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using nccl ops from TRT-LLM namespace
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‎examples/distributed_inference/README.md‎

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@@ -14,3 +14,7 @@ See the examples started with `data_parallel` for more details.
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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.
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torchrun --nproc_per_node=2 tensor_parallel_llama2.py
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3. Tensor parallel distributed inference using nccl ops plugin
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mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py

‎examples/distributed_inference/requirement.txt‎

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@@ -1,3 +1,6 @@
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accelerate
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transformers
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diffusers
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diffusers
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site
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# Install tensorrt-llm without its dependencies (use the command separately). pip install tensorrt-llm --no-deps
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tensorrt-llm

‎examples/distributed_inference/tensor_parallel_simple_example.py‎

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@@ -1,8 +1,17 @@
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import ctypes
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import logging
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import os
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import site
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import sys
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import time
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from enum import IntEnum, IntFlag, auto
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from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
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import numpy as np
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import tensorrt as trt
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import tensorrt_llm
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch_tensorrt
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from torch.distributed._tensor import Shard
@@ -12,6 +21,181 @@
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RowwiseParallel,
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parallelize_module,
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)
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from torch.fx import GraphModule, Node
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from torch.fx.node import Argument, Target
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from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
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from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
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dynamo_tensorrt_converter,
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)
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from torch_tensorrt.dynamo.lowering.passes.fuse_distributed_ops import (
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custom_fused_all_gather_op,
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custom_fused_reduce_scatter_op,
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)
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from torch_tensorrt.dynamo.types import TRTTensor
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from torch_tensorrt.fx.converters.converter_utils import set_layer_name
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# This is required for env initialization since we use mpirun
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def initialize(rank=0, world_size=1, port=29500):
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local_rank = int(
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os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
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)
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world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", world_size))
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# Set up environment variable to run with mpirun
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os.environ["RANK"] = str(local_rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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os.environ["MASTER_ADDR"] = "127.0.0.1"
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os.environ["MASTER_PORT"] = str(port)
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# Necessary to assign a device to each rank.
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torch.cuda.set_device(local_rank)
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# We use nccl backend
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dist.init_process_group("nccl")
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# set a manual seed for reproducibility
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torch.manual_seed(1111)
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return local_rank, world_size
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initialize()
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# create a device mesh based on the given world_size.
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_world_size = int(os.environ["WORLD_SIZE"])
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device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
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_rank = device_mesh.get_rank()
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device_id = _rank % torch.cuda.device_count() # Ensure each rank gets a unique device
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torch.cuda.set_device(device_id)
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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fh = logging.FileHandler(f"./tensor_parallel_simple_example_{_rank}.log", mode="w")
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fh.setLevel(logging.INFO)
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logger.addHandler(fh)
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# TensorRT NCCL plugins
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tensorrt_llm_lib_path = tensorrt_llm.__file__
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plugin_lib_path = tensorrt_llm_lib_path + "/libs/libnvinfer_plugin_tensorrt_llm.so"
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try:
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ctypes.CDLL(plugin_lib_path)
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logger.info(f"plugin loaded successfully")
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except OSError as e:
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logger.info(f"unsuccessful load : {e}")
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trt.init_libnvinfer_plugins(None, "")
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# Iterate over all registered plugin creators
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plugin_registry = trt.get_plugin_registry()
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for plugin_creator in plugin_registry.plugin_creator_list:
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logger.info(
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f"Plugin Name: {plugin_creator.name}, Namespace: {plugin_creator.plugin_namespace}, Version: {plugin_creator.plugin_version}"
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)
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# class for AllReduce
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class AllReduceStrategy(IntEnum):
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"""Warning: actual definition is in kernels/customAllReduceKernels.h.
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They must be kept in sync.
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"""
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NCCL = 0
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ONESHOT = 1
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TWOSHOT = 2
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AUTO = 3
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class AllReduceConfig(IntFlag):
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"""Warning: actual definition is in kernels/customAllReduceKernels.h.
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They must be kept in sync
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"""
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USE_MEMCPY = auto()
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PUSH_MODE = auto()
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@dynamo_tensorrt_converter(custom_fused_all_gather_op)
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def insert_nccl_gather_op(
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ctx: ConversionContext,
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target: Target,
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args: Tuple[Argument, ...],
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kwargs: Dict[str, Argument],
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name: str,
127+
) -> Union[TRTTensor, Sequence[TRTTensor]]:
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plug_inputs = [args[0]]
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allgather_plg_creator = trt.get_plugin_registry().get_plugin_creator(
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"AllGather", "1", "tensorrt_llm"
131+
)
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assert allgather_plg_creator is not None
133+
world_size = dist.get_world_size()
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group = list(range(world_size))
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group = trt.PluginField(
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"group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
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)
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p_dtype = trt.float16
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pf_type = trt.PluginField(
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"type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
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)
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pfc = trt.PluginFieldCollection([group, pf_type])
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allgather = allgather_plg_creator.create_plugin("allgather", pfc)
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layer = ctx.net.add_plugin_v2(plug_inputs, allgather)
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set_layer_name(layer, target, name)
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return layer.get_output(0)
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@dynamo_tensorrt_converter(custom_fused_reduce_scatter_op)
150+
def insert_nccl_reduce_scatter_plugin(
151+
ctx: ConversionContext,
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target: Target,
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args: Tuple[Argument, ...],
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kwargs: Dict[str, Argument],
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name: str,
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) -> Union[TRTTensor, Sequence[TRTTensor]]:
157+
plug_inputs = [args[0]]
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allreduce_plg_creator = trt.get_plugin_registry().get_plugin_creator(
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"ReduceScatter", "1", "tensorrt_llm"
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)
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assert allreduce_plg_creator is not None
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counter = 0
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strategy = AllReduceStrategy.NCCL
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config = AllReduceConfig(0)
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world_size = dist.get_world_size()
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group = list(range(world_size))
170+
group = trt.PluginField(
171+
"group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
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)
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p_dtype = trt.float16
175+
pf_dtype = trt.PluginField(
176+
"type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
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)
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pfc = [group, pf_dtype]
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p_strategy = trt.PluginField(
180+
"strategy", np.array([int(strategy)], np.int8), trt.PluginFieldType.INT8
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)
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pfc.append(p_strategy)
183+
p_config = trt.PluginField(
184+
"config", np.array([int(config)], np.int8), trt.PluginFieldType.INT8
185+
)
186+
pfc.append(p_config)
187+
p_counter = trt.PluginField(
188+
"counter", np.array([counter], np.int32), trt.PluginFieldType.INT32
189+
)
190+
pfc.append(p_counter)
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pfc = trt.PluginFieldCollection(pfc)
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ar_plug = allreduce_plg_creator.create_plugin("allreduce", pfc)
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layer = ctx.net.add_plugin_v2(plug_inputs, ar_plug)
196+
set_layer_name(layer, target, name)
197+
return layer.get_output(0)
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"""
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This example copies some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py
@@ -36,13 +220,6 @@ def forward(self, x):
36220
return x
37221

38222

39-
# create a device mesh based on the given world_size.
40-
_world_size = int(os.environ["WORLD_SIZE"])
41-
42-
device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
43-
_rank = device_mesh.get_rank()
44-
45-
46223
print(f"Starting PyTorch TP example on rank {_rank}.")
47224
assert (
48225
_world_size % 2 == 0

‎py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py‎

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@@ -5,6 +5,7 @@
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from .accumulate_fp32_matmul import accumulate_fp32_matmul
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from .constant_folding import constant_fold
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from .fuse_distributed_ops import fuse_distributed_ops
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from .fuse_prims_broadcast import fuse_prims_broadcast
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from .lower_linear import lower_linear
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from .lower_scaled_dot_product_attention import lower_scaled_dot_product_attention
@@ -25,6 +26,7 @@
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lower_scaled_dot_product_attention,
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lower_linear,
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fuse_prims_broadcast,
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fuse_distributed_ops,
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replace_max_pool_with_indices,
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replace_full_like_with_full,
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view_to_reshape,

‎py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py‎

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@@ -0,0 +1,71 @@
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import logging
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from typing import Sequence
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import torch
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# dead-code elimination, linting, and recompilation for graph, in-place
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from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
8+
clean_up_graph_after_modifications,
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)
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logger = logging.getLogger(__name__)
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def custom_fused_all_gather_op(args0, args1, args2):
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return torch.ops._c10d_functional.wait_tensor.default(
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torch.ops._c10d_functional.all_gather_into_tensor.default(args0, args1, args2)
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)
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def custom_fused_reduce_scatter_op(args0, args1, args2, args3):
21+
return torch.ops._c10d_functional.wait_tensor.default(
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torch.ops._c10d_functional.reduce_scatter_tensor.default(
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args0, args1, args2, args3
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)
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)
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def fuse_distributed_ops(
29+
gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]
30+
) -> torch.fx.GraphModule:
31+
modified_graph = False
32+
for node in gm.graph.nodes:
33+
if (
34+
node.target
35+
in (
36+
torch.ops._c10d_functional.all_gather_into_tensor.default,
37+
torch.ops._c10d_functional.reduce_scatter_tensor.default,
38+
)
39+
and len(node.users) == 1
40+
and list(node.users)[0].target
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== torch.ops._c10d_functional.wait_tensor.default
42+
):
43+
wait_tensor_node = list(node.users)[0]
44+
fused_op = None
45+
if node.target == torch.ops._c10d_functional.all_gather_into_tensor.default:
46+
fused_op = custom_fused_all_gather_op
47+
fused_op_args = (node.args[0], node.args[1], node.args[2])
48+
else:
49+
fused_op = custom_fused_reduce_scatter_op
50+
fused_op_args = (node.args[0], node.args[1], node.args[2], node.args[3])
51+
with gm.graph.inserting_after(wait_tensor_node):
52+
fused_node = gm.graph.create_node(
53+
op="call_function",
54+
target=fused_op, # Define your custom fused function
55+
args=fused_op_args,
56+
)
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wait_tensor_node.replace_all_uses_with(fused_node)
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fused_node.meta.update(node.meta)
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modified_graph = True
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gm.graph.erase_node(wait_tensor_node)
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gm.graph.erase_node(node)
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# If graph was modified, clean it up
65+
if modified_graph:
66+
gm = clean_up_graph_after_modifications(gm)
67+
logger.debug(
68+
f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}"
69+
)
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return gm

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