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feat: Add ATen lowering pass system
- Add documentation, testing, and lowering pass management systems for ATen lowering passes
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‎docsrc/index.rst‎

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@@ -73,6 +73,7 @@ Tutorials
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tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
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tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
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tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
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tutorials/_rendered_examples/dynamo/dynamo_aten_lowering_passes
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Python API Documenation
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------------------------

‎examples/dynamo/README.rst‎

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@@ -9,3 +9,4 @@ a number of ways you can leverage this backend to accelerate inference.
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* :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
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* :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
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* :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
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:ref:`dynamo_aten_lowering_passes`: Custom modifications of a graph of ATen operators via lowering passes

‎examples/dynamo/dynamo_aten_lowering_passes.py‎

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"""
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.. _dynamo_aten_lowering_passes:
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Dynamo ATen Lowering Passes
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======================================================
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This interactive script is intended as an overview of the process by which ATen lowering passes are written and used."""
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# %%
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# 1. Lowering Pass Function
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# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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#
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# An ATen lowering pass function in Torch-TRT must satisfy two requirements:
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# - The function must take as input a single `torch.fx.GraphModule` and return the lowered
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# `torch.fx.GraphModule`
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# - The function must leave the graph in a valid and invoke-able state, including performing any
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# necessary linting and recompilation
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#
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# See below for an example of a lowering pass which repairs graphs that have inputs which are
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# also outputs, a disallowed configuration for TRT Engines.
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# %%
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import logging
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import torch
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logger = logging.getLogger(__name__)
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def repair_input_as_output(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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"""Repair scenarios where inputs are also outputs of the graph
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TRT does not allow such cases, so we insert a clone (identity) layer
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"""
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modified_graph = False
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# Extract graph placeholder Tensors
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placeholders = [
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node
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for node in gm.graph.nodes
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if (
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node.op == "placeholder"
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and isinstance(node.type, type)
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and issubclass(node.type, torch.Tensor)
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)
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]
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for placeholder in placeholders:
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# If any placeholder has any users which are direct graph outputs
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if len(placeholder.users) >= 1 and any(
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user.op == "output" for user in placeholder.users
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):
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modified_graph = True
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# Get direct graph outputs which are direct uses of placeholders
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direct_outputs = [user for user in placeholder.users if user.op == "output"]
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# Insert clone node for placeholder to ensure placeholder is not a direct output
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with gm.graph.inserting_after(placeholder):
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cloned_placeholder = gm.graph.call_function(
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torch.ops.aten.clone.default,
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args=(placeholder,),
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)
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# Replace placeholder as output with cloned version
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for output in direct_outputs:
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output.replace_input_with(placeholder, cloned_placeholder)
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# If the graph was modified, clean up the graph and ensure it is up-to-date
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if modified_graph:
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gm.graph.eliminate_dead_code()
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gm.graph.lint()
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gm.recompile()
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logger.debug(f"Graph after repair_input_as_output:\n{gm.graph}")
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return gm
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# %%
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# 2. Lowering Pass Registration
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# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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#
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# To add a lowering pass, use the convenience function `add_lowering_pass` in the module
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# `torch_tensorrt.dynamo.lowering.passes`. See below for an example:
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# %%
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from torch_tensorrt.dynamo.lowering.passes import add_lowering_pass
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add_lowering_pass(repair_input_as_output)
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# %%
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# 3. Apply Available Lowering Passes
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# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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#
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# To apply all lowering passes to a graph, the convenience function `apply_lowering_passes` in the module
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# `torch_tensorrt.dynamo.lowering.passes` can be used. This function is automatically invoked in the Torch-TRT Dynamo
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# paths. Additionally, the graph after each modifying pass is logged in the debug logs for Torch-TRT runs.

‎py/torch_tensorrt/dynamo/backend/backends.py‎

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@@ -7,27 +7,15 @@
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import torch
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import torch._dynamo as td
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import torch.utils._pytree as pytree
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import torch_tensorrt
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from torch._dynamo.utils import detect_fake_mode
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from torch._functorch.aot_autograd import _aot_export_function
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from torch._ops import OpOverload
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from torch_tensorrt.dynamo import CompilationSettings
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from torch_tensorrt.dynamo.compile import compile_module
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from torch_tensorrt.dynamo.lowering._decompositions import get_decompositions
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from torch_tensorrt.dynamo.lowering import apply_lowering_passes, get_decompositions
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from torch_tensorrt.dynamo.lowering._pre_aot_lowering import pre_aot_substitutions
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from torch_tensorrt.dynamo.utils import parse_dynamo_kwargs
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from packaging import version
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# Modify import location of utilities based on Torch version
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if version.parse(torch_tensorrt.sanitized_torch_version()) < version.parse("2.1.1"):
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from torch._inductor.freezing import ConstantFolder, replace_node_with_constant
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else:
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from torch._inductor.constant_folding import (
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ConstantFolder,
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replace_node_with_constant,
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)
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logger = logging.getLogger(__name__)
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fake_mode, "allow_non_fake_inputs", True
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), fake_mode:
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# Invoke AOTAutograd to translate operators to aten
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graph_module = aot_export_for_compile(
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gm = aot_export_for_compile(
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gm,
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sample_inputs,
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decompositions=get_decompositions(
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logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph))
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constant_fold(graph_module)
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gm = apply_lowering_passes(gm)
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trt_compiled = compile_module(
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graph_module,
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gm,
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sample_inputs,
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settings=settings,
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)
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raise
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@torch.utils._python_dispatch._disable_current_modes() # type: ignore
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def constant_fold(gm: torch.fx.GraphModule) -> Any:
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"""Adapted from:
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https://github.com/pytorch/pytorch/blob/3a79621c9dce17f77fbddc06aab21f6bc477f313/torch/_inductor/freezing.py#L178-L197
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Folds constants in the graph module, not skipping constructors
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Modifies the graph in-place and replaces node with constants
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"""
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cf = ConstantFolder(gm, skip_constructors=False)
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cf.run()
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for node, constant in cf.node_replacements.items():
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replace_node_with_constant(gm, node, constant)
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erased_params = []
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for node in gm.graph.nodes:
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if node.op == "get_attr" and len(node.users) == 0:
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delattr(gm, node.target)
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erased_params.append(node)
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for node in erased_params:
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gm.graph.erase_node(node)
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gm.graph.eliminate_dead_code()
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gm.graph.lint()
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gm.recompile()
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def aot_export_for_compile(
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func: torch.fx.GraphModule,
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args: Sequence[torch.Tensor],

‎py/torch_tensorrt/dynamo/lowering/__init__.py‎

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from ._fusers import * # noqa: F401
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from ._pre_aot_lowering import SUBSTITUTION_REGISTRY # noqa: F401
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from ._pre_aot_lowering import register_substitution # noqa: F401
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from .passes import add_lowering_pass, apply_lowering_passes
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from .substitutions import * # noqa: F401

‎py/torch_tensorrt/dynamo/lowering/passes/__init__.py‎

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from typing import Callable
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import torch
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from torch.fx.passes.pass_manager import PassManager
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from .constant_folding import constant_fold
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from .repair_input_as_output import repair_input_as_output
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ATEN_LOWERING_PASSES = PassManager.build_from_passlist(
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[
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constant_fold,
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repair_input_as_output,
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]
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)
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def add_lowering_pass(
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lowering_pass: Callable[[torch.fx.GraphModule], torch.fx.GraphModule]
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) -> None:
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"""Adds a lowering pass to the registry"""
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ATEN_LOWERING_PASSES.add_pass(lowering_pass)
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return
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def apply_lowering_passes(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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"""Applies the lowering passes to a graph module, returns the modified GraphModule"""
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return ATEN_LOWERING_PASSES(gm)

‎py/torch_tensorrt/dynamo/lowering/passes/constant_folding.py‎

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import logging
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import torch
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import torch_tensorrt
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from packaging import version
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# Modify import location of utilities based on Torch version
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if version.parse(torch_tensorrt.sanitized_torch_version()) < version.parse("2.1.1"):
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from torch._inductor.freezing import ConstantFolder, replace_node_with_constant
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else:
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from torch._inductor.constant_folding import (
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ConstantFolder,
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replace_node_with_constant,
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)
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logger = logging.getLogger(__name__)
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@torch.utils._python_dispatch._disable_current_modes() # type: ignore
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def constant_fold(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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"""Adapted from:
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https://github.com/pytorch/pytorch/blob/3a79621c9dce17f77fbddc06aab21f6bc477f313/torch/_inductor/freezing.py#L178-L197
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Folds constants in the graph module, not skipping constructors
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Modifies the graph in-place and replaces node with constants
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"""
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cf = ConstantFolder(gm, skip_constructors=False)
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cf.run()
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for node, constant in cf.node_replacements.items():
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replace_node_with_constant(gm, node, constant)
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erased_params = []
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for node in gm.graph.nodes:
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# If get_attr node has no users, mark it for deletion
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if node.op == "get_attr" and len(node.users) == 0:
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# If the node's parameter is not a parameter of any other node, remove it
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if not any(
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other.target == node.target for other in gm.graph.nodes if other != node
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):
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delattr(gm, node.target)
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erased_params.append(node)
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# Remove unused nodes from the graph
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for node in erased_params:
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gm.graph.erase_node(node)
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gm.graph.eliminate_dead_code()
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gm.graph.lint()
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gm.recompile()
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logger.debug(f"Graph after constant folding:\n{gm.graph}")
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return gm

‎py/torch_tensorrt/dynamo/lowering/passes/repair_input_as_output.py‎

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import logging
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import torch
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logger = logging.getLogger(__name__)
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def repair_input_as_output(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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"""Repair scenarios where inputs are also outputs of the graph
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TRT does not allow such cases, so we insert a clone (identity) layer
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"""
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modified_graph = False
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# Extract graph placeholder Tensors
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placeholders = [
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node
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for node in gm.graph.nodes
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if (
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node.op == "placeholder"
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and isinstance(node.type, type)
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and issubclass(node.type, torch.Tensor)
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)
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]
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for placeholder in placeholders:
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# If any placeholder has any users which are direct graph outputs
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if len(placeholder.users) >= 1 and any(
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user.op == "output" for user in placeholder.users
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):
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modified_graph = True
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# Get direct graph outputs which are direct uses of placeholders
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direct_outputs = [user for user in placeholder.users if user.op == "output"]
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# Insert clone node for placeholder to ensure placeholder is not a direct output
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with gm.graph.inserting_after(placeholder):
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cloned_placeholder = gm.graph.call_function(
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torch.ops.aten.clone.default,
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args=(placeholder,),
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)
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# Replace placeholder as output with cloned version
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for output in direct_outputs:
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output.replace_input_with(placeholder, cloned_placeholder)
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if modified_graph:
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gm.graph.eliminate_dead_code()
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gm.graph.lint()
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gm.recompile()
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logger.debug(f"Graph after repair_input_as_output:\n{gm.graph}")
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return gm

‎setup.py‎

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"torch_tensorrt.dynamo.conversion.impl.unary",
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"torch_tensorrt.dynamo.lowering",
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"torch_tensorrt.dynamo.lowering.substitutions",
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"torch_tensorrt.dynamo.lowering.passes",
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"torch_tensorrt.dynamo.partitioning",
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"torch_tensorrt.dynamo.runtime",
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"torch_tensorrt.dynamo.tools",
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"torch_tensorrt.dynamo.conversion.impl.unary": "py/torch_tensorrt/dynamo/conversion/impl/unary",
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"torch_tensorrt.dynamo.lowering": "py/torch_tensorrt/dynamo/lowering",
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"torch_tensorrt.dynamo.lowering.substitutions": "py/torch_tensorrt/dynamo/lowering/substitutions",
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"torch_tensorrt.dynamo.lowering.passes": "py/torch_tensorrt/dynamo/lowering/passes",
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"torch_tensorrt.dynamo.partitioning": "py/torch_tensorrt/dynamo/partitioning",
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"torch_tensorrt.dynamo.runtime": "py/torch_tensorrt/dynamo/runtime",
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"torch_tensorrt.dynamo.tools": "py/torch_tensorrt/dynamo/tools",

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