From 37cb8a778d507e076078255194800673cd942c16 Mon Sep 17 00:00:00 2001
From: Apurba Bose <44209735+apbose@users.noreply.github.com>
Date: Thu, 9 Jan 2025 13:47:18 -0800
Subject: [PATCH] Cherrypick: nccl ops multi gpu (#3342)

Signed-off-by: Naren Dasan <naren@narendasan.com>
Co-authored-by: Naren Dasan <1790613+narendasan@users.noreply.github.com>
---
 examples/distributed_inference/README.md      |   34 +
 .../distributed_inference/requirement.txt     |    3 +-
 .../tensor_parallel_initialize_dist.py        |   67 ++
 .../tensor_parallel_llama3.py                 |   26 +-
 .../tensor_parallel_simple_example.py         |   24 +-
 py/torch_tensorrt/dynamo/_defaults.py         |    1 +
 py/torch_tensorrt/dynamo/_settings.py         |    3 +
 py/torch_tensorrt/dynamo/backend/backends.py  |   56 +-
 .../dynamo/conversion/__init__.py             |    8 +-
 .../dynamo/conversion/aten_ops_converters.py  |    9 +-
 .../dynamo/conversion/converter_utils.py      |   74 +-
 .../conversion/custom_ops_converters.py       |   61 +
 .../dynamo/conversion/impl/__init__.py        |    1 +
 .../conversion/impl/elementwise/base.py       |    4 +-
 .../dynamo/conversion/impl/nccl_ops.py        |  120 ++
 .../dynamo/conversion/impl/select.py          |    4 +-
 .../dynamo/lowering/passes/__init__.py        |    1 +
 .../lowering/passes/_aten_lowering_pass.py    |    2 +
 .../passes/_modify_reshape_complex_nodes.py   |  105 ++
 .../_replace_complex_placeholder_to_tuple.py  |  112 ++
 .../lowering/passes/fuse_distributed_ops.py   |   80 ++
 .../dynamo/lowering/passes/pass_utils.py      |   39 +
 pyproject.toml                                |    3 +-
 uv.lock                                       | 1072 ++++++++++++++++-
 24 files changed, 1845 insertions(+), 64 deletions(-)
 create mode 100644 examples/distributed_inference/tensor_parallel_initialize_dist.py
 create mode 100644 py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py
 create mode 100644 py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py
 create mode 100644 py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py
 create mode 100644 py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py
 create mode 100644 py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py

diff --git a/examples/distributed_inference/README.md b/examples/distributed_inference/README.md
index 4c88570b6f..d4cf9508e1 100644
--- a/examples/distributed_inference/README.md
+++ b/examples/distributed_inference/README.md
@@ -14,3 +14,37 @@ See the examples started with `data_parallel` for more details.
 Here we use torch.distributed as an example, but compilation with tensor parallelism is agnostic to the implementation framework as long as the module is properly sharded.
 
 torchrun --nproc_per_node=2 tensor_parallel_llama2.py
+
+3. Tensor parallel distributed inference using nccl ops plugin
+
+ apt install libmpich-dev
+
+ apt install libopenmpi-dev
+
+ #For python3.10
+
+ pip install tensorrt-llm
+
+ For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so. Please set that in the environment variable export TRTLLM_PLUGINS_PATH={lib_path}. For example, we have already set the variable in initialize_distributed_env(). You can replace this with your TRTLLM_PLUGINS_PATH and unset it there
+
+ #then pip install the tensorrt and torch version compatible with installed torchTRT
+
+ mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py
+
+ #For other python
+
+4. Tensor parallel distributed llama3 inference using nccl ops plugin
+
+ apt install libmpich-dev
+
+ apt install libopenmpi-dev
+
+#For python3.10
+
+ pip install tensorrt-llm
+
+ For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so
+
+ #then pip install the tensorrt and torch version compatible with installed torchTRT
+
+ mpirun -n 2 --allow-run-as-root python tensor_parallel_llama3.py
diff --git a/examples/distributed_inference/requirement.txt b/examples/distributed_inference/requirement.txt
index 6d8e0aa9f2..bb3b0f28f1 100644
--- a/examples/distributed_inference/requirement.txt
+++ b/examples/distributed_inference/requirement.txt
@@ -1,3 +1,4 @@
 accelerate
 transformers
-diffusers
\ No newline at end of file
+diffusers
+tensorrt-llm
\ No newline at end of file
diff --git a/examples/distributed_inference/tensor_parallel_initialize_dist.py b/examples/distributed_inference/tensor_parallel_initialize_dist.py
new file mode 100644
index 0000000000..21e4cbc282
--- /dev/null
+++ b/examples/distributed_inference/tensor_parallel_initialize_dist.py
@@ -0,0 +1,67 @@
+import logging
+import os
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import tensorrt as trt
+import torch
+import torch.distributed as dist
+from torch.distributed._tensor.device_mesh import init_device_mesh
+
+
+def find_repo_root(max_depth=10):
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    for i in range(max_depth):
+        files = os.listdir(dir_path)
+        if "MODULE.bazel" in files:
+            return dir_path
+        else:
+            dir_path = os.path.dirname(dir_path)
+
+    raise RuntimeError("Could not find repo root")
+
+
+def initialize_logger(rank, logger_file_name):
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w")
+    fh.setLevel(logging.INFO)
+    logger.addHandler(fh)
+    return logger
+
+
+# This is required for env initialization since we use mpirun
+def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=29500):
+    local_rank = int(
+        os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
+    )
+    world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", world_size))
+
+    # Set up environment variable to run with mpirun
+    os.environ["RANK"] = str(local_rank)
+    os.environ["WORLD_SIZE"] = str(world_size)
+    os.environ["MASTER_ADDR"] = "127.0.0.1"
+    os.environ["MASTER_PORT"] = str(port)
+    os.environ["TRTLLM_PLUGINS_PATH"] = (
+        find_repo_root() + "/lib/libnvinfer_plugin_tensorrt_llm.so"
+    )
+
+    # Necessary to assign a device to each rank.
+    torch.cuda.set_device(local_rank)
+
+    # We use nccl backend
+    dist.init_process_group("nccl")
+
+    # set a manual seed for reproducibility
+    torch.manual_seed(1111)
+
+    device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,))
+    rank = device_mesh.get_rank()
+    assert rank == local_rank
+    logger = initialize_logger(rank, logger_file_name)
+    device_id = (
+        rank % torch.cuda.device_count()
+    )  # Ensure each rank gets a unique device
+    torch.cuda.set_device(device_id)
+
+    return device_mesh, world_size, rank, logger
diff --git a/examples/distributed_inference/tensor_parallel_llama3.py b/examples/distributed_inference/tensor_parallel_llama3.py
index fc03a64386..998c378be2 100644
--- a/examples/distributed_inference/tensor_parallel_llama3.py
+++ b/examples/distributed_inference/tensor_parallel_llama3.py
@@ -5,27 +5,25 @@
 import time
 
 import torch
-import torch_tensorrt
 from llama3_model import ModelArgs, ParallelTransformer
+from tensor_parallel_initialize_dist import initialize_distributed_env
 from torch.distributed._composable.fsdp import MixedPrecisionPolicy
 from torch.distributed._composable.fsdp.fully_shard import fully_shard
 from torch.distributed._tensor import Replicate, Shard
 from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
     checkpoint_wrapper,
 )
-from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
 
-_rank = int(os.environ["RANK"])
-_world_size = int(os.environ["WORLD_SIZE"])
-tp_size = 2
-
-logger = logging.getLogger()
-logger.setLevel(logging.INFO)
-fh = logging.FileHandler(f"./tensor_parallel_log_{_rank}.log", mode="w")
-fh.setLevel(logging.INFO)
-logger.addHandler(fh)
+device_mesh, _world_size, _rank, logger = initialize_distributed_env(
+    "./tensor_parallel_llama3"
+)
+# Import should be after initialization of the TRT-LLM plugin .so path
+import tensorrt_llm
 
-tp_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
+logger.info(f"Starting PyTorch TP example on rank {_rank}.")
+assert (
+    _world_size % 2 == 0
+), f"TP examples require even number of GPUs, but got {_world_size} gpus"
 
 model_args = ModelArgs(
     vocab_size=32000,
@@ -38,7 +36,7 @@
 )
 
 with torch.no_grad():
-    model = ParallelTransformer(model_args, tp_mesh)
+    model = ParallelTransformer(model_args, device_mesh)
     torch.manual_seed(0)
     inp = torch.randint(32000, (8, 256), device="cuda")
     python_result = model(inp)
@@ -53,7 +51,7 @@
             "use_python_runtime": True,
             "workspace_size": 1 << 33,
             "debug": False,
-            "timing_cache_path": "/opt/file/cache/timing_cache_llama.bin",
+            "use_aot_joint_export": False,
         },
         dynamic=False,
     )
diff --git a/examples/distributed_inference/tensor_parallel_simple_example.py b/examples/distributed_inference/tensor_parallel_simple_example.py
index 470487a751..837648fdb4 100755
--- a/examples/distributed_inference/tensor_parallel_simple_example.py
+++ b/examples/distributed_inference/tensor_parallel_simple_example.py
@@ -1,18 +1,22 @@
-import os
-import sys
 import time
 
+import tensorrt as trt
 import torch
 import torch.nn as nn
 import torch_tensorrt
+from tensor_parallel_initialize_dist import initialize_distributed_env
 from torch.distributed._tensor import Shard
-from torch.distributed._tensor.device_mesh import init_device_mesh
 from torch.distributed.tensor.parallel import (
     ColwiseParallel,
     RowwiseParallel,
     parallelize_module,
 )
 
+device_mesh, _world_size, _rank, logger = initialize_distributed_env(
+    "./tensor_parallel_simple_example"
+)
+import tensorrt_llm
+
 """
 This example copies some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py
 """
@@ -36,14 +40,7 @@ def forward(self, x):
         return x
 
 
-# create a device mesh based on the given world_size.
-_world_size = int(os.environ["WORLD_SIZE"])
-
-device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
-_rank = device_mesh.get_rank()
-
-
-print(f"Starting PyTorch TP example on rank {_rank}.")
+logger.info(f"Starting PyTorch TP example on rank {_rank}.")
 assert (
     _world_size % 2 == 0
 ), f"TP examples require even number of GPUs, but got {_world_size} gpus"
@@ -78,6 +75,7 @@ def forward(self, x):
         "enabled_precisions": {torch.float32, torch.float16},
         "use_python_runtime": True,
         "min_block_size": 1,
+        "use_aot_joint_export": False,
     },
     dynamic=False,
 )
@@ -91,9 +89,9 @@ def forward(self, x):
     output = tp_model(inp)
     end = time.time()
     if i == 0:
-        print(f"Compilation time is {end-start}")
+        logger.info(f"Compilation time is {end-start}")
         assert (
             python_result - output
         ).std() < 0.01, "Compilation result is not correct."
     elif _rank == 0:
-        print(f"Inference time is {end-start}")
+        logger.info(f"Inference time is {end-start}")
diff --git a/py/torch_tensorrt/dynamo/_defaults.py b/py/torch_tensorrt/dynamo/_defaults.py
index 76630a75a5..18932e6cd0 100644
--- a/py/torch_tensorrt/dynamo/_defaults.py
+++ b/py/torch_tensorrt/dynamo/_defaults.py
@@ -46,6 +46,7 @@
 IMMUTABLE_WEIGHTS = True
 ENABLE_WEIGHT_STREAMING = False
 ENABLE_CROSS_COMPILE_FOR_WINDOWS = False
+USE_AOT_JOINT_EXPORT = True
 
 
 def default_device() -> Device:
diff --git a/py/torch_tensorrt/dynamo/_settings.py b/py/torch_tensorrt/dynamo/_settings.py
index 7a22663af3..05fb5ce094 100644
--- a/py/torch_tensorrt/dynamo/_settings.py
+++ b/py/torch_tensorrt/dynamo/_settings.py
@@ -33,6 +33,7 @@
     STRIP_ENGINE_WEIGHTS,
     TIMING_CACHE_PATH,
     TRUNCATE_DOUBLE,
+    USE_AOT_JOINT_EXPORT,
     USE_EXPLICIT_TYPING,
     USE_FAST_PARTITIONER,
     USE_FP32_ACC,
@@ -91,6 +92,7 @@ class CompilationSettings:
         enable_weight_streaming (bool): Enable weight streaming.
         enable_cross_compile_for_windows (bool): By default this is False means TensorRT engines can only be executed on the same platform where they were built.
             True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows
+        use_aot_joint_export (bool): Use aot_export_joint_simple, else wrap backend with AOT_autograd, required for distributed tensors
     """
 
     enabled_precisions: Set[dtype] = field(default_factory=lambda: ENABLED_PRECISIONS)
@@ -131,6 +133,7 @@ class CompilationSettings:
     immutable_weights: bool = IMMUTABLE_WEIGHTS
     enable_weight_streaming: bool = ENABLE_WEIGHT_STREAMING
     enable_cross_compile_for_windows: bool = ENABLE_CROSS_COMPILE_FOR_WINDOWS
+    use_aot_joint_export: bool = USE_AOT_JOINT_EXPORT
 
 
 _SETTINGS_TO_BE_ENGINE_INVARIANT = (
diff --git a/py/torch_tensorrt/dynamo/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py
index 59bd7d011d..ef04745562 100644
--- a/py/torch_tensorrt/dynamo/backend/backends.py
+++ b/py/torch_tensorrt/dynamo/backend/backends.py
@@ -1,17 +1,20 @@
 from __future__ import annotations
 
+import functools
 import logging
 import unittest
 from typing import Any, Callable, Sequence
 
 import torch
 import torch._dynamo as td
+from torch._dynamo.backends.common import aot_autograd
 from torch._dynamo.utils import detect_fake_mode
 from torch._functorch.aot_autograd import aot_export_joint_simple
 from torch_tensorrt.dynamo import CompilationSettings
 from torch_tensorrt.dynamo._compiler import compile_module
 from torch_tensorrt.dynamo.lowering import (
     get_decompositions,
+    modify_reshape_complex_nodes,
     post_lowering,
     remove_detach,
     remove_sym_nodes,
@@ -49,7 +52,25 @@ def aot_torch_tensorrt_aten_backend(
     gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any
 ) -> torch.nn.Module:
     settings, engine_cache = parse_dynamo_kwargs(kwargs)
-    return _pretraced_backend(gm, sample_inputs, settings, engine_cache)
+    if settings.use_aot_joint_export:
+        return _pretraced_backend(gm, sample_inputs, settings, engine_cache)
+    logger.debug("Wrapping the backend with aot_autograd\n")
+    _pretraced_backend_autograd = functools.partial(
+        _pretraced_backend, settings=settings, engine_cache=engine_cache
+    )
+    settings_aot_autograd = {}
+    settings_aot_autograd["decompostions"] = get_decompositions(
+        settings.enable_experimental_decompositions
+    )
+    # This is added since detach lowering leads to alias nodes
+    # Error - View operation returned a tensor that is the same as the input base tensor
+    # torch nop_decompositions in torch/_decomp/decompositions.py
+    if aten.detach in settings_aot_autograd["decompositions"]:
+        del settings_aot_autograd["decompositions"][aten.detach]
+    return aot_autograd(
+        fw_compiler=_pretraced_backend_autograd,
+        decompositions=get_decompositions(settings.enable_experimental_decompositions),
+    )(gm, sample_inputs)
 
 
 def _pretraced_backend(
@@ -89,15 +110,26 @@ def _pretraced_backend(
             # Remove detach nodes
             remove_detach(gm, settings)
 
+            complexInputIndices = []
+            for i, torch_input in enumerate(torch_inputs):
+                if torch_inputs[i].dtype == torch.complex64:
+                    complexInputIndices.append(i)
+                    torch_input_real = torch_inputs[i].real
+                    torch_input_imaginary = torch_inputs[i].imag
+                    torch_inputs[i] = torch.stack(
+                        (torch_input_real, torch_input_imaginary), dim=-1
+                    )
+
             # Invoke AOTAutograd to translate operators to aten
-            gm = aot_export_joint_simple(
-                gm,
-                sample_inputs,
-                trace_joint=False,
-                decompositions=get_decompositions(
-                    settings.enable_experimental_decompositions
-                ),
-            )
+            if settings.use_aot_joint_export:
+                gm = aot_export_joint_simple(
+                    gm,
+                    sample_inputs,
+                    trace_joint=False,
+                    decompositions=get_decompositions(
+                        settings.enable_experimental_decompositions
+                    ),
+                )
 
             logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph))
 
@@ -105,6 +137,12 @@ def _pretraced_backend(
 
             logger.debug("Lowered Input graph:\n " + str(gm.graph))
 
+            if complexInputIndices:
+                modify_reshape_complex_nodes(gm, complexInputIndices)
+                logger.debug(
+                    "Input graph after modifying complex nodes:\n " + str(gm.graph)
+                )
+
             torchtrt_inputs = prepare_inputs(
                 torch_inputs, disable_memory_format_check=True
             )
diff --git a/py/torch_tensorrt/dynamo/conversion/__init__.py b/py/torch_tensorrt/dynamo/conversion/__init__.py
index 26a428ed8d..f41540a0cf 100644
--- a/py/torch_tensorrt/dynamo/conversion/__init__.py
+++ b/py/torch_tensorrt/dynamo/conversion/__init__.py
@@ -1,4 +1,10 @@
-from . import aten_ops_converters, ops_evaluators, plugins, prims_ops_converters
+from . import (
+    aten_ops_converters,
+    custom_ops_converters,
+    ops_evaluators,
+    plugins,
+    prims_ops_converters,
+)
 from ._conversion import convert_module, interpret_module_to_result
 from ._ConversionContext import ConversionContext
 from ._ConverterRegistry import *  # noqa: F403
diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 16d20d3c76..5254c6a0ac 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -2,7 +2,7 @@
 
 import logging
 import operator
-from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
+from typing import Callable, Dict, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -16,6 +16,7 @@
     has_static_shapes_in_args,
 )
 from torch_tensorrt.dynamo.conversion.converter_utils import (
+    args_bounds_check,
     enforce_tensor_types,
     get_positive_dim,
     is_only_operator_on_placeholder,
@@ -25,12 +26,6 @@
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
 
-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 get_ir(target: Target) -> SourceIR:
     target_module = getattr(target, "__module__", "None")
     if any(
diff --git a/py/torch_tensorrt/dynamo/conversion/converter_utils.py b/py/torch_tensorrt/dynamo/conversion/converter_utils.py
index 937ed27fe4..f4bb4877cc 100644
--- a/py/torch_tensorrt/dynamo/conversion/converter_utils.py
+++ b/py/torch_tensorrt/dynamo/conversion/converter_utils.py
@@ -1,10 +1,11 @@
 import collections
+import ctypes
 import functools
 import logging
+import os
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, overload
 
 import numpy as np
-import tensorrt as trt
 import torch
 import torch_tensorrt.dynamo.conversion.impl as impl
 from torch.fx.node import Argument, Target
@@ -18,6 +19,8 @@
     DynamoConverterImplSignature,
 )
 
+import tensorrt as trt
+
 from ..types import Shape, TRTDataType, TRTLayer, TRTTensor
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -913,3 +916,72 @@ def set_layer_name(
         else f"{source_ir}_ops.{target.__name__}"
     )
     layer.name = f"[{layer.type.name}]-[{target_name}]-[{name}]"
+
+
+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
diff --git a/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py
new file mode 100644
index 0000000000..17850fabce
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py
@@ -0,0 +1,61 @@
+# mypy: disallow-untyped-decorators=False
+
+import logging
+from typing import Dict, Sequence, Tuple, Union
+
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
+from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
+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,
+)
+
+import tensorrt as trt
+
+_LOGGER: logging.Logger = logging.getLogger(__name__)
+
+if load_tensorrt_llm():
+
+    @dynamo_tensorrt_converter(tensorrt_fused_nccl_all_gather_op)
+    def fused_nccl_gather(
+        ctx: ConversionContext,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        name: str,
+    ) -> Union[trt.ITensor, Sequence[trt.ITensor]]:
+        return impl.distributed.nccl_gather(
+            ctx,
+            target,
+            SourceIR.ATEN,
+            name,
+            [args[0]],
+        )
+
+    @dynamo_tensorrt_converter(tensorrt_fused_nccl_reduce_scatter_op)
+    def fused_nccl_reduce_scatter(
+        ctx: ConversionContext,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        name: str,
+    ) -> Union[trt.ITensor, Sequence[trt.ITensor]]:
+        return impl.distributed.nccl_reduce_scatter(
+            ctx,
+            target,
+            SourceIR.ATEN,
+            name,
+            [args[0]],
+        )
+
+    breakpoint()
+else:
+    _LOGGER.debug(
+        "Did not load torch.distributed converters since TensorRT-LLM is not available"
+    )
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
index c1187f0dd9..75f7492591 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
@@ -13,6 +13,7 @@
     full,
     grid,
     matmul,
+    nccl_ops,
     normalization,
     pad,
     permutation,
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index 3ce1714a7d..4a4b33abea 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -3,7 +3,6 @@
 from typing import Any, Callable, Optional, Union
 
 import numpy as np
-import tensorrt as trt
 import torch
 from torch.fx.node import Target
 from torch_tensorrt import _enums
@@ -14,12 +13,13 @@
     broadcast_to_same_shape,
     cast_trt_tensor,
     get_trt_tensor,
-    broadcast,
     has_dynamic_shape,
     set_layer_name,
 )
 from torch_tensorrt.dynamo.types import TRTElementWiseOp, TRTTensor
 
+import tensorrt as trt
+
 
 def get_python_op_from_trt_elementwise_op(
     trt_op: TRTElementWiseOp,
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py
new file mode 100644
index 0000000000..013268f803
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py
@@ -0,0 +1,120 @@
+import os
+from enum import IntEnum, IntFlag, auto
+from typing import Optional, Tuple, Union
+
+import numpy as np
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
+from torch_tensorrt.fx.converters.converter_utils import SourceIR, set_layer_name
+
+import tensorrt as trt
+
+
+# class for AllReduce
+class AllReduceStrategy(IntEnum):
+    """Warning: actual definition is in kernels/customAllReduceKernels.h.
+
+    They must be kept in sync.
+    """
+
+    NCCL = 0
+    ONESHOT = 1
+    TWOSHOT = 2
+    AUTO = 3
+
+
+class AllReduceConfig(IntFlag):
+    """Warning: actual definition is in kernels/customAllReduceKernels.h.
+
+    They must be kept in sync
+    """
+
+    USE_MEMCPY = auto()
+    PUSH_MODE = auto()
+
+
+def nccl_gather(
+    ctx: ConversionContext,
+    target: Union[Target, str],
+    source_ir: Optional[SourceIR],
+    name: str,
+    plug_inputs: Tuple[Argument, ...],
+) -> trt.ITensor:
+    allgather_plg_creator = trt.get_plugin_registry().get_plugin_creator(
+        "AllGather", "1", "tensorrt_llm"
+    )
+    assert allgather_plg_creator is not None
+    _world_size = os.environ.get("WORLD_SIZE")
+    if _world_size is not None:
+        world_size = int(_world_size)
+    else:
+        raise RuntimeError(
+            "The WORLD_SIZE env variable is not set in distributed environment"
+        )
+    group = list(range(world_size))
+    group = trt.PluginField(
+        "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
+    )
+    p_dtype = trt.float32
+    pf_type = trt.PluginField(
+        "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
+    )
+    pfc = trt.PluginFieldCollection([group, pf_type])
+    allgather = allgather_plg_creator.create_plugin("allgather", pfc)
+    layer = ctx.net.add_plugin_v2(plug_inputs, allgather)
+    set_layer_name(layer, target, name, source_ir)
+    return layer.get_output(0)
+
+
+def nccl_reduce_scatter(
+    ctx: ConversionContext,
+    target: Union[Target, str],
+    source_ir: Optional[SourceIR],
+    name: str,
+    plug_inputs: Tuple[Argument, ...],
+) -> trt.ITensor:
+    allreduce_plg_creator = trt.get_plugin_registry().get_plugin_creator(
+        "ReduceScatter", "1", "tensorrt_llm"
+    )
+
+    assert allreduce_plg_creator is not None
+
+    counter = 0
+    strategy = AllReduceStrategy.NCCL
+    config = AllReduceConfig(0)
+    _world_size = os.environ.get("WORLD_SIZE")
+    if _world_size is not None:
+        world_size = int(_world_size)
+    else:
+        raise RuntimeError(
+            "The WORLD_SIZE env variable is not set in distributed environment"
+        )
+    group = list(range(world_size))
+    group = trt.PluginField(
+        "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
+    )
+
+    p_dtype = trt.float16
+    pf_dtype = trt.PluginField(
+        "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
+    )
+    pfc = [group, pf_dtype]
+    p_strategy = trt.PluginField(
+        "strategy", np.array([int(strategy)], np.int8), trt.PluginFieldType.INT8
+    )
+    pfc.append(p_strategy)
+    p_config = trt.PluginField(
+        "config", np.array([int(config)], np.int8), trt.PluginFieldType.INT8
+    )
+    pfc.append(p_config)
+    p_counter = trt.PluginField(
+        "counter", np.array([counter], np.int32), trt.PluginFieldType.INT32
+    )
+    pfc.append(p_counter)
+
+    pfc = trt.PluginFieldCollection(pfc)
+    ar_plug = allreduce_plg_creator.create_plugin("allreduce", pfc)
+
+    layer = ctx.net.add_plugin_v2(plug_inputs, ar_plug)
+    set_layer_name(layer, target, name, source_ir)
+    return layer.get_output(0)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/select.py b/py/torch_tensorrt/dynamo/conversion/impl/select.py
index 0d55c5f014..dc5458d2c8 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/select.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/select.py
@@ -2,7 +2,6 @@
 from typing import Optional, Sequence, Union
 
 import numpy as np
-import tensorrt as trt
 import torch
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
@@ -16,7 +15,6 @@
     to_numpy,
 )
 from torch_tensorrt.dynamo.conversion.impl.elementwise import convert_binary_elementwise
-from torch_tensorrt.dynamo.conversion.impl.shape import get_shape_with_dynamic_shape
 from torch_tensorrt.dynamo.conversion.impl.shape import shape as get_shape
 from torch_tensorrt.dynamo.utils import DYNAMIC_DIM
 from torch_tensorrt.fx.converters.converter_utils import (
@@ -25,6 +23,8 @@
 )
 from torch_tensorrt.fx.types import TRTTensor
 
+import tensorrt as trt
+
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
 
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
index c0e2803e60..716c6505fe 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
@@ -1,3 +1,4 @@
 from ._aten_lowering_pass import *
+from ._modify_reshape_complex_nodes import modify_reshape_complex_nodes
 from .remove_sym_nodes import remove_sym_nodes
 from .repair_input_aliasing import repair_input_aliasing
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
index 661c76d3b6..f24fd8ec21 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
@@ -6,6 +6,7 @@
 
 from .accumulate_fp32_matmul import accumulate_fp32_matmul
 from .constant_folding import constant_fold
+from .fuse_distributed_ops import fuse_distributed_ops
 from .fuse_prims_broadcast import fuse_prims_broadcast
 from .lower_scaled_dot_product_attention import lower_scaled_dot_product_attention
 from .pass_manager import DynamoPassManager
@@ -22,6 +23,7 @@
         constant_fold,
         repair_input_as_output,
         fuse_prims_broadcast,
+        fuse_distributed_ops,
         replace_max_pool_with_indices,
         lower_scaled_dot_product_attention,
         view_to_reshape,
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py
new file mode 100644
index 0000000000..f8ca1f71b9
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py
@@ -0,0 +1,105 @@
+import logging
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+    clean_up_graph_after_modifications,
+    find_complex_nodes,
+)
+
+from ._replace_complex_placeholder_to_tuple import replace_complex_placeholder_to_tuple
+
+
+def tensorrt_complex_mul(args0, args1):
+    args0_real, args0_imag = torch.ops.aten.split.Tensor(args0, 1, -1)
+    args1_real, args1_imag = torch.ops.aten.split.Tensor(args1, 1, -1)
+
+    args0_real = torch.ops.aten.squeeze.dim(args0_real, -1)
+    args0_imag = torch.ops.aten.squeeze.dim(args0_imag, -1)
+    args1_real = torch.ops.aten.squeeze.dim(args1_real, -1)
+    args1_imag = torch.ops.aten.squeeze.dim(args1_imag, -1)
+
+    complex_mul_real = torch.ops.aten.sub(
+        torch.ops.aten.mul(args0_real, args1_real),
+        torch.ops.aten.mul(args0_imag, args1_imag),
+    )
+    complex_mul_imag = torch.ops.aten.add(
+        torch.ops.aten.mul(args0_real, args1_imag),
+        torch.ops.aten.mul(args0_imag, args1_real),
+    )
+
+    return torch.ops.aten.stack((complex_mul_real, complex_mul_imag), -1)
+
+
+def remove_complex_real_view_nodes(gm: torch.fx.GraphModule):
+    modified_graph = False
+    nodes_to_remove = []
+    for node in gm.graph.nodes:
+        if "view_as_complex" in node.name or "view_as_real" in node.name:
+            nodes_to_remove.append(node)
+
+    for node in nodes_to_remove:
+        input_node = node.args[0] if node.args else None
+
+        for other_node in gm.graph.nodes:
+            new_args = tuple(
+                input_node if arg is node else arg for arg in other_node.args
+            )
+            other_node.args = new_args
+
+        gm.graph.erase_node(node)
+        modified_graph = True
+
+    if modified_graph:
+        gm = clean_up_graph_after_modifications(gm)
+        logger.debug(
+            f"Graph after removing view_as_complex nodes and view_as_real nodes:\n{gm.graph}"
+        )
+
+
+def modify_reshape_nodes(gm: torch.fx.GraphModule, complex_nodes):
+    for node in gm.graph.nodes:
+        if node in complex_nodes:
+            # slice and transpose will remain same
+            if "reshape" in node.name:
+                new_shape = list(node.args[1]) + [2]
+                node.args = (node.args[0], tuple(new_shape))
+
+
+def modify_mul_nodes(gm: torch.fx.GraphModule, complex_nodes):
+    modified_graph = False
+    for node in gm.graph.nodes:
+        if node in complex_nodes:
+            if "mul" in node.name:
+                complex_mul_args = (node.args[0], node.args[1])
+                with gm.graph.inserting_after(node):
+                    replacement_node = gm.graph.create_node(
+                        op="call_function",
+                        target=tensorrt_complex_mul,
+                        args=complex_mul_args,
+                    )
+                node.replace_all_uses_with(replacement_node)
+                replacement_node.meta.update(node.meta)
+                modified_graph = True
+                gm.graph.erase_node(node)
+
+    if modified_graph:
+        gm = clean_up_graph_after_modifications(gm)
+        logger.debug(
+            f"Graph after custom complex mul nodes is applied to the graph:\n{gm.graph}"
+        )
+
+
+def modify_complex_nodes(gm: torch.fx.GraphModule, complex_nodes):
+    modify_reshape_nodes(gm, complex_nodes)
+    remove_complex_real_view_nodes(gm)
+    modify_mul_nodes(gm, complex_nodes)
+
+
+def modify_reshape_complex_nodes(gm: torch.fx.GraphModule, complexInputIndices):
+    complex_nodes = find_complex_nodes(gm)
+    if complex_nodes:
+        replace_complex_placeholder_to_tuple(gm, complexInputIndices)
+        modify_complex_nodes(gm, complex_nodes)
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py
new file mode 100644
index 0000000000..e2edec3d28
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py
@@ -0,0 +1,112 @@
+import logging
+from typing import List, Tuple
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensorMode
+from torch.fx.node import _get_qualified_name
+from torch_tensorrt.dynamo.conversion.converter_utils import args_bounds_check
+
+# dead-code elimination, linting, and recompilation for graph, in-place
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+    clean_up_graph_after_modifications,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def replace_complex_placeholder_to_tuple(
+    gm: torch.fx.GraphModule,
+    inputListindices: List[int],
+) -> torch.fx.GraphModule:
+    modified_graph = False
+    input_arg_list = [f"arg{inputListIndex}_1" for inputListIndex in inputListindices]
+    for node in gm.graph.nodes:
+        if node.op == "placeholder" and node.target in input_arg_list:
+            from torch._subclasses.fake_tensor import FakeTensorMode
+
+            node_shape = node.meta["val"].size()
+            new_node_shape = node_shape + (2,)
+            new_node_dtype = None
+            if node.meta["val"].dtype == torch.complex64:
+                new_node_dtype = torch.float32
+            else:
+                new_node_dtype = torch.float64
+            fake_mode = FakeTensorMode()
+
+            real_tensor = torch.empty(new_node_shape, dtype=new_node_dtype)
+            with FakeTensorMode() as fake_mode:
+                new_placeholder_tuple = fake_mode.from_tensor(real_tensor)
+            node.meta["val"] = new_placeholder_tuple
+            modified_graph = True
+            # propagate the meta data change for the downstream ops
+            # TODO:to check if this is required in all cases
+            propogate_complex_num_shape_change_till_complex_mul(gm, node, fake_mode)
+
+    # If graph was modified, clean it up
+    if modified_graph:
+        gm = clean_up_graph_after_modifications(gm)
+        logger.debug(
+            f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}"
+        )
+
+    return gm
+
+
+def infer_slice_shape(node: torch.fx.Node) -> Tuple[int, ...]:
+    input_shape = node.args[0].meta["val"].shape
+    slice_args = node.args
+    dim = slice_args[1]
+    start = slice_args[2]
+    end = slice_args[3]
+    step = args_bounds_check(slice_args, 4, replacement=1)
+    new_shape = list(input_shape)
+    new_shape[dim] = (end - start + step - 1) // step
+    return tuple(new_shape)
+
+
+def infer_reshape_shape(node: torch.fx.Node) -> torch.fx.node.Argument:
+    return node.args[1]
+
+
+shape_inference_funcs = {
+    "torch.ops.aten.slice.Tensor": infer_slice_shape,
+    "torch.ops.aten.reshape.default": infer_reshape_shape,
+}
+
+
+# Please note this function is for the use case of Llama model
+# with complex placeholder->reshape->slice->complex mul
+# Hence mul is the terminating op
+def propogate_complex_num_shape_change_till_complex_mul(
+    node: torch.fx.Node, start_node: torch.fx.Node, fake_mode: FakeTensorMode
+) -> None:
+    visited_nodes = set()
+    stack = [start_node]
+    while stack:
+        node = stack.pop()
+        if node in visited_nodes:
+            continue
+        visited_nodes.add(node)
+        update_node_meta(node, fake_mode)
+        for user in node.users:
+            if (
+                user.op == "call_function"
+                and _get_qualified_name(user.target) == "torch.ops.aten.mul.Tensor"
+            ):
+                continue
+            stack.append(user)
+
+
+def update_node_meta(node: torch.fx.Node, fake_mode: FakeTensorMode) -> None:
+    op_name = node.name
+    op_target = node.target
+
+    if node.op == "call_function":
+        op_target = _get_qualified_name(node.target)
+
+    if op_target in shape_inference_funcs:
+        new_shape = shape_inference_funcs[op_target](node)
+        real_tensor = torch.empty(new_shape, dtype=node.meta["val"].dtype)
+        node.meta["val"] = fake_mode.from_tensor(real_tensor)
+    else:
+        print("No shape for the inference function", {op_name})
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py
new file mode 100644
index 0000000000..f709f177d6
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py
@@ -0,0 +1,80 @@
+import logging
+from typing import Any
+
+import torch
+from torch_tensorrt.dynamo._settings import CompilationSettings
+
+# dead-code elimination, linting, and recompilation for graph, in-place
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+    clean_up_graph_after_modifications,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: @apbose make these actual torch custom ops, should allow aot_export
+def tensorrt_fused_nccl_all_gather_op(
+    inp: Any, group_size: int, group_name: str
+) -> torch.Tensor:
+    return torch.ops._c10d_functional.wait_tensor.default(
+        torch.ops._c10d_functional.all_gather_into_tensor.default(
+            inp, group_size, group_name
+        )
+    )
+
+
+def tensorrt_fused_nccl_reduce_scatter_op(
+    inp: Any, reduce_op: str, group_size: int, group_name: str
+) -> torch.Tensor:
+    return torch.ops._c10d_functional.wait_tensor.default(
+        torch.ops._c10d_functional.reduce_scatter_tensor.default(
+            inp, reduce_op, group_size, group_name
+        )
+    )
+
+
+def fuse_distributed_ops(
+    gm: torch.fx.GraphModule, settings: CompilationSettings
+) -> torch.fx.GraphModule:
+    modified_graph = False
+    for node in gm.graph.nodes:
+        if (
+            node.target
+            in (
+                torch.ops._c10d_functional.all_gather_into_tensor.default,
+                torch.ops._c10d_functional.reduce_scatter_tensor.default,
+            )
+            and len(node.users) == 1
+            and list(node.users)[0].target
+            == torch.ops._c10d_functional.wait_tensor.default
+        ):
+            wait_tensor_node = list(node.users)[0]
+            fused_op = None
+            if node.target == torch.ops._c10d_functional.all_gather_into_tensor.default:
+                with gm.graph.inserting_after(wait_tensor_node):
+                    fused_node = gm.graph.create_node(
+                        op="call_function",
+                        target=tensorrt_fused_nccl_all_gather_op,  # Define your custom fused function
+                        args=(node.args[0], node.args[1], node.args[2]),
+                    )
+            else:
+                fused_node = gm.graph.create_node(
+                    op="call_function",
+                    target=tensorrt_fused_nccl_reduce_scatter_op,  # Define your custom fused function
+                    args=(node.args[0], node.args[1], node.args[2], node.args[3]),
+                )
+
+            wait_tensor_node.replace_all_uses_with(fused_node)
+            fused_node.meta.update(node.meta)
+            modified_graph = True
+            gm.graph.erase_node(wait_tensor_node)
+            gm.graph.erase_node(node)
+
+    # If graph was modified, clean it up
+    if modified_graph:
+        gm = clean_up_graph_after_modifications(gm)
+        logger.debug(
+            f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}"
+        )
+
+    return gm
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
index 31a55099c2..1736a234a2 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
@@ -29,3 +29,42 @@ def get_tensor_placeholders(
     ]
 
     return placeholders
+
+
+def find_complex_nodes(gm: torch.fx.GraphModule):
+    complex_nodes = []
+    complexNodes = {}
+    for node in gm.graph.nodes:
+        if is_node_complex(node, complexNodes):
+            complex_nodes.append(node)
+    return complex_nodes
+
+
+def is_node_complex(node: torch.fx.Node, complexNodes):
+    if not isinstance(node, torch.fx.Node):
+        return False
+    if node.name in complexNodes:
+        return True
+    if node.op == "call_function" and node.args is not None:
+        for arg in node.args:
+            if isinstance(arg, int):
+                continue
+            elif isinstance(arg, (list, tuple)):
+                for eachNode in arg:
+                    if is_node_complex(eachNode, complexNodes):
+                        complexNodes[node.name] = True
+                        return True
+
+            elif hasattr(arg, "meta") and "val" in arg.meta:
+                if isinstance(arg.meta["val"], (list, tuple)):
+                    for eachFakeTensorMeta in arg.meta["val"]:
+                        if eachFakeTensorMeta.dtype in (
+                            torch.complex64,
+                            torch.complex128,
+                        ):
+                            complexNodes[node.name] = True
+                            return True
+                elif arg.meta["val"].dtype in (torch.complex64, torch.complex128):
+                    complexNodes[node.name] = True
+                    return True
+    return False
diff --git a/pyproject.toml b/pyproject.toml
index e2e451b430..0472b39f12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -85,9 +85,10 @@ dev = [
 torchvision = [
     "torchvision",
 ] #Leaving torchvisions dependency unconstrained so uv can just install something that should work for the torch we have. TV's on PyT makes it hard to put version constrains in
-quantization = ["nvidia-modelopt[deploy,hf,torch]~=0.17.0"]
+quantization = ["nvidia-modelopt[deploy,hf,torch]>=0.17.0"]
 monitoring-tools = ["rich>=13.7.1"]
 jupyter = ["rich[jupyter]>=13.7.1"]
+distributed = ["tensorrt-llm>=0.16.0"]
 
 [project.urls]
 Homepage = "https://pytorch.org/tensorrt"
diff --git a/uv.lock b/uv.lock
index 56c34c4c14..a8982d6b88 100644
--- a/uv.lock
+++ b/uv.lock
@@ -40,6 +40,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "anyio"
+version = "4.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "exceptiongroup", marker = "(python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'windows')" },
+    { name = "idna", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+    { name = "sniffio", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+    { name = "typing-extensions", marker = "(python_full_version < '3.13' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform == 'windows')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f6/40/318e58f669b1a9e00f5c4453910682e2d9dd594334539c7b7817dabb765f/anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48", size = 177076 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/7a/4daaf3b6c08ad7ceffea4634ec206faeff697526421c20f07628c7372156/anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352", size = 93052 },
+]
+
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -52,6 +67,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/45/86/4736ac618d82a20d87d2f92ae19441ebc7ac9e7a581d7e58bbe79233b24a/asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24", size = 27764 },
 ]
 
+[[package]]
+name = "async-timeout"
+version = "5.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a5/ae/136395dfbfe00dfc94da3f3e136d0b13f394cba8f4841120e34226265780/async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3", size = 9274 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fe/ba/e2081de779ca30d473f21f5b30e0e737c438205440784c7dfc81efc2b029/async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c", size = 6233 },
+]
+
+[[package]]
+name = "attrs"
+version = "24.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/48/c8/6260f8ccc11f0917360fc0da435c5c9c7504e3db174d5a12a1494887b045/attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff", size = 805984 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/89/aa/ab0f7891a01eeb2d2e338ae8fecbe57fcebea1a24dbb64d45801bfab481d/attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308", size = 63397 },
+]
+
 [[package]]
 name = "black"
 version = "24.8.0"
@@ -74,6 +107,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/27/1e/83fa8a787180e1632c3d831f7e58994d7aaf23a0961320d21e84f922f919/black-24.8.0-py3-none-any.whl", hash = "sha256:972085c618ee94f402da1af548a4f218c754ea7e5dc70acb168bfaca4c2542ed", size = 206504 },
 ]
 
+[[package]]
+name = "build"
+version = "1.2.2.post1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "(os_name == 'nt' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform == 'windows')" },
+    { name = "importlib-metadata", marker = "(python_full_version < '3.10.2' and sys_platform == 'linux') or (python_full_version < '3.10.2' and sys_platform == 'windows')" },
+    { name = "packaging", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+    { name = "pyproject-hooks", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+    { name = "tomli", marker = "(python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'windows')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/46/aeab111f8e06793e4f0e421fcad593d547fb8313b50990f31681ee2fb1ad/build-1.2.2.post1.tar.gz", hash = "sha256:b36993e92ca9375a219c99e606a122ff365a760a2d4bba0caa09bd5278b608b7", size = 46701 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/c2/80633736cd183ee4a62107413def345f7e6e3c01563dbca1417363cf957e/build-1.2.2.post1-py3-none-any.whl", hash = "sha256:1d61c0887fa860c01971625baae8bdd338e517b836a2f70dd1f7aa3a6b2fc5b5", size = 22950 },
+]
+
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -82,6 +131,55 @@ wheels = [
     { url = "https://download.pytorch.org/whl/test/certifi-2024.8.30-py3-none-any.whl" },
 ]
 
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/de/cc/4635c320081c78d6ffc2cab0a76025b691a91204f4aa317d568ff9280a2d/cffi-1.17.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382", size = 426024 },
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