks/WORKSPACE.notebook

@@ -69,12 +69,6 @@ http_archive(
 # Locally installed dependencies (use in cases of custom dependencies or aarch64)
 ####################################################################################
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",

py/ci/build_whl.sh

@@ -100,7 +100,6 @@ libtorchtrt() {
     CUDA_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.cuda_version()")
     TORCHTRT_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.torch_tensorrt_version_release()")
     TRT_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.tensorrt_version()")
-    CUDNN_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.cudnn_version()")
     TORCH_VERSION=$(${PY_DIR}/bin/python -c "from torch import __version__;print(__version__.split('+')[0])")
     cp ${PROJECT_DIR}/bazel-bin/libtorchtrt.tar.gz ${PROJECT_DIR}/py/wheelhouse/libtorchtrt-${TORCHTRT_VERSION}-cudnn${CUDNN_VERSION}-tensorrt${TRT_VERSION}-cuda${CUDA_VERSION}-libtorch${TORCH_VERSION}-x86_64-linux.tar.gz
 }
@@ -120,7 +119,6 @@ libtorchtrt_pre_cxx11_abi() {
     CUDA_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.cuda_version()")
     TORCHTRT_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.torch_tensorrt_version_release()")
     TRT_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.tensorrt_version()")
-    CUDNN_VERSION=$(cd ${PROJECT_DIR} && ${PY_DIR}/bin/python3 -c "import versions; versions.cudnn_version()")
     TORCH_VERSION=$(${PY_DIR}/bin/python -c "from torch import __version__;print(__version__.split('+')[0])")
-    cp ${PROJECT_DIR}/bazel-bin/libtorchtrt.tar.gz ${PROJECT_DIR}/py/wheelhouse/libtorchtrt-${TORCHTRT_VERSION}-pre-cxx11-abi-cudnn${CUDNN_VERSION}-tensorrt${TRT_VERSION}-cuda${CUDA_VERSION}-libtorch${TORCH_VERSION}-x86_64-linux.tar.gz
+    cp ${PROJECT_DIR}/bazel-bin/libtorchtrt.tar.gz ${PROJECT_DIR}/py/wheelhouse/libtorchtrt-${TORCHTRT_VERSION}-pre-cxx11-abi-tensorrt${TRT_VERSION}-cuda${CUDA_VERSION}-libtorch${TORCH_VERSION}-x86_64-linux.tar.gz
 }

py/torch_tensorrt/__init__.py

@@ -6,7 +6,6 @@
 
 from torch_tensorrt._version import (  # noqa: F401
     __cuda_version__,
-    __cudnn_version__,
     __tensorrt_version__,
     __version__,
 )
@@ -40,11 +39,9 @@ def _find_lib(name: str, paths: List[str]) -> str:
     import tensorrt  # noqa: F401
 except ImportError:
     cuda_version = _parse_semver(__cuda_version__)
-    cudnn_version = _parse_semver(__cudnn_version__)
     tensorrt_version = _parse_semver(__tensorrt_version__)
 
     CUDA_MAJOR = cuda_version["major"]
-    CUDNN_MAJOR = cudnn_version["major"]
     TENSORRT_MAJOR = tensorrt_version["major"]
 
     if sys.platform.startswith("win"):

setup.py

@@ -26,7 +26,6 @@
 
 __version__: str = "0.0.0"
 __cuda_version__: str = "0.0"
-__cudnn_version__: str = "0.0"
 __tensorrt_version__: str = "0.0"
 
 LEGACY_BASE_VERSION_SUFFIX_PATTERN = re.compile("a0$")
@@ -62,7 +61,6 @@ def get_base_version() -> str:
 
 def load_dep_info():
     global __cuda_version__
-    global __cudnn_version__
     global __tensorrt_version__
     with open("dev_dep_versions.yml", "r") as stream:
         versions = yaml.safe_load(stream)
@@ -72,7 +70,6 @@ def load_dep_info():
             )
         else:
             __cuda_version__ = versions["__cuda_version__"]
-        __cudnn_version__ = versions["__cudnn_version__"]
         __tensorrt_version__ = versions["__tensorrt_version__"]
 
 
@@ -230,7 +227,6 @@ def gen_version_file():
         print("creating version file")
         f.write('__version__ = "' + __version__ + '"\n')
         f.write('__cuda_version__ = "' + __cuda_version__ + '"\n')
-        f.write('__cudnn_version__ = "' + __cudnn_version__ + '"\n')
         f.write('__tensorrt_version__ = "' + __tensorrt_version__ + '"\n')
 
 

toolchains/ci_workspaces/WORKSPACE.sbsa

@@ -74,12 +74,6 @@ new_local_repository(
     build_file = "third_party/libtorch/BUILD"
 )
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",

toolchains/ci_workspaces/WORKSPACE.x86_64

@@ -75,12 +75,6 @@ new_local_repository(
     build_file = "third_party/libtorch/BUILD"
 )
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",

toolchains/ci_workspaces/WORKSPACE.x86_64.cu118.release.rhel

@@ -75,12 +75,6 @@ http_archive(
 # Locally installed dependencies (use in cases of custom dependencies or aarch64)
 ####################################################################################
 
-new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
-)
-
 new_local_repository(
    name = "tensorrt",
    path = "/usr/",