From 3c65a699ec2dbd1daf5db0463b7ee75933a667d2 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Sat, 4 Jan 2025 11:34:52 +0000 Subject: [PATCH] 2025-01-04 nightly release (c636b39b0d9ded2397819045a0f7db6b8a643fc9) --- 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 | 14 +- uv.lock | 1660 ++++++++++++++--- 24 files changed, 2235 insertions(+), 273 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 6cc3412849..999973f829 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" @@ -101,17 +102,12 @@ include-package-data = false [tool.uv] environments = ["sys_platform == 'linux'", "sys_platform == 'windows'"] - prerelease = "if-necessary-or-explicit" - +index-strategy = "unsafe-best-match" # Needed for TRT-LLM [tool.uv.sources] -torch = [ - { index = "pytorch-nightly-cu126"}, -] -torchvision = [ - { index = "pytorch-nightly-cu126"}, -] +torch = [{ index = "pytorch-nightly-cu126" }] +torchvision = [{ index = "pytorch-nightly-cu126" }] [[tool.uv.index]] name = "pytorch-nightly-cu126" diff --git a/uv.lock b/uv.lock index a5fb0fb364..cf0a651463 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,11 @@ version = 1 requires-python = 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