TRT-LLM loading mechanism tool

py/torch_tensorrt/dynamo/_compiler.py

@@ -98,6 +98,7 @@ def cross_compile_for_windows(
     enable_weight_streaming: bool = _defaults.ENABLE_WEIGHT_STREAMING,
     tiling_optimization_level: str = _defaults.TILING_OPTIMIZATION_LEVEL,
     l2_limit_for_tiling: int = _defaults.L2_LIMIT_FOR_TILING,
+    use_distributed_mode_trace: bool = _defaults.USE_DISTRIBUTED_MODE_TRACE,
     **kwargs: Any,
 ) -> torch.fx.GraphModule:
     """Compile an ExportedProgram module using TensorRT in Linux for Inference in Windows
@@ -173,6 +174,7 @@ def cross_compile_for_windows(
         enable_weight_streaming (bool): Enable weight streaming.
         tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support ["none", "fast", "moderate", "full"].
         l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).
+        use_distributed_mode_trace (bool):  Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model
         **kwargs: Any,
     Returns:
         torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT
@@ -332,6 +334,7 @@ def cross_compile_for_windows(
         "enable_weight_streaming": enable_weight_streaming,
         "tiling_optimization_level": tiling_optimization_level,
         "l2_limit_for_tiling": l2_limit_for_tiling,
+        "use_distributed_mode_trace": use_distributed_mode_trace,
     }
 
     # disable the following settings is not supported for cross compilation for windows feature
@@ -421,6 +424,7 @@ def compile(
     enable_weight_streaming: bool = _defaults.ENABLE_WEIGHT_STREAMING,
     tiling_optimization_level: str = _defaults.TILING_OPTIMIZATION_LEVEL,
     l2_limit_for_tiling: int = _defaults.L2_LIMIT_FOR_TILING,
+    use_distributed_mode_trace: bool = _defaults.USE_DISTRIBUTED_MODE_TRACE,
     **kwargs: Any,
 ) -> torch.fx.GraphModule:
     """Compile an ExportedProgram module for NVIDIA GPUs using TensorRT
@@ -498,6 +502,7 @@ def compile(
         enable_weight_streaming (bool): Enable weight streaming.
         tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support ["none", "fast", "moderate", "full"].
         l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).
+        use_distributed_mode_trace (bool):  Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model
         **kwargs: Any,
     Returns:
         torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT
@@ -674,6 +679,7 @@ def compile(
         "enable_weight_streaming": enable_weight_streaming,
         "tiling_optimization_level": tiling_optimization_level,
         "l2_limit_for_tiling": l2_limit_for_tiling,
+        "use_distributed_mode_trace": use_distributed_mode_trace,
     }
 
     settings = CompilationSettings(**compilation_options)
@@ -964,6 +970,7 @@ def convert_exported_program_to_serialized_trt_engine(
     enable_weight_streaming: bool = _defaults.ENABLE_WEIGHT_STREAMING,
     tiling_optimization_level: str = _defaults.TILING_OPTIMIZATION_LEVEL,
     l2_limit_for_tiling: int = _defaults.L2_LIMIT_FOR_TILING,
+    use_distributed_mode_trace: bool = _defaults.USE_DISTRIBUTED_MODE_TRACE,
     **kwargs: Any,
 ) -> bytes:
     """Convert an ExportedProgram to a serialized TensorRT engine
@@ -1029,6 +1036,7 @@ def convert_exported_program_to_serialized_trt_engine(
         enable_weight_streaming (bool): Enable weight streaming.
         tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support ["none", "fast", "moderate", "full"].
         l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).
+        use_distributed_mode_trace: bool = _defaults.USE_DISTRIBUTED_MODE_TRACE,
     Returns:
         bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs
     """
@@ -1147,6 +1155,7 @@ def convert_exported_program_to_serialized_trt_engine(
         "enable_weight_streaming": enable_weight_streaming,
         "tiling_optimization_level": tiling_optimization_level,
         "l2_limit_for_tiling": l2_limit_for_tiling,
+        "use_distributed_mode_trace": use_distributed_mode_trace,
     }
 
     settings = CompilationSettings(**compilation_options)

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -1002,66 +1002,3 @@ def args_bounds_check(
     args: Tuple[Argument, ...], i: int, replacement: Optional[Any] = None
 ) -> Any:
     return args[i] if len(args) > i and args[i] is not None else replacement
-
-
-def load_tensorrt_llm() -> bool:
-    """
-    Attempts to load the TensorRT-LLM plugin and initialize it.
-
-    Returns:
-        bool: True if the plugin was successfully loaded and initialized, False otherwise.
-    """
-    try:
-        import tensorrt_llm as trt_llm  # noqa: F401
-
-        _LOGGER.info("TensorRT-LLM successfully imported")
-        return True
-    except (ImportError, AssertionError) as e_import_error:
-        # Check for environment variable for the plugin library path
-        plugin_lib_path = os.environ.get("TRTLLM_PLUGINS_PATH")
-        if not plugin_lib_path:
-            _LOGGER.warning(
-                "TensorRT-LLM is not installed. Please install TensorRT-LLM or set TRTLLM_PLUGINS_PATH to the directory containing libnvinfer_plugin_tensorrt_llm.so to use converters for torch.distributed ops",
-            )
-            return False
-
-        _LOGGER.info(f"TensorRT-LLM Plugin lib path found: {plugin_lib_path}")
-        try:
-            # Load the shared library
-            handle = ctypes.CDLL(plugin_lib_path)
-            _LOGGER.info(f"Successfully loaded plugin library: {plugin_lib_path}")
-        except OSError as e_os_error:
-            _LOGGER.error(
-                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:
-            # Configure plugin initialization arguments
-            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:
-            # Initialize the plugin
-            TRT_LLM_PLUGIN_NAMESPACE = "tensorrt_llm"
-            if handle.initTrtLlmPlugins(None, TRT_LLM_PLUGIN_NAMESPACE.encode("utf-8")):
-                _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

py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py

@@ -11,11 +11,11 @@
 from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
     dynamo_tensorrt_converter,
 )
-from torch_tensorrt.dynamo.conversion.converter_utils import load_tensorrt_llm
 from torch_tensorrt.dynamo.lowering.passes.fuse_distributed_ops import (
     tensorrt_fused_nccl_all_gather_op,
     tensorrt_fused_nccl_reduce_scatter_op,
 )
+from torch_tensorrt.dynamo.utils import load_tensorrt_llm
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 

py/torch_tensorrt/dynamo/utils.py

@@ -1,7 +1,13 @@
 from __future__ import annotations
 
+import ctypes
 import gc
 import logging
+import os
+import shutil
+import subprocess
+import sys
+import urllib.request
 import warnings
 from dataclasses import fields, replace
 from enum import Enum
@@ -14,7 +20,7 @@
 from torch._subclasses.fake_tensor import FakeTensor
 from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
 from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import dtype
+from torch_tensorrt._enums import Platform, dtype
 from torch_tensorrt._features import ENABLED_FEATURES
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo import _defaults
@@ -812,3 +818,123 @@ def is_tegra_platform() -> bool:
     if torch.cuda.get_device_capability() in [(8, 7), (7, 2)]:
         return True
     return False
+
+
+def download_plugin_lib_path(py_version: str, platform: str) -> str:
+    plugin_lib_path = None
+
+    # Downloading TRT-LLM lib
+    # TODO: check how to fix the 0.18.0 hardcode below
+    base_url = "https://pypi.nvidia.com/tensorrt-llm/"
+    file_name = f"tensorrt_llm-0.18.0-{py_version}-{py_version}-{platform}.whl"
+    download_url = base_url + file_name
+    if not (os.path.exists(file_name)):
+        try:
+            logger.debug(f"Downloading {download_url} ...")
+            urllib.request.urlretrieve(download_url, file_name)
+            logger.debug("Download succeeded and TRT-LLM wheel is now present")
+        except subprocess.CalledProcessError as e:
+            logger.error(
+                "Download failed (file not found or connection issue). Error code:",
+                e.returncode,
+            )
+        except FileNotFoundError:
+            logger.error("wget is required but not found. Please install wget.")
+
+    # Proceeding with the unzip of the wheel file
+    # This will exist if the filename was already downloaded
+    if os.path.exists("./tensorrt_llm/libs/libnvinfer_plugin_tensorrt_llm.so"):
+        plugin_lib_path = "./tensorrt_llm/libs/" + "libnvinfer_plugin_tensorrt_llm.so"
+    else:
+        try:
+            import zipfile
+        except:
+            raise ImportError(
+                "zipfile module is required but not found. Please install zipfile"
+            )
+        with zipfile.ZipFile(file_name, "r") as zip_ref:
+            zip_ref.extractall(".")  # Extract to a folder named 'tensorrt_llm'
+            plugin_lib_path = (
+                "./tensorrt_llm/libs/" + "libnvinfer_plugin_tensorrt_llm.so"
+            )
+    return plugin_lib_path
+
+
+def load_tensorrt_llm() -> 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.
+    """
+    plugin_lib_path = os.environ.get("TRTLLM_PLUGINS_PATH")
+    if not plugin_lib_path:
+        # 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
+        else:
+            # this is used as the default py version
+            py_version = f"cp312"
+            platform = Platform.current_platform()
+
+            platform = str(platform).lower()
+            plugin_lib_path = download_plugin_lib_path(py_version, platform)
+
+    try:
+        # Load the shared TRT-LLM file
+        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}. "
+                f"The dependency libmpi.so is missing. "
+                f"Please install the packages libmpich-dev and libopenmpi-dev.",
+                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:
+        # Configure plugin initialization arguments
+        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:
+        # Initialize the plugin
+        TRT_LLM_PLUGIN_NAMESPACE = "tensorrt_llm"
+        if handle.initTrtLlmPlugins(None, TRT_LLM_PLUGIN_NAMESPACE.encode("utf-8")):
+            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