upgrade torch from 2.10.dev to2.11.dev

MODULE.bazel

@@ -1,6 +1,6 @@
 module(
     name = "torch_tensorrt",
-    version = "2.10.0a0",
+    version = "2.11.0a0",
     repo_name = "org_pytorch_tensorrt",
 )
 

README.md

@@ -5,9 +5,9 @@ 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-2.10-green)](https://download.pytorch.org/whl/nightly/cu130)
+[![pytorch](https://img.shields.io/badge/PyTorch-2.11-green)](https://download.pytorch.org/whl/nightly/cu130)
 [![cuda](https://img.shields.io/badge/CUDA-13.0-green)](https://developer.nvidia.com/cuda-downloads)
-[![trt](https://img.shields.io/badge/TensorRT-10.14.0-green)](https://github.com/nvidia/tensorrt)
+[![trt](https://img.shields.io/badge/TensorRT-10.14.1-green)](https://github.com/nvidia/tensorrt)
 [![license](https://img.shields.io/badge/license-BSD--3--Clause-blue)](./LICENSE)
 [![Linux x86-64 Nightly Wheels](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux-x86_64.yml/badge.svg?branch=nightly)](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux-x86_64.yml)
 [![Linux SBSA Nightly Wheels](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux-aarch64.yml/badge.svg?branch=nightly)](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux-aarch64.yml)
@@ -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 2.10.0.dev (latest nightly)
+- Libtorch 2.11.0.dev (latest nightly)
 - CUDA 13.0 (CUDA 12.6 on Jetson)
 - TensorRT 10.14.1.48 (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 10
+#define TORCH_TENSORRT_MINOR_VERSION 11
 #define TORCH_TENSORRT_PATCH_VERSION 0
 #define TORCH_TENSORRT_VERSION      \
   STR(TORCH_TENSORRT_MAJOR_VERSION) \

py/requirements.txt

@@ -2,7 +2,7 @@ numpy
 packaging
 pybind11==2.6.2
 --extra-index-url https://download.pytorch.org/whl/nightly/cu130
-torch>=2.10.0.dev,<2.11.0
+torch>=2.11.0.dev,<2.12.0
 --extra-index-url https://pypi.ngc.nvidia.com
 pyyaml
 dllist

py/torch_tensorrt/_features.py

@@ -52,7 +52,9 @@
 _WINDOWS_CROSS_COMPILE = check_cross_compile_trt_win_lib()
 _TRTLLM_AVAIL = load_tensorrt_llm_for_nccl()
 
-if importlib.util.find_spec("tensorrt.plugin"):
+if importlib.util.find_spec("tensorrt.plugin") and importlib.util.find_spec(
+    "tensorrt.plugin._lib"
+):
     _QDP_PLUGIN_AVAIL = True
 else:
     _QDP_PLUGIN_AVAIL = False

py/torch_tensorrt/dynamo/runtime/meta_ops/register_meta_ops.py

@@ -6,6 +6,35 @@
 from torch_tensorrt.dynamo.utils import input_is_dynamic, unwrap_tensor_shape
 
 
+@torch.library.register_fake("aten::cudnn_grid_sampler")  # type: ignore
+def fake_aten_cudnn_grid_sampler(
+    input: torch.Tensor,
+    grid: torch.Tensor,
+    interpolation_mode: int = 0,
+    padding_mode: int = 0,
+    align_corners: bool = True,
+) -> torch.Tensor:
+    """
+    Meta kernel for aten::cudnn_grid_sampler to enable FakeTensor/compile flows.
+    Shapes follow grid_sampler semantics:
+      - 2D: input [N, C, H_in, W_in], grid [N, H_out, W_out, 2] -> output [N, C, H_out, W_out]
+      - 3D: input [N, C, D_in, H_in, W_in], grid [N, D_out, H_out, W_out, 3] -> output [N, C, D_out, H_out, W_out]
+    """
+    if grid.dim() == 4:
+        n, h_out, w_out, _ = grid.shape
+        c = input.shape[1]
+        out_shape = [n, c, h_out, w_out]
+    elif grid.dim() == 5:
+        n, d_out, h_out, w_out, _ = grid.shape
+        c = input.shape[1]
+        out_shape = [n, c, d_out, h_out, w_out]
+    else:
+        raise RuntimeError(
+            f"aten::cudnn_grid_sampler: unexpected grid rank {grid.dim()}"
+        )
+    return torch.empty(out_shape, dtype=input.dtype, device=input.device)
+
+
 @torch.library.register_fake("tensorrt::execute_engine")  # type: ignore
 def fake_tensorrt_execute_engine(
     inputs: List[torch.Tensor], fake_trt_engine: Any

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
     "ninja>=1.11.0",
     "pyyaml>=6.0",
     "cffi>=1.15.1",
-    "torch>=2.10.0.dev,<2.11.0",
+    "torch>=2.11.0.dev,<2.12.0",
     "pybind11==2.6.2",
 ]
 build-backend = "setuptools.build_meta"

setup.py

@@ -742,7 +742,7 @@ 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>=2.10.0.dev,<2.11.0",
+            "torch>=2.11.0.dev,<2.12.0",
             "tensorrt>=10.14.1,<10.15.0",
         ]
 
@@ -753,7 +753,7 @@ def get_x86_64_requirements(base_requirements):
     if IS_DLFW_CI:
         return requirements
     else:
-        requirements = requirements + ["torch>=2.10.0.dev,<2.11.0"]
+        requirements = requirements + ["torch>=2.11.0.dev,<2.12.0"]
         if USE_TRT_RTX:
             return requirements + [
                 "tensorrt_rtx>=1.2.0.54",

tests/py/dynamo/automatic_plugin/test_automatic_plugin.py

@@ -3,13 +3,12 @@
 
 import torch
 import torch.nn as nn
+import torch_tensorrt
 import triton
 import triton.language as tl
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 
-import torch_tensorrt
-
 from ..conversion.harness import DispatchTestCase
 
 
@@ -56,15 +55,15 @@ def elementwise_mul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     return x
 
 
-if not torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx:
+if torch_tensorrt.ENABLED_FEATURES.qdp_plugin:
     torch_tensorrt.dynamo.conversion.plugins.custom_op(
         "torchtrt_ex::elementwise_mul", supports_dynamic_shapes=True
     )
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx,
-    "TensorRT RTX does not support plugins",
+    not torch_tensorrt.ENABLED_FEATURES.qdp_plugin,
+    "QDP Plugin is not available",
 )
 class TestAutomaticPlugin(DispatchTestCase):
 

tests/py/dynamo/automatic_plugin/test_automatic_plugin_with_attrs.py

@@ -3,13 +3,12 @@
 
 import torch
 import torch.nn as nn
+import torch_tensorrt
 import triton
 import triton.language as tl
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 
-import torch_tensorrt
-
 from ..conversion.harness import DispatchTestCase
 
 
@@ -57,15 +56,15 @@ def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Ten
     return x
 
 
-if not torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx:
+if torch_tensorrt.ENABLED_FEATURES.qdp_plugin:
     torch_tensorrt.dynamo.conversion.plugins.custom_op(
         "torchtrt_ex::elementwise_scale_mul", supports_dynamic_shapes=True
     )
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx,
-    "TensorRT RTX does not support plugins",
+    not torch_tensorrt.ENABLED_FEATURES.qdp_plugin,
+    "QDP Plugin is not available",
 )
 class TestAutomaticPlugin(DispatchTestCase):
 

tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py

@@ -4,10 +4,9 @@
 import pytest
 import torch
 import torch.nn as nn
+import torch_tensorrt
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
-
-import torch_tensorrt
 from torch_tensorrt._enums import dtype
 
 from ..conversion.harness import DispatchTestCase
@@ -28,7 +27,7 @@ def _(input: torch.Tensor, weight: torch.Tensor, b: float = 1e-6) -> torch.Tenso
     return input
 
 
-if not torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx:
+if torch_tensorrt.ENABLED_FEATURES.qdp_plugin:
     torch_tensorrt.dynamo.conversion.plugins.custom_op(
         "flashinfer::rmsnorm", supports_dynamic_shapes=True
     )
@@ -37,8 +36,8 @@ def _(input: torch.Tensor, weight: torch.Tensor, b: float = 1e-6) -> torch.Tenso
 @unittest.skip("Not Available")
 @unittest.skipIf(
     not importlib.util.find_spec("flashinfer")
-    or torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx,
-    "flashinfer not installed or TensorRT RTX is present",
+    or not torch_tensorrt.ENABLED_FEATURES.qdp_plugin,
+    "flashinfer not installed or QDP Plugin is not available",
 )
 class TestAutomaticPlugin(DispatchTestCase):
 

version.txt

@@ -1 +1 @@
-2.10.0a0
+2.11.0a0