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NXP Backend: Add infrastructure for pre processing passes in edge dialect #13183
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robert-kalmar
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nxp-upstream:upstream/main-nxp/EIEX-409-upstream-infrastructure-for-pre-processing-passes-in-edge-dialect
Aug 13, 2025
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219
backends/nxp/edge_passes/move_auxiliary_operator_into_separate_qdq_cluster_pass.py
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# Copyright 2025 NXP | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
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||
import torch | ||
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from executorch.backends.nxp.edge_passes.neutron_edge_pass import NeutronEdgePass | ||
from executorch.backends.nxp.neutron_partitioner import QDQClusterRecognizer | ||
from executorch.exir.dialects._ops import ops as exir_ops | ||
from torch.fx import Node | ||
from torch.fx.passes.infra.pass_base import PassResult | ||
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def insert_qdq_pair_after_node( | ||
graph: torch.fx.Graph, anchor: torch.fx.Node, q_params: tuple | ||
): | ||
# Insert a Quantize node. | ||
with graph.inserting_after(anchor): | ||
quantize_op = graph.create_node( | ||
op="call_function", | ||
target=exir_ops.edge.quantized_decomposed.quantize_per_tensor.default, | ||
args=(), # Will be added later. | ||
) | ||
quantize_op.meta = anchor.meta | ||
|
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# Insert a Dequantize node. | ||
with graph.inserting_after(quantize_op): | ||
dequantize_op = graph.create_node( | ||
op="call_function", | ||
target=exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default, | ||
args=(quantize_op,) + q_params, | ||
) | ||
dequantize_op.meta = quantize_op.meta | ||
anchor.replace_all_uses_with(dequantize_op) | ||
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# Add this at the end, so the `anchor.replace_all_uses_with(dequantize_op)` does not replace the first use of the | ||
# `quantize_op`. | ||
quantize_op.args = (anchor,) + q_params | ||
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def _is_dequantize(node_: Node) -> bool: | ||
return ( | ||
node_.op == "call_function" | ||
and node_.target | ||
== exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default | ||
) | ||
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||
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def _is_quantize(node_: Node) -> bool: | ||
return ( | ||
node_.op == "call_function" | ||
and node_.target | ||
== exir_ops.edge.quantized_decomposed.quantize_per_tensor.default | ||
) | ||
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||
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class MoveLeadingAuxiliaryOperatorIntoSeparateQDQClusterPass(NeutronEdgePass): | ||
""" | ||
│ | ||
┌─────▼──────┐ | ||
│ │ dequantize │ | ||
┌─────▼──────┐ └─────┬──────┘ | ||
│ dequantize │ ┌─────▼──────┐ | ||
└─────┬──────┘ │ <aux_node> │ | ||
┌─────▼──────┐ └─────┬──────┘ | ||
│ <aux_node> │ ┌────▼─────┐ ┐ | ||
└─────┬──────┘ │ quantize │ │ | ||
┌──────────▼──────────┐ replaced with └────┬─────┘ │ | ||
⋯┤ <main_cluster_node> ├⋯ ──────────────► │ │ newly added nodes | ||
└──────────┬──────────┘ ┌─────▼──────┐ │ | ||
▼ │ dequantize │ │ | ||
⋮ └─────┬──────┘ ┘ | ||
┌────▼─────┐ ┌──────────▼──────────┐ | ||
│ quantize │ ⋯┤ <main_cluster_node> ├⋯ | ||
└────┬─────┘ └──────────┬──────────┘ | ||
▼ ▼ | ||
⋮ | ||
┌────▼─────┐ | ||
│ quantize │ | ||
└────┬─────┘ | ||
▼ | ||
""" | ||
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allowed_auxiliary_nodes = [exir_ops.edge.aten.view_copy.default] | ||
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# List of approved nodes to which the <aux_node> can be connected in order for the pass to make the modification. | ||
allowed_main_cluster_nodes = [ | ||
exir_ops.edge.aten.addmm.default, | ||
exir_ops.edge.aten.mm.default, | ||
] | ||
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def run(self, graph_module: torch.fx.GraphModule) -> PassResult: | ||
for aux_node in graph_module.graph.nodes: | ||
if ( | ||
aux_node.op != "call_function" | ||
or aux_node.target not in self.allowed_auxiliary_nodes | ||
): | ||
continue | ||
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dequantize_node = aux_node.args[0] | ||
if not _is_dequantize(dequantize_node): | ||
# Not the intended use case. | ||
continue | ||
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users = list(aux_node.users.keys()) | ||
if len(users) != 1: | ||
# Not the intended use case. | ||
continue | ||
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main_cluster_node = users[0] | ||
if ( | ||
main_cluster_node.op != "call_function" | ||
or main_cluster_node.target not in self.allowed_main_cluster_nodes | ||
): | ||
# Unsupported `main_cluster_node`. | ||
continue | ||
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# Make sure the nodes are part of the same QDQ cluster. | ||
cluster = QDQClusterRecognizer().get_qdq_cluster(main_cluster_node) | ||
if any( | ||
node_ not in cluster | ||
for node_ in [dequantize_node, aux_node, main_cluster_node] | ||
): | ||
continue | ||
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# ---- The nodes follow the pattern described in the header. ---- | ||
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q_params = dequantize_node.args[1:] | ||
insert_qdq_pair_after_node(graph_module.graph, aux_node, q_params) | ||
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# The graph has now changed, and we shouldn't keep iterating through it. Return the new graph and the parent | ||
# class will call this pass again. | ||
return PassResult(graph_module, True) | ||
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# Nothing was changed. | ||
return PassResult(graph_module, False) | ||
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class MoveTrailingAuxiliaryOperatorIntoSeparateQDQClusterPass(NeutronEdgePass): | ||
""" | ||
│ | ||
┌─────▼──────┐ | ||
│ │ dequantize │ | ||
┌─────▼──────┐ └─────┬──────┘ | ||
│ dequantize │ ⋮ | ||
└─────┬──────┘ ┌──────────▼──────────┐ | ||
▼ ⋯┤ <main_cluster_node> ├⋯ | ||
⋮ └──────────┬──────────┘ | ||
┌──────────▼──────────┐ replaced with ┌────▼─────┐ ┐ | ||
⋯┤ <main_cluster_node> ├⋯ ──────────────► │ quantize │ │ | ||
└──────────┬──────────┘ └────┬─────┘ │ | ||
┌─────▼──────┐ │ │ newly added nodes | ||
│ <aux_node> │ ┌─────▼──────┐ │ | ||
└─────┬──────┘ │ dequantize │ │ | ||
┌────▼─────┐ └─────┬──────┘ ┘ | ||
│ quantize │ ┌─────▼──────┐ | ||
└────┬─────┘ │ <aux_node> │ | ||
▼ └─────┬──────┘ | ||
┌────▼─────┐ | ||
│ quantize │ | ||
└────┬─────┘ | ||
▼ | ||
""" | ||
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allowed_auxiliary_nodes = [exir_ops.edge.aten.view_copy.default] | ||
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# List of approved nodes to which the `<aux_node>` can be connected in order for the pass to make the modification. | ||
allowed_main_cluster_nodes = [ | ||
exir_ops.edge.aten.addmm.default, | ||
exir_ops.edge.aten.mm.default, | ||
] | ||
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def run(self, graph_module: torch.fx.GraphModule) -> PassResult: | ||
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for aux_node in graph_module.graph.nodes: | ||
if ( | ||
aux_node.op != "call_function" | ||
or aux_node.target not in self.allowed_auxiliary_nodes | ||
): | ||
continue | ||
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main_cluster_node = aux_node.args[0] | ||
if ( | ||
main_cluster_node.op != "call_function" | ||
or main_cluster_node.target not in self.allowed_main_cluster_nodes | ||
): | ||
# Unsupported `main_cluster_node`. | ||
continue | ||
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users = list(aux_node.users.keys()) | ||
if len(users) != 1: | ||
# Not the intended use case. | ||
continue | ||
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quantize_node = users[0] | ||
if not _is_quantize(quantize_node): | ||
# Not the intended use case. | ||
continue | ||
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# Make sure the nodes are part of the same QDQ cluster. | ||
cluster = QDQClusterRecognizer().get_qdq_cluster(main_cluster_node) | ||
if any( | ||
node_ not in cluster | ||
for node_ in [quantize_node, aux_node, main_cluster_node] | ||
): | ||
continue | ||
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# ---- The nodes follow the pattern described in the header. ---- | ||
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q_params = quantize_node.args[1:] | ||
insert_qdq_pair_after_node(graph_module.graph, main_cluster_node, q_params) | ||
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# The graph has now changed, and we shouldn't keep iterating through it. Return the new graph and the parent | ||
# class will call this pass again. | ||
return PassResult(graph_module, True) | ||
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# Nothing was changed. | ||
return PassResult(graph_module, False) |
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# Copyright 2025 NXP | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
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import logging | ||
from abc import abstractmethod | ||
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import torch | ||
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from executorch.exir.pass_base import ExportPass | ||
from torch.fx.passes.infra.pass_base import PassResult | ||
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class NeutronEdgePass(ExportPass): | ||
"""Abstract parent class for pre-processing passes on the edge dialect level.""" | ||
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def call(self, graph_module: torch.fx.GraphModule) -> PassResult: | ||
"""Call `self.run()` as long as changes are being made. After a pass modifies the graph, it cannot keep on | ||
iterating through its nodes, and must return. This method allows the pass to go through the whole model. | ||
""" | ||
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# Every pass will return once it makes a change to the graph, to avoid traversing and modifying a graph at the | ||
# same time. Therefore, it must be called multiple times (at most `iteration_limit` times). | ||
iteration_limit = len(graph_module.graph.nodes) | ||
modified = False | ||
for _ in range(iteration_limit): | ||
res = self.run(graph_module) | ||
if res.modified: | ||
modified = True | ||
graph_module = res.graph_module | ||
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else: | ||
# No more changes have been made. | ||
graph_module = self.recompile_module(graph_module) | ||
return PassResult(graph_module, modified) | ||
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# Iteration limit was reached. | ||
logging.warning( | ||
f"The NeutronEdgePass `{self.__class__.__name__}` reached the iteration limit." | ||
) | ||
graph_module = self.recompile_module(graph_module) | ||
return PassResult(graph_module, modified) | ||
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@abstractmethod | ||
def run(self, graph_module: torch.fx.GraphModule) -> PassResult: | ||
"""Child classes should implement their graph modification here.""" | ||
pass | ||
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def recompile_module( | ||
self, graph_module: torch.fx.GraphModule | ||
) -> torch.fx.GraphModule: | ||
"""Recompile the graph and re-trace the metadata. This should ensure that the datatypes and shapes are correct.""" | ||
graph_module.recompile() | ||
return super().call(graph_module).graph_module |
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@@ -0,0 +1,89 @@ | ||
# Copyright 2025 NXP | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
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import copy | ||
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from executorch.backends.nxp.edge_passes.move_auxiliary_operator_into_separate_qdq_cluster_pass import ( | ||
MoveLeadingAuxiliaryOperatorIntoSeparateQDQClusterPass, | ||
MoveTrailingAuxiliaryOperatorIntoSeparateQDQClusterPass, | ||
) | ||
from executorch.backends.nxp.edge_passes.neutron_edge_pass import NeutronEdgePass | ||
from executorch.exir import EdgeProgramManager | ||
from executorch.exir.program._program import ( | ||
_get_updated_graph_signature, | ||
_get_updated_range_constraints, | ||
) | ||
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from torch import nn | ||
from torch.export import ExportedProgram | ||
from torch.fx.passes.infra.pass_base import PassResult | ||
from torch.fx.passes.infra.pass_manager import PassManager | ||
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class NeutronEdgePassManager(PassManager): | ||
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def __init__(self, passes: list[NeutronEdgePass] = None): | ||
passes: list[NeutronEdgePass] = passes or [ | ||
MoveLeadingAuxiliaryOperatorIntoSeparateQDQClusterPass(), | ||
MoveTrailingAuxiliaryOperatorIntoSeparateQDQClusterPass(), | ||
] | ||
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super().__init__( | ||
passes, | ||
steps=10, # Empirical value. At most 10 cycles of passes will be run. | ||
) | ||
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def _transform_graph_module(self, module: nn.Module) -> PassResult: | ||
"""Apply the passes to a single graph module.""" | ||
pass_result: PassResult = super().__call__(module) | ||
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graph_module = pass_result.graph_module | ||
graph_module.graph.eliminate_dead_code() | ||
graph_module.recompile() | ||
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return pass_result | ||
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def __call__(self, epm: EdgeProgramManager) -> EdgeProgramManager: | ||
"""Apply the passes to all graph modules in the edge program.""" | ||
new_programs: dict[str, ExportedProgram] = {} | ||
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for name, program in epm._edge_programs.items(): | ||
pass_result = self._transform_graph_module(program.graph_module) | ||
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if pass_result.modified: | ||
# Create a new exported program. | ||
new_program = ExportedProgram( | ||
root=pass_result.graph_module, | ||
graph=pass_result.graph_module.graph, | ||
graph_signature=_get_updated_graph_signature( | ||
program.graph_signature, pass_result.graph_module | ||
), | ||
state_dict=program.state_dict, | ||
range_constraints=_get_updated_range_constraints( | ||
pass_result.graph_module | ||
), | ||
module_call_graph=copy.deepcopy(program._module_call_graph), | ||
example_inputs=program.example_inputs, | ||
constants=program.constants, | ||
verifiers=[program.verifier], | ||
) | ||
new_program.graph_module.meta.update(program.graph_module.meta) | ||
new_program.graph_module.meta.update(pass_result.graph_module.meta) | ||
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else: | ||
# Keep the old exported program. | ||
new_program = program | ||
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new_programs[name] = new_program | ||
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if len(new_programs) == 0: | ||
# No passes were run, return the old EdgeProgramManager. | ||
return epm | ||
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else: | ||
# Return a new EdgeProgramManager with the updated programs. | ||
return EdgeProgramManager( | ||
new_programs, copy.deepcopy(epm._config_methods), epm.compile_config | ||
) |
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@Pop-korn, in principal this does not differ from the initial draft. You return everytime, you make a modification and the caller
EdgePassManager
starts a new iteration. Can you do all the graph modification and return then?@digantdesai, @Pop-korn noticed in some of the passes the code iterates over a changing graph. E.g. https://github.com/pytorch/executorch/blob/main/backends/xnnpack/_passes/fuse_batch_norm.py#L41 the
graph.nodes
gets modified in-place in the https://github.com/pytorch/executorch/blob/main/backends/xnnpack/_passes/fuse_batch_norm.py#L208, everytime a match is found.@Pop-korn, can you please confirm? @digantdesai Is this legit and intended?
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Responding to the first paragraph:
We can safely to all the modifications at once, and then return. We would be modifying the list of nodes while iterating over it, but it shouldn't have negative side-effects as we are only inserting nodes. The downside is that is is not good practice. Do you think that implementation would be preferred?
The second paragraph:
As far as I can tell, the XNNPack batch_norm fusion pass does indeed modify the graph while iterating over it. When the pass removes a
batch_norm
node, the next iteration of thefor
loop will skip a node. If (somehow) the graph contained the sequenceconvolution
->batch_norm
->batch_norm
, only the firstbatch_norm
node would be fused, and the second one would be skipped.