upgrade torch-tensorrt from 2.13 to 2.14 on main

MODULE.bazel

@@ -1,6 +1,6 @@
 module(
     name = "torch_tensorrt",
-    version = "2.13.0a0",
+    version = "2.14.0a0",
     repo_name = "org_pytorch_tensorrt",
 )
 

README.md

@@ -5,7 +5,7 @@ Torch-TensorRT
 <h4> Easily achieve the best inference performance for any PyTorch model on the NVIDIA platform. </h4>
 
 [![Documentation](https://img.shields.io/badge/docs-master-brightgreen)](https://nvidia.github.io/Torch-TensorRT/)
-[![pytorch](https://img.shields.io/badge/PyTorch-2.13-green)](https://download.pytorch.org/whl/nightly/cu130)
+[![pytorch](https://img.shields.io/badge/PyTorch-2.14-green)](https://download.pytorch.org/whl/nightly/cu132)
 [![cuda](https://img.shields.io/badge/CUDA-13.2-green)](https://developer.nvidia.com/cuda-downloads)
 [![trt](https://img.shields.io/badge/TensorRT-11.0.0-green)](https://github.com/nvidia/tensorrt)
 [![license](https://img.shields.io/badge/license-BSD--3--Clause-blue)](./LICENSE)
@@ -121,7 +121,7 @@ auto results = trt_mod.forward({input_tensor});
 These are the following dependencies used to verify the testcases. Torch-TensorRT can work with other versions, but the tests are not guaranteed to pass.
 
 - Bazel 8.1.1
-- Libtorch 2.13.0.dev (latest nightly)
+- Libtorch 2.14.0.dev (latest nightly)
 - CUDA 13.2 (CUDA 12.6 on Jetson)
 - TensorRT 11.0.0.114 (TensorRT 10.3 on Jetson)
 

cpp/include/torch_tensorrt/macros.h

@@ -24,7 +24,7 @@
 #define STR(x) XSTR(x)
 
 #define TORCH_TENSORRT_MAJOR_VERSION 2
-#define TORCH_TENSORRT_MINOR_VERSION 13
+#define TORCH_TENSORRT_MINOR_VERSION 14
 #define TORCH_TENSORRT_PATCH_VERSION 0
 #define TORCH_TENSORRT_VERSION      \
   STR(TORCH_TENSORRT_MAJOR_VERSION) \

docker/MODULE.bazel.ngc

@@ -1,6 +1,6 @@
 module(
     name = "torch_tensorrt",
-    version = "2.13.0a0",
+    version = "2.14.0a0",
     repo_name = "org_pytorch_tensorrt",
 )
 

py/requirements.txt

@@ -2,7 +2,7 @@ numpy
 packaging
 pybind11==2.6.2
 --extra-index-url https://download.pytorch.org/whl/nightly/cu130
-torch>=2.13.0.dev,<2.14.0
+torch>=2.14.0.dev,<2.15.0
 --extra-index-url https://pypi.ngc.nvidia.com
 pyyaml
 dllist

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
     "ninja>=1.11.0",
     "pyyaml>=6.0",
     "cffi>=1.15.1",
-    "torch>=2.13.0.dev,<2.14.0",
+    "torch>=2.14.0.dev,<2.15.0",
     "pybind11==2.6.2",
 ]
 build-backend = "setuptools.build_meta"
@@ -90,7 +90,7 @@ test = [
 test-ext = [
     "timm>=1.0.3",
     "transformers>=5.0.0",
-    "torchvision>=0.27.0.dev,<0.28.0",
+    "torchvision>=0.28.0.dev,<0.29.0",
     "flashinfer-python; python_version >'3.9' and python_version <'3.13'",
 ]
 

setup.py

@@ -837,7 +837,7 @@ def get_sbsa_requirements(base_requirements):
         # TensorRT does not currently build wheels for Tegra, so we need to use the local tensorrt install from the tarball for thor
         # also due to we use sbsa torch_tensorrt wheel for thor, so when we build sbsa wheel, we need to only include tensorrt dependency.
         return requirements + [
-            "torch>=2.13.0.dev,<2.14.0",
+            "torch>=2.14.0.dev,<2.15.0",
             "tensorrt>=11.0.0,<11.1.0",
         ]
 
@@ -849,7 +849,7 @@ def get_x86_64_requirements(base_requirements):
         return requirements
     else:
         requirements = requirements + [
-            "torch>=2.13.0.dev,<2.14.0",
+            "torch>=2.14.0.dev,<2.15.0",
         ]
         if USE_TRT_RTX:
             return requirements + [

version.txt

@@ -1 +1 @@
-2.13.0a0
+2.14.0a0