chore: Bump TRT version to 10.3.0.26 (#3071)

MODULE.bazel

@@ -79,20 +79,20 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "606436ed219c72d1a25a889b2b0ae5cb5a68499dd6f944da4cabb3c34c067d55",
-    strip_prefix = "TensorRT-10.1.0.27",
+    sha256 = "adff1cd5abe5d87013806172351e58fd024e5bf0fc61d49ef4b84cd38ed99081",
+    strip_prefix = "TensorRT-10.3.0.26",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/tars/TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar.gz",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz",
     ],
 )
 
 http_archive(
     name = "tensorrt_win",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "2eb98008944945377eb328871a308704e95bf3bb295fc548784c6da41a70bbed",
-    strip_prefix = "TensorRT-10.1.0.27",
+    sha256 = "2bb4bcb79e8c33575816d874b0512ea28c302af1c06ee6d224da71aa182f75e0",
+    strip_prefix = "TensorRT-10.3.0.26",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/zip/TensorRT-10.1.0.27.Windows.win10.cuda-12.4.zip",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/zip/TensorRT-10.3.0.26.Windows.win10.cuda-12.5.zip",
     ],
 )
 

README.md

@@ -7,7 +7,7 @@ Torch-TensorRT
 [![Documentation](https://img.shields.io/badge/docs-master-brightgreen)](https://nvidia.github.io/Torch-TensorRT/)
 [![pytorch](https://img.shields.io/badge/PyTorch-2.4-green)](https://www.python.org/downloads/release/python-31013/)
 [![cuda](https://img.shields.io/badge/CUDA-12.4-green)](https://developer.nvidia.com/cuda-downloads)
-[![trt](https://img.shields.io/badge/TensorRT-10.1.0-green)](https://github.com/nvidia/tensorrt-llm)
+[![trt](https://img.shields.io/badge/TensorRT-10.3.0-green)](https://github.com/nvidia/tensorrt-llm)
 [![license](https://img.shields.io/badge/license-BSD--3--Clause-blue)](./LICENSE)
 [![linux_tests](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux.yml/badge.svg)](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux.yml)
 [![windows_tests](https://github.com/pytorch/TensorRT/actions/workflows/build-test-windows.yml/badge.svg)](https://github.com/pytorch/TensorRT/actions/workflows/build-test-windows.yml)
@@ -119,7 +119,7 @@ These are the following dependencies used to verify the testcases. Torch-TensorR
 - Bazel 6.3.2
 - Libtorch 2.5.0.dev (latest nightly) (built with CUDA 12.4)
 - CUDA 12.4
-- TensorRT 10.1.0.27
+- TensorRT 10.3.0.26
 
 ## Deprecation Policy
 

core/conversion/converters/impl/batch_norm.cpp

@@ -123,7 +123,7 @@ auto batch_norm_registrations TORCHTRT_UNUSED =
               // track_running_stats=True
               LOG_DEBUG("Args[3] running_mean : " << args[3].isIValue() << " / " << args[3].IValue()->isNone());
               LOG_DEBUG("Args[4] running_var : " << args[4].isIValue() << " / " << args[4].IValue()->isNone());
-              LOG_DEBUG("use_input_stats, momemtum, cudnn_enabled disregarded");
+              LOG_DEBUG("use_input_stats, momemtum are disregarded");
               LOG_DEBUG("ctx->input_is_dynamic : " << ctx->input_is_dynamic);
 
               // Expand spatial dims from 1D to 2D if needed
@@ -154,6 +154,17 @@ auto batch_norm_registrations TORCHTRT_UNUSED =
                 return true;
               }
 
+              auto cudnn_enabled = static_cast<bool>(args[8].unwrapToBool(false));
+              if (!cudnn_enabled) {
+                LOG_DEBUG(
+                    "cuDNN is not enabled, skipping instance_norm conversion. \
+                    Since TRT 10.0, cuDNN is loaded as a dynamic dependency, \
+                    so for some functionalities, users need to install correct \
+                    cuDNN version by themselves. Please see our support matrix \
+                    here: https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html.");
+                return false;
+              }
+
               const int relu = 0;
               const float alpha = 0;
               LOG_DEBUG("Set parameter `relu` and `alpha` to 0");

dev_dep_versions.yml

@@ -1,2 +1,2 @@
 __cuda_version__: "12.4"
-__tensorrt_version__: "10.1.0"
+__tensorrt_version__: "10.3.0"

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 TensorRT 10.1.0.27
+- The example below uses TensorRT 10.3.0.26
 - 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=10.1.0 -f docker/Dockerfile -t torch_tensorrt:latest .
+DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=10.3.0 -f docker/Dockerfile -t torch_tensorrt:latest .
 ```
 
 Run:

packaging/pre_build_script_windows.sh

@@ -12,7 +12,7 @@ if [[ "${CU_VERSION::4}" < "cu12" ]]; then
          pyproject.toml
 fi
 
-#curl -Lo TensorRT.zip https://developer.download.nvidia.com/compute/machine-learning/tensorrt/10.0.1/zip/TensorRT-10.0.1.6.Windows10.win10.cuda-12.4.zip
+#curl -Lo TensorRT.zip https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/zip/TensorRT-10.3.0.26.Windows.win10.cuda-12.5.zip
 #unzip -o TensorRT.zip -d C:/
 TORCH_TORCHVISION=$(grep "^torch" py/requirements.txt)
 INDEX_URL=https://download.pytorch.org/whl/${CHANNEL}/${CU_VERSION}

packaging/smoke_test_script.sh

@@ -2,5 +2,5 @@
 # The issue was smoke test installs the built torch_tensorrt wheel file and checks `import torch_tensorrt; print(torch_tensorrt.__version__)`
 # Since tensorrt cannot be pip installable in CI, the smoke test will fail.
 # One way we tried to handle it is manually install tensorrt wheel while by extracting from the tarball.
-# However, the TensorRT-10.1.0.27/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it.
+# However, the TensorRT-10.3.0.26/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it.
 # TODO: Implement a custom smoke_test script to verify torch_tensorrt installation.

py/ci/Dockerfile.ci

@@ -3,13 +3,13 @@ FROM pytorch/manylinux-builder:cuda12.4
 RUN yum install -y ninja-build
 
 # download TensorRT tarball
-RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/tars/TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar.gz \
-&& gunzip TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar.gz \
-&& tar -xvf TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar \
-&& rm TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar
+RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \
+&& gunzip TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \
+&& tar -xvf TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar \
+&& rm TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar
 
-ENV TENSORRT_DIR=/TensorRT-10.1.0.27
-ENV TENSORRT_VERSION=10.1.0
+ENV TENSORRT_DIR=/TensorRT-10.3.0.26
+ENV TENSORRT_VERSION=10.3.0
 
 RUN wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
     && mv bazelisk-linux-amd64 /usr/bin/bazel \

pyproject.toml

@@ -8,7 +8,7 @@ requires = [
     "cffi>=1.15.1",
     "typing-extensions>=4.7.0",
     "future>=0.18.3",
-    "tensorrt-cu12==10.1.0",
+    "tensorrt-cu12==10.3.0",
     "torch >=2.5.0.dev,<2.6.0",
     "pybind11==2.6.2",
     "numpy",
@@ -55,9 +55,9 @@ keywords = [
 ]
 dependencies = [
     "torch >=2.5.0.dev,<2.6.0",
-    "tensorrt-cu12==10.1.0",
-    "tensorrt-cu12-bindings==10.1.0",
-    "tensorrt-cu12-libs==10.1.0",
+    "tensorrt-cu12==10.3.0",
+    "tensorrt-cu12-bindings==10.3.0",
+    "tensorrt-cu12-libs==10.3.0",
     "packaging>=23",
     "numpy",
     "typing-extensions>=4.7.0",

tests/core/conversion/converters/test_instance_norm.cpp

@@ -18,7 +18,7 @@ constexpr auto graph = R"IR(
             %running_mean.1 : Tensor?,
             %running_var.1 : Tensor?,
             %use_input_stats.1 : bool):
-        %cudnn_enabled.1 : bool = prim::Constant[value=1]()
+        %cudnn_enabled.1 : bool = prim::Constant[value=0]()
         %momentum.1 : float = prim::Constant[value=0.10000000000000001]()
         %eps.1 : float = prim::Constant[value=1.0000000000000001e-05]()
         %4 : Tensor = aten::instance_norm(%input.1,

tests/py/ts/api/test_classes.py

@@ -309,10 +309,8 @@ def test_get_layer_info(self):
         """
         {
             "Layers": [
-                "reshape_before_%26 : Tensor = aten::matmul(%x.1, %25)",
-                "%26 : Tensor = aten::matmul(%x.1, %25) + [Freeze Tensor %27 : Tensor = trt::const(%10) ] + (Unnamed Layer* 4) [Shuffle] + unsqueeze_node_after_[Freeze Tensor %27 : Tensor = trt::const(%10) ] + (Unnamed Layer* 4) [Shuffle]_(Unnamed Layer* 4) [Shuffle]_output + %28 : Tensor = aten::add(%27, %26, %24)",
-                "%31 : Tensor = aten::matmul(%28, %30) + [Freeze Tensor %32 : Tensor = trt::const(%12) ] + (Unnamed Layer* 10) [Shuffle] + unsqueeze_node_after_[Freeze Tensor %32 : Tensor = trt::const(%12) ] + (Unnamed Layer* 10) [Shuffle]_(Unnamed Layer* 10) [Shuffle]_output + %33 : Tensor = aten::add(%32, %31, %29)",
-                "copied_squeeze_after_%33 : Tensor = aten::add(%32, %31, %29)"
+                "%26 : Tensor = aten::matmul(%x.1, %25)_myl0_0",
+                "%31 : Tensor = aten::matmul(%28, %30)_myl0_1"
             ],
             "Bindings": [
                 "input_0",
@@ -326,7 +324,7 @@ def test_get_layer_info(self):
         trt_mod = TestTorchTensorRTModule._get_trt_mod()
         trt_json = json.loads(trt_mod.get_layer_info())
         [self.assertTrue(k in trt_json.keys()) for k in ["Layers", "Bindings"]]
-        self.assertTrue(len(trt_json["Layers"]) == 4)
+        self.assertTrue(len(trt_json["Layers"]) == 2)
         self.assertTrue(len(trt_json["Bindings"]) == 2)
 
 

toolchains/ci_workspaces/MODULE.bazel.tmpl

@@ -67,20 +67,20 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "606436ed219c72d1a25a889b2b0ae5cb5a68499dd6f944da4cabb3c34c067d55",
-    strip_prefix = "TensorRT-10.1.0.27",
+    sha256 = "adff1cd5abe5d87013806172351e58fd024e5bf0fc61d49ef4b84cd38ed99081",
+    strip_prefix = "TensorRT-10.3.0.26",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/tars/TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar.gz",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz",
     ],
 )
 
 http_archive(
     name = "tensorrt_win",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "2eb98008944945377eb328871a308704e95bf3bb295fc548784c6da41a70bbed",
-    strip_prefix = "TensorRT-10.1.0.27",
+    sha256 = "2bb4bcb79e8c33575816d874b0512ea28c302af1c06ee6d224da71aa182f75e0",
+    strip_prefix = "TensorRT-10.3.0.26",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/zip/TensorRT-10.1.0.27.Windows.win10.cuda-12.4.zip",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/zip/TensorRT-10.3.0.26.Windows.win10.cuda-12.5.zip",
     ],
 )
 

toolchains/legacy/WORKSPACE.win.release.tmpl

@@ -63,7 +63,7 @@ http_archive(
 
 new_local_repository(
    name = "tensorrt_win",
-   path = "C:/TensorRT-10.1.0.27",
+   path = "C:/TensorRT-10.3.0.26",
    build_file = "@//third_party/tensorrt/local:BUILD"
 )
 

toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl

@@ -71,10 +71,10 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "606436ed219c72d1a25a889b2b0ae5cb5a68499dd6f944da4cabb3c34c067d55",
-    strip_prefix = "TensorRT-10.1.0.27",
+    sha256 = "adff1cd5abe5d87013806172351e58fd024e5bf0fc61d49ef4b84cd38ed99081",
+    strip_prefix = "TensorRT-10.3.0.26",
     urls = [
-        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.1.0/tars/TensorRT-10.1.0.27.Linux.x86_64-gnu.cuda-12.4.tar.gz",
+        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz",
     ],
 )