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fix: TRT upgrade to 9.2 on Linux Testing
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README.md

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@@ -119,7 +119,7 @@ These are the following dependencies used to verify the testcases. Torch-TensorR
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- Libtorch 2.2.0.dev (latest nightly) (built with CUDA 12.1)
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- CUDA 12.1
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- cuDNN 8.9.5
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- TensorRT 8.6.1
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- TensorRT 9.2.0
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## Prebuilt Binaries and Wheel files
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WORKSPACE

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@@ -81,10 +81,10 @@ http_archive(
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http_archive(
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name = "tensorrt",
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build_file = "@//third_party/tensorrt/archive:BUILD",
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sha256 = "0f8157a5fc5329943b338b893591373350afa90ca81239cdadd7580cd1eba254",
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strip_prefix = "TensorRT-8.6.1.6",
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sha256 = "3dd505a9e0d0adf9257080b543f51d91df736dbd1f75417b9dde1a7b7a5d87f2",
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strip_prefix = "tensorrt-9.2.0.5",
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urls = [
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"https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz",
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"https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/9.2.0/tensorrt-9.2.0.5.linux.x86_64-gnu.cuda-12.2.tar.gz",
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],
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)
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dev_dep_versions.yml

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__version__: "2.2.0.dev0"
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__cuda_version__: "12.1"
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__cudnn_version__: "8.9"
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__tensorrt_version__: "8.6"
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__tensorrt_version__: "9.2"

docker/README.md

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@@ -17,14 +17,14 @@ Note: By default the container uses the `pre-cxx11-abi` version of Torch + Torch
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### Instructions
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- The example below uses CUDNN 8.9 and TensorRT 8.6
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- The example below uses CUDNN 8.9 and TensorRT 9.2
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- See <a href="https://github.com/pytorch/TensorRT#dependencies">dependencies</a> for a list of current default dependencies.
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> From root of Torch-TensorRT repo
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Build:
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```
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DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=8.6 --build-arg CUDNN_VERSION=8.9 -f docker/Dockerfile -t torch_tensorrt:latest .
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DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=9.2 --build-arg CUDNN_VERSION=8.9 -f docker/Dockerfile -t torch_tensorrt:latest .
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```
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Run:

py/requirements.txt

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@@ -4,6 +4,6 @@ pybind11==2.6.2
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--extra-index-url https://download.pytorch.org/whl/nightly/cu121
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torch>=2.2.0.dev,<2.3.0
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torchvision>=0.17.0.dev,<0.18.0
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--extra-index-url https://pypi.ngc.nvidia.com
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tensorrt==8.6.1
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--extra-index-url https://pypi.nvidia.com
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tensorrt==9.2.0.post12.dev5
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pyyaml

pyproject.toml

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@@ -8,7 +8,7 @@ requires = [
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"cffi>=1.15.1",
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"typing-extensions>=4.7.0",
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"future>=0.18.3",
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"tensorrt>=8.6,<8.7",
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"tensorrt>=8.6",
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"torch >=2.2.0.dev,<2.3.0",
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#"torch==2.1.0.dev20230731",
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"pybind11==2.6.2",
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dependencies = [
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"torch >=2.2.0.dev,<2.3.0",
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#"torch==2.1.0.dev20230731",
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"tensorrt>=8.6,<8.7",
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"tensorrt>=8.6",
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"packaging>=23",
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"numpy",
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"typing-extensions>=4.7.0",

third_party/cudnn/local/BUILD

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cc_library(
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name = "cudnn_headers",
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hdrs = ["include/cudnn.h"] + glob([
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"include/cudnn_*.h",
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hdrs = glob([
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"include/cudnn*.h",
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]),
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includes = ["include/"],
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visibility = ["//visibility:private"],

third_party/tensorrt/local/BUILD

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@@ -40,9 +40,7 @@ cc_library(
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"include/aarch64-linux-gnu/NvInferPluginUtils.h",
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],
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),
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":ci_rhel_x86_64_linux": [
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"include/NvUtils.h",
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] + glob(
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":ci_rhel_x86_64_linux": glob(
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[
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"include/NvInfer*.h",
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],

toolchains/legacy/pyproject.toml

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@@ -8,7 +8,7 @@ requires = [
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"cffi>=1.15.1",
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"typing-extensions>=4.7.0",
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"future>=0.18.3",
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"tensorrt>=8.6,<8.7",
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"tensorrt>=8.6",
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"torch>=1.13.0,<2.0",
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"pybind11==2.6.2",
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"numpy",
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keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
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dependencies = [
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"torch>=1.13.0,<2.0",
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"tensorrt>=8.6,<8.7",
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"tensorrt>=8.6",
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"packaging>=23",
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"numpy",
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"typing-extensions>=4.7.0",

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