using nccl ops from TRT-LLM namespace (#3250)

Signed-off-by: Naren Dasan <[email protected]>
Co-authored-by: Naren Dasan <[email protected]>

Author: apbose

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

examples/distributed_inference/requirement.txt

@@ -1,3 +1,4 @@
 accelerate
 transformers
-diffusers
+diffusers
+tensorrt-llm

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

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,
     )

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}")

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:

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 = (

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(
@@ -89,22 +110,39 @@ 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))
 
             gm = post_lowering(gm, settings)
 
             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
             )

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