TRT-LLM installation tool

.github/workflows/build-test-linux-x86_64.yml

@@ -315,6 +315,39 @@ jobs:
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_ts_models_tests_results.xml -n auto  models/
         popd
 
+  L1-dynamo-distributed-tests:
+    name: L1 dynamo distributed tests
+    needs: [filter-matrix, build,L0-dynamo-core-tests, L0-dynamo-converter-tests, L0-py-core-tests, L0-torchscript-tests]
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - repository: pytorch/tensorrt
+            package-name: torch_tensorrt
+            pre-script: packaging/pre_build_script.sh
+            post-script: packaging/post_build_script.sh
+            smoke-test-script: packaging/smoke_test_script.sh
+    uses: ./.github/workflows/linux-test.yml
+    with:
+      job-name: L1-dynamo-distributed-tests
+      repository: "pytorch/tensorrt"
+      ref: ""
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: main
+      build-matrix: ${{ needs.filter-matrix.outputs.matrix }}
+      pre-script: ${{ matrix.pre-script }}
+      script: |
+        set -euo pipefail
+        export USE_HOST_DEPS=1
+        export CI_BUILD=1
+        export USE_TRTLLM_PLUGINS=1
+        dnf install -y mpich mpich-devel openmpi openmpi-devel
+        pushd .
+        cd tests/py
+        cd dynamo
+        python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_distributed_test_results.xml distributed/test_nccl_ops.py
+        popd
+
   L2-torch-compile-tests:
     name: L2 torch compile tests
     needs: [filter-matrix, build, L1-torch-compile-tests, L1-dynamo-compile-tests, L1-dynamo-core-tests, L1-torchscript-tests]

dev_dep_versions.yml

@@ -1,3 +1,4 @@
 __cuda_version__: "12.8"
 __tensorrt_version__: "10.13.3"
 __tensorrt_rtx_version__: "1.0.0"
+__tensorrt_llm_version__: "0.17.0.post1"

py/torch_tensorrt/_features.py

@@ -7,6 +7,7 @@
 import tensorrt
 from torch_tensorrt._utils import (
     check_cross_compile_trt_win_lib,
+    load_tensorrt_llm_for_nccl,
     sanitized_torch_version,
 )
 
@@ -23,6 +24,7 @@
         "qdp_plugin",
         "windows_cross_compile",
         "tensorrt_rtx",
+        "trtllm_for_nccl",
     ],
 )
 
@@ -48,6 +50,7 @@
 _FX_FE_AVAIL = False if _TENSORRT_RTX else True
 _REFIT_AVAIL = True
 _WINDOWS_CROSS_COMPILE = check_cross_compile_trt_win_lib()
+_TRTLLM_AVAIL = load_tensorrt_llm_for_nccl()
 
 if importlib.util.find_spec("tensorrt.plugin"):
     _QDP_PLUGIN_AVAIL = True
@@ -63,6 +66,7 @@
     _QDP_PLUGIN_AVAIL,
     _WINDOWS_CROSS_COMPILE,
     _TENSORRT_RTX,
+    _TRTLLM_AVAIL,
 )
 
 T = TypeVar("T")
@@ -158,6 +162,22 @@ def not_implemented(*args: List[Any], **kwargs: Dict[str, Any]) -> Any:
     return wrapper
 
 
+def needs_trtllm_for_nccl(f: Callable[..., Any]) -> Callable[..., Any]:
+    def wrapper(*args: List[Any], **kwargs: Dict[str, Any]) -> Any:
+        if ENABLED_FEATURES.trtllm_for_nccl:
+            return f(*args, **kwargs)
+        else:
+
+            def not_implemented(*args: List[Any], **kwargs: Dict[str, Any]) -> Any:
+                raise NotImplementedError(
+                    "Refit feature is currently not available in Python 3.13 or higher"
+                )
+
+            return not_implemented(*args, **kwargs)
+
+    return wrapper
+
+
 def for_all_methods(
     decorator: Callable[..., Any], exclude: Optional[List[str]] = None
 ) -> Callable[..., Any]:

py/torch_tensorrt/_utils.py

@@ -1,9 +1,22 @@
+import ctypes
+import getpass
+import logging
+import os
+import platform
 import sys
-from typing import Any
+import tempfile
+import urllib.request
+from pathlib import Path
+from typing import Any, Optional
 
 import tensorrt as trt
 import torch
 
+logger = logging.getLogger(__name__)
+
+_WHL_CPYTHON_VERSION = "cp310"
+_TENSORRT_LLM_VERSION_ = "0.17.0.post1"
+
 
 def sanitized_torch_version() -> Any:
     return (
@@ -50,3 +63,254 @@ def is_tensorrt_version_supported(min_version: str) -> bool:
     except (ImportError, ValueError):
         # If tensorrt is not installed or version cannot be determined
         return False
+
+
+def is_thor() -> bool:
+    if torch.cuda.get_device_capability() in [(11, 0)]:
+        return True
+    return False
+
+
+def is_platform_supported_for_trtllm() -> bool:
+    """
+    Checks if the current platform supports TensorRT-LLM plugins for the NCCL backend.
+
+    Returns:
+        bool: True if supported, False otherwise.
+
+    Unsupported:
+        - Windows platforms
+        - Jetson/Orin/Xavier (aarch64 architecture + 'tegra' in platform release)
+        - Thor devices
+        - CUDA 13 not supported
+    """
+    system = platform.system().lower()
+    machine = platform.machine().lower()
+    release = platform.release().lower()
+
+    if "windows" in system:
+        logger.info(
+            "TensorRT-LLM plugins for NCCL backend are not supported on Windows."
+        )
+        return False
+
+    if machine == "aarch64" and "tegra" in release or is_thor():
+        logger.info(
+            "TensorRT-LLM plugins for NCCL backend are not supported on Jetson/Orin/Xavier (Tegra) or Thor devices."
+        )
+        return False
+
+    try:
+        cuda_version = torch.version.cuda  # e.g., "12.4" or "13.0"
+        if cuda_version is None:
+            logger.error(
+                "This pytorch build does not support CUDA, please reinstall pytorch with CUDA support"
+            )
+            return False
+
+        major, minor = map(int, cuda_version.split("."))
+        if major != 12:
+            logger.error(
+                "CUDA 13 is not currently supported for TRT-LLM plugins. Please install pytorch with CUDA 12.x support"
+            )
+            return False
+
+        return True
+
+    except Exception as e:
+        logger.warning(f"Failed to detect CUDA version: {e}")
+        return False
+
+    return True
+
+
+def _cache_root() -> Path:
+    username = getpass.getuser()
+    return Path(tempfile.gettempdir()) / f"torch_tensorrt_{username}"
+
+
+def _extracted_dir_trtllm(platform_system: str, platform_machine: str) -> Path:
+    return (
+        _cache_root()
+        / "trtllm"
+        / f"{_TENSORRT_LLM_VERSION_}_{platform_system}_{platform_machine}"
+    )
+
+
+def download_and_get_plugin_lib_path() -> Optional[str]:
+    """
+    Returns the path to the TensorRT‑LLM shared library, downloading and extracting if necessary.
+
+    Args:
+        platform (str): Platform identifier (e.g., 'linux_x86_64')
+
+    Returns:
+        Optional[str]: Path to shared library or None if operation fails.
+    """
+    platform_system = platform.system().lower()
+    platform_machine = platform.machine().lower()
+    wheel_filename = (
+        f"tensorrt_llm-{_TENSORRT_LLM_VERSION_}-{_WHL_CPYTHON_VERSION}-"
+        f"{_WHL_CPYTHON_VERSION}-{platform_system}_{platform_machine}.whl"
+    )
+    wheel_path = _cache_root() / wheel_filename
+    extract_dir = _extracted_dir_trtllm(platform_system, platform_machine)
+    # else will never be met though
+    lib_filename = (
+        "libnvinfer_plugin_tensorrt_llm.so"
+        if "linux" in platform_system
+        else "libnvinfer_plugin_tensorrt_llm.dll"
+    )
+    # eg: /tmp/torch_tensorrt_<username>/trtllm/0.17.0.post1_linux_x86_64/tensorrt_llm/libs/libnvinfer_plugin_tensorrt_llm.so
+    plugin_lib_path = extract_dir / "tensorrt_llm" / "libs" / lib_filename
+
+    if plugin_lib_path.exists():
+        return str(plugin_lib_path)
+
+    wheel_path.parent.mkdir(parents=True, exist_ok=True)
+    extract_dir.mkdir(parents=True, exist_ok=True)
+
+    if not wheel_path.exists():
+        base_url = "https://pypi.nvidia.com/tensorrt-llm/"
+        download_url = base_url + wheel_filename
+        try:
+            logger.debug(f"Downloading {download_url} ...")
+            urllib.request.urlretrieve(download_url, wheel_path)
+            logger.debug("Download succeeded and TRT-LLM wheel is now present")
+        except urllib.error.HTTPError as e:
+            logger.error(
+                f"HTTP error {e.code} when trying to download {download_url}: {e.reason}"
+            )
+        except urllib.error.URLError as e:
+            logger.error(
+                f"URL error when trying to download {download_url}: {e.reason}"
+            )
+        except OSError as e:
+            logger.error(f"Local file write error: {e}")
+
+    try:
+        import zipfile
+    except ImportError as e:
+        raise ImportError(
+            "zipfile module is required but not found. Please install zipfile"
+        )
+    try:
+        with zipfile.ZipFile(wheel_path) as zip_ref:
+            zip_ref.extractall(extract_dir)
+            logger.debug(f"Extracted wheel to {extract_dir}")
+    except FileNotFoundError as e:
+        # This should capture the errors in the download failure above
+        logger.error(f"Wheel file not found at {wheel_path}: {e}")
+        raise RuntimeError(
+            f"Failed to find downloaded wheel file at {wheel_path}"
+        ) from e
+    except zipfile.BadZipFile as e:
+        logger.error(f"Invalid or corrupted wheel file: {e}")
+        raise RuntimeError(
+            "Downloaded wheel file is corrupted or not a valid zip archive"
+        ) from e
+    except Exception as e:
+        logger.error(f"Unexpected error while extracting wheel: {e}")
+        raise RuntimeError(
+            "Unexpected error during extraction of TensorRT-LLM wheel"
+        ) from e
+
+    try:
+        wheel_path.unlink(missing_ok=True)
+        logger.debug(f"Deleted wheel file: {wheel_path}")
+    except Exception as e:
+        logger.warning(f"Could not delete wheel file {wheel_path}: {e}")
+    if not plugin_lib_path.exists():
+        logger.error(
+            f"Plugin library not found at expected location: {plugin_lib_path}"
+        )
+        return None
+
+    return str(plugin_lib_path)
+
+
+def load_and_initialize_trtllm_plugin(plugin_lib_path: str) -> bool:
+    """
+    Loads and initializes the TensorRT-LLM plugin from the given shared library path.
+
+    Args:
+        plugin_lib_path (str): Path to the shared TensorRT-LLM plugin library.
+
+    Returns:
+        bool: True if successful, False otherwise.
+    """
+    try:
+        handle = ctypes.CDLL(plugin_lib_path)
+        logger.info(f"Successfully loaded plugin library: {plugin_lib_path}")
+    except OSError as e_os_error:
+        if "libmpi" in str(e_os_error):
+            logger.warning(
+                f"Failed to load libnvinfer_plugin_tensorrt_llm.so from {plugin_lib_path}, got error {e_os_error} (hint: libmpi.so is a necessary dependency; ensure that OpenMPI or MPICH is installed on your system)",
+                exc_info=e_os_error,
+            )
+        else:
+            logger.warning(
+                f"Failed to load libnvinfer_plugin_tensorrt_llm.so from {plugin_lib_path}. "
+                f"Ensure the path is correct and the library is compatible.",
+                exc_info=e_os_error,
+            )
+        return False
+
+    try:
+        handle.initTrtLlmPlugins.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
+        handle.initTrtLlmPlugins.restype = ctypes.c_bool
+    except AttributeError as e_plugin_unavailable:
+        logger.warning(
+            "Unable to initialize the TensorRT-LLM plugin library",
+            exc_info=e_plugin_unavailable,
+        )
+        return False
+
+    try:
+        if handle.initTrtLlmPlugins(None, b"tensorrt_llm"):
+            logger.info("TensorRT-LLM plugin successfully initialized")
+            return True
+        else:
+            logger.warning("TensorRT-LLM plugin library failed in initialization")
+            return False
+    except Exception as e_initialization_error:
+        logger.warning(
+            "Exception occurred during TensorRT-LLM plugin library initialization",
+            exc_info=e_initialization_error,
+        )
+        return False
+    return False
+
+
+def load_tensorrt_llm_for_nccl() -> bool:
+    """
+    Attempts to load the TensorRT-LLM plugin and initialize it.
+    Either the env variable TRTLLM_PLUGINS_PATH can specify the path
+    Or the user can specify USE_TRTLLM_PLUGINS as either of (1, true, yes, on) to download the TRT-LLM distribution and load it
+
+    Returns:
+        bool: True if the plugin was successfully loaded and initialized, False otherwise.
+    """
+    if not is_platform_supported_for_trtllm():
+        return False
+    plugin_lib_path = os.environ.get("TRTLLM_PLUGINS_PATH")
+
+    if plugin_lib_path:
+        return load_and_initialize_trtllm_plugin(plugin_lib_path)
+    else:
+        # this option can be used by user if TRTLLM_PLUGINS_PATH is not set by user
+        use_trtllm_plugin = os.environ.get("USE_TRTLLM_PLUGINS", "0").lower() in (
+            "1",
+            "true",
+            "yes",
+            "on",
+        )
+        if not use_trtllm_plugin:
+            logger.warning(
+                "Neither TRTLLM_PLUGIN_PATH is set nor is it directed to download the shared library. Please set either of the two to use TRT-LLM libraries in torchTRT"
+            )
+            return False
+
+        plugin_lib_path = download_and_get_plugin_lib_path()
+        return load_and_initialize_trtllm_plugin(plugin_lib_path)  # type: ignore[arg-type]
+    return False