Merge pull request #2432 from gs-olive/segfault_fix

.circleci/config.yml

@@ -109,7 +109,7 @@ commands:
             sudo docker run --rm --runtime=nvidia --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
 
   install-cudnn:
-    description: "Install CUDNN 8.8.1"
+    description: "Install CUDNN 8.9.5"
     parameters:
       os:
         type: string
@@ -119,7 +119,7 @@ commands:
         default: "x86_64"
       cudnn-version:
         type: string
-        default: "8.8.1.3"
+        default: "8.9.5.30"
       cuda-version:
         type: string
         default: "cuda12.0"
@@ -198,7 +198,7 @@ commands:
         default: "cuda12.0"
       cudnn-version:
         type: string
-        default: "8.8.1.3"
+        default: "8.9.5.30"
       trt-version-short:
         type: string
         default: "8.6.1"
@@ -246,7 +246,7 @@ commands:
           default: "8.6.1"
         cudnn-version-long:
           type: string
-          default: "8.8.1.3"
+          default: "8.9.5.30"
       steps:
         - run:
             name: Set up python environment
@@ -1460,7 +1460,7 @@ parameters:
     default: "https://download.pytorch.org/whl/nightly/cu121"
   cudnn-version:
     type: string
-    default: "8.8.1.3"
+    default: "8.9.5.30"
   trt-version-short:
     type: string
     default: "8.6.1"
@@ -1483,7 +1483,7 @@ parameters:
     default: "https://download.pytorch.org/whl/cu117"
   cudnn-version-legacy:
     type: string
-    default: "8.8.1.3"
+    default: "8.9.5.30"
   trt-version-short-legacy:
     type: string
     default: "8.6.1"

README.md

@@ -118,7 +118,7 @@ These are the following dependencies used to verify the testcases. Torch-TensorR
 - Bazel 5.2.0
 - Libtorch 2.2.0.dev (latest nightly) (built with CUDA 12.1)
 - CUDA 12.1
-- cuDNN 8.8.1
+- cuDNN 8.9.5
 - TensorRT 8.6.1
 
 ## Prebuilt Binaries and Wheel files

WORKSPACE

@@ -71,10 +71,10 @@ http_archive(
 http_archive(
     name = "cudnn",
     build_file = "@//third_party/cudnn/archive:BUILD",
-    sha256 = "79d77a769c7e7175abc7b5c2ed5c494148c0618a864138722c887f95c623777c",
-    strip_prefix = "cudnn-linux-x86_64-8.8.1.3_cuda12-archive",
+    sha256 = "2a2eb89a2ab51071151c6082f1e816c702167a711a9372f9f73a7b5c4b06e01a",
+    strip_prefix = "cudnn-linux-x86_64-8.9.5.30_cuda12-archive",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/cudnn/secure/8.8.1/local_installers/12.0/cudnn-linux-x86_64-8.8.1.3_cuda12-archive.tar.xz",
+        "https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.5/local_installers/12.x/cudnn-linux-x86_64-8.9.5.30_cuda12-archive.tar.xz",
     ],
 )
 

dev_dep_versions.yml

@@ -1,4 +1,4 @@
 __version__: "2.2.0.dev0"
 __cuda_version__: "12.1"
-__cudnn_version__: "8.8"
+__cudnn_version__: "8.9"
 __tensorrt_version__: "8.6"

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 8.9 and 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=8.8 -f docker/Dockerfile -t torch_tensorrt:latest .
+DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=8.6 --build-arg CUDNN_VERSION=8.9 -f docker/Dockerfile -t torch_tensorrt:latest .
 ```
 
 Run:

tools/perf/perf_run.py

@@ -7,14 +7,14 @@
 import time
 import timeit
 import warnings
+from functools import wraps
 
 import numpy as np
 import pandas as pd
 import tensorrt as trt
 
 # Importing supported Backends
 import torch
-import torch.backends.cudnn as cudnn
 from utils import (
     BENCHMARK_MODELS,
     parse_backends,
@@ -30,6 +30,7 @@
 
 
 def run_with_try_except(func):
+    @wraps(func)
     def wrapper_func(*args, **kwargs):
         try:
             return func(*args, **kwargs)
@@ -527,7 +528,6 @@ def recordStats(backend, timings, precision, batch_size=1, compile_time_s=None):
     )
     args = arg_parser.parse_args()
 
-    cudnn.benchmark = True
     # Create random input tensor of certain size
     torch.manual_seed(12345)
     model_name = "Model"
@@ -542,9 +542,6 @@ def recordStats(backend, timings, precision, batch_size=1, compile_time_s=None):
     if os.path.exists(model_name):
         print("Loading user provided torchscript model: ", model_name)
         model = torch.jit.load(model_name).cuda().eval()
-    elif model_name in BENCHMARK_MODELS:
-        print("Loading torchscript model from BENCHMARK_MODELS for: ", model_name)
-        model = BENCHMARK_MODELS[model_name]["model"].eval().cuda()
 
     # Load PyTorch Model, if provided
     if len(model_name_torch) > 0 and os.path.exists(model_name_torch):

tools/perf/utils.py

@@ -1,12 +1,8 @@
-from typing import Optional, Sequence, Union
-
 import custom_models as cm
 import timm
 import torch
 import torchvision.models as models
 
-import torch_tensorrt
-
 BENCHMARK_MODEL_NAMES = {
     "vgg16",
     "alexnet",