fix: Revert to TRT 8.6.0

Author: gs-olive

.circleci/config.yml

@@ -203,10 +203,10 @@ commands:
         default: "8.8.0.121"
       trt-version-short:
         type: string
-        default: "8.6.1"
+        default: "8.6.0"
       trt-version-long:
         type: string
-        default: "8.6.1.6-1"
+        default: "8.6.0.12-1"
       bazel-version:
         type: string
         default: "5.2.0"
@@ -249,7 +249,7 @@ commands:
       parameters:
         trt-version-long:
           type: string
-          default: "8.6.1"
+          default: "8.6.0"
         cudnn-version-long:
           type: string
           default: "8.8.0.121"
@@ -1317,10 +1317,10 @@ parameters:
     default: "8.8.0.121"
   trt-version-short:
     type: string
-    default: "8.6.1"
+    default: "8.6.0"
   trt-version-long:
     type: string
-    default: "8.6.1"
+    default: "8.6.0"
 
   # Jetson platform config
   torch-jetson-build:

README.md

@@ -119,7 +119,7 @@ These are the following dependencies used to verify the testcases. Torch-TensorR
 - Libtorch 2.1.0.dev20230419 (built with CUDA 11.8)
 - CUDA 11.8
 - cuDNN 8.8.0
-- TensorRT 8.6.1
+- TensorRT 8.6.0
 
 ## Prebuilt Binaries and Wheel files
 

WORKSPACE

@@ -81,10 +81,10 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "15bfe6053d45feec45ecc7123a9106076b0b43fa0435f242d89dca0778337759",
-    strip_prefix = "TensorRT-8.6.1.6",
+    sha256 = "c1732a1093c57ab79fa0b687f061be369e449c9c17792b660f3663ecd8fa7b63",
+    strip_prefix = "TensorRT-8.6.0.12",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.0/tars/TensorRT-8.6.0.12.Linux.x86_64-gnu.cuda-11.8.tar.gz",
     ],
 )
 

docker/README.md

@@ -17,14 +17,14 @@ Note: By default the container uses the `pre-cxx11-abi` version of Torch + Torch
 
 ### Instructions
 
-- The example below uses CUDNN 8.8 and TensorRT 8.6
+- The example below uses CUDNN 8.8 and TensorRT 8.6.0
 - 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=8.8 -f docker/Dockerfile -t torch_tensorrt:latest .
+DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=8.6.0 --build-arg CUDNN_VERSION=8.8 -f docker/Dockerfile -t torch_tensorrt:latest .
 ```
 
 Run:

py/requirements.txt

@@ -5,4 +5,4 @@ pybind11==2.6.2
 torch==2.1.0.dev20230419+cu118
 torchvision==0.16.0.dev20230419+cu118
 --extra-index-url https://pypi.ngc.nvidia.com
-tensorrt==8.6.1
+tensorrt==8.6.0

py/torch_tensorrt/fx/README.md

@@ -12,7 +12,7 @@ FX2TRT is merged as FX module in Torch-TensorRT
     $ conda install pytorch torchvision torchtext cudatoolkit=11.8 -c pytorch-nightly
     # Install TensorRT python package
     $ pip3 install nvidia-pyindex
-    $ pip3 install tensorrt==8.6.1
+    $ pip3 install tensorrt==8.6.0
     $ git clone https://github.com/pytorch/TensorRT.git
     $ cd TensorRT/py && python setup.py install --fx-only && cd ..
     $ python -c "import torch_tensorrt.fx"