NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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Merged
roman-janik-nxp merged 1 commit into from
Oct 13, 2025
Merged

NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default #14535

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1 change: 1 addition & 0 deletions backends/nxp/backend/edge_program_converter.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down
5 changes: 5 additions & 0 deletions backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
"""
This converter is responsible for converting both edge operators:
- aten.clone.default
- dim_order_ops._clone_dim_order.default
"""

@staticmethod
def _is_supported_in_IR(
Expand Down
1 change: 1 addition & 0 deletions backends/nxp/neutron_partitioner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down
8 changes: 7 additions & 1 deletion backends/nxp/tests/executors.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
return any(node.target in ops for node in graph.nodes)
return graph_contains_any(
graph, condition=lambda n: hasattr(n, "target") and n.target in ops
)


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down
165 changes: 109 additions & 56 deletions backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
to_edge_program,
to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
convert_run_compare,
graph_contains_any,
graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
torch.manual_seed(23)
np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
def __init__(
self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
model = SingleConvBlockWithDropout(
conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
).eval()
class TestCloneConverter(unittest.TestCase):
__test__ = False # Prevent interfering with PyTest tests

converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
@classmethod
def setUpClass(cls):
torch.manual_seed(23)
np.random.seed(23)

quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
@staticmethod
def _node_is_clone(node) -> bool:
clone_ops = [
exir_ops.edge.aten.clone.default,
exir_ops.edge.dim_order_ops._clone_dim_order.default,
]

tflite_flatbuffers_model, io_formats = converter_spy.spy_return
exported_program: ExportedProgram = converter_spy.call_args.args[1]

assert not graph_contains_any_of_ops(
graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
)

input_data = (np.random.random(input_shape) * 50).astype(np.int8)
convert_run_compare(
exported_program,
tfl_model=tflite_flatbuffers_model,
tflite_input_preprocess=ToNHWCPreprocess(),
tflite_output_preprocess=ToNCHWPreprocess(),
input_data=input_data,
atol=1.0,
)
def target_can_be_clone(node):

@MartinPavellaMartinPavellaSep 24, 2025

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👍🏻

if hasattr(node, "op") and node.op == "call_function":
return "clone" in node.target.__name__

return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
model = KWSFinalBlock(input_shape).eval()
return node in clone_ops or target_can_be_clone(node)

converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

tflite_flatbuffers_model, io_formats = converter_spy.spy_return
exported_program: ExportedProgram = converter_spy.call_args.args[1]

assert not graph_contains_any_of_ops(
graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
@parameterized.expand(
list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
)

input_data = (np.random.random(input_shape) * 50).astype(np.int8)
convert_run_compare(
exported_program,
tfl_model=tflite_flatbuffers_model,
tflite_input_preprocess=ToNHWCPreprocess(),
input_data=input_data,
atol=1.0,
def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
model = SingleConvBlockWithDropout(
conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
).eval()

with kgb.spy_on(
EdgeProgramToIRConverter.convert_program, call_original=True
) as converter_spy:
quantized_program = to_quantized_edge_program(
model, input_shape
).exported_program()

tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

assert not graph_contains_any(
graph=quantized_program.graph,
condition=TestCloneConverter._node_is_clone,
)

input_data = (np.random.random(input_shape) * 50).astype(np.int8)
convert_run_compare(
exported_program,
tfl_model=tflite_flatbuffers_model,
tflite_input_preprocess=ToChannelLastPreprocess(),
tflite_output_preprocess=ToChannelFirstPreprocess(),
input_data=input_data,
atol=1.0,
)

@parameterized.expand(
list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
)
def test_conv_dropout_no_quant(
self, inplace_dropout: bool, input_shape: tuple[int]
):
model = SingleConvBlockWithDropout(
conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
).eval()

edge_program = to_edge_program(model, input_shape).exported_program()

has_clone = graph_contains_any_of_ops(
graph=edge_program.graph,
ops=[
exir_ops.edge.aten.clone.default,
exir_ops.edge.dim_order_ops._clone_dim_order.default,
],
)

# Clone with inplace=True should not produce clone edge op and vice versa
assert inplace_dropout ^ has_clone

def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
model = KWSFinalBlock(input_shape).eval()

with kgb.spy_on(
EdgeProgramToIRConverter.convert_program, call_original=True
) as converter_spy:
quantized_program = to_quantized_edge_program(
model, input_shape
).exported_program()

tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

assert not graph_contains_any(
graph=quantized_program.graph,
condition=TestCloneConverter._node_is_clone,
)

input_data = (np.random.random(input_shape) * 50).astype(np.int8)
convert_run_compare(
exported_program,
tfl_model=tflite_flatbuffers_model,
tflite_input_preprocess=ToChannelLastPreprocess(),
input_data=input_data,
atol=1.0,
)
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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to your account

 Merged

roman-janik-nxp
merged 1 commit into

from

Oct 13, 2025
 Merged


NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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 Merged

roman-janik-nxp
merged 1 commit into

from

Oct 13, 2025
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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

roman-janik-nxp
merged 1 commit into

from

Oct 13, 2025
 Merged


NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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 Merged

roman-janik-nxp
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Oct 13, 2025
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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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 Merged

roman-janik-nxp
merged 1 commit into

from

Oct 13, 2025
 Merged


NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

Copy link


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Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default#14535

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 Merged

roman-janik-nxp
merged 1 commit into

from

Oct 13, 2025
 Merged


NXP backend: Add conversion and quantization support for dim_order_ops._clone_dim_order.default
#14535
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1 change: 1 addition & 0 deletions
backends/nxp/backend/edge_program_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -34,6 +34,7 @@
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

5 changes: 5 additions & 0 deletions
backends/nxp/backend/ir/converter/node_converters/ops_converters/clone_converter.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,11 @@ def _has_supported_memory_format(node: Node) -> bool:


class CloneConverter(NodeConverter):
 """
 This converter is responsible for converting both edge operators:
 - aten.clone.default
 - dim_order_ops._clone_dim_order.default
 """

 @staticmethod
 def _is_supported_in_IR(
Expand Down

1 change: 1 addition & 0 deletions
backends/nxp/neutron_partitioner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -201,6 +201,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
 exir_ops.edge.aten.avg_pool2d.default: AvgPool2dConverter, # noqa F405
 exir_ops.edge.aten.cat.default: CatConverter, # noqa F405
 exir_ops.edge.aten.clone.default: CloneConverter, # noqa F405
 exir_ops.edge.dim_order_ops._clone_dim_order.default: CloneConverter, # noqa F405
 exir_ops.edge.aten.constant_pad_nd.default: ConstantPadNDConverter, # noqa F405
 exir_ops.edge.aten.convolution.default: ConvolutionConverter, # noqa F405
 exir_ops.edge.aten.hardtanh.default: HardTanhConverter, # noqa F405
Expand Down

8 changes: 7 additions & 1 deletion
backends/nxp/tests/executors.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -368,7 +368,13 @@ def convert_run_compare(


def graph_contains_any_of_ops(graph: Graph, ops: list) -> bool:
 return any(node.target in ops for node in graph.nodes)
 return graph_contains_any(
 graph, condition=lambda n: hasattr(n, "target") and n.target in ops
 )


def graph_contains_any(graph: Graph, condition: Callable[[Node], bool]) -> bool:
 return any(map(condition, graph.nodes))


target_support_check_function = Callable[[Node, NeutronTargetSpec], bool]
Expand Down

165 changes: 109 additions & 56 deletions
backends/nxp/tests/ir/converter/node_converter/test_clone_converter.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,31 +4,33 @@
# LICENSE file in the root directory of this source tree.


import itertools
import unittest

import kgb
import numpy as np
import pytest
import torch

from executorch.backends.nxp.backend.edge_program_converter import (
 EdgeProgramToIRConverter,
)
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
from executorch.backends.nxp.tests.executorch_pipeline import (
 to_edge_program,
 to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import (
 convert_run_compare,
 graph_contains_any,
 graph_contains_any_of_ops,
ToNCHWPreprocess,
ToNHWCPreprocess,
ToChannelFirstPreprocess,
ToChannelLastPreprocess,
)
from executorch.exir.dialects._ops import ops as exir_ops
from parameterized import parameterized
from torch import nn
from torch.export import ExportedProgram


@pytest.fixture(autouse=True)
def reseed_model_per_test_run():
 torch.manual_seed(23)
 np.random.seed(23)


class SingleConvBlockWithDropout(torch.nn.Module):
 def __init__(
 self, conv_in_channels: int = 3, perform_inplace_dropout: bool = False
Expand DownExpand Up@@ -74,57 +76,108 @@ def forward(self, x):
 return self.block(x)


@pytest.mark.parametrize("inplace_dropout", [False, True])
@pytest.mark.parametrize("input_shape", [(1, 3, 128, 128), (1, 3, 256, 256)])
def test_conv_dropout_quant(mocker, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()
class TestCloneConverter(unittest.TestCase):
 __test__ = False # Prevent interfering with PyTest tests

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
 @classmethod
 def setUpClass(cls):
 torch.manual_seed(23)
 np.random.seed(23)

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()
 @staticmethod
 def _node_is_clone(node) -> bool:
 clone_ops = [
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ]

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 tflite_output_preprocess=ToNCHWPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
 def target_can_be_clone(node):
@MartinPavellaMartinPavellaSep 24, 2025

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👍🏻
 if hasattr(node, "op") and node.op == "call_function":
 return "clone" in node.target.__name__

 return False

@pytest.mark.parametrize("inplace_dropout", [False, True])
def test_clone_pool_view_copy_quant(
 mocker, inplace_dropout: bool, input_shape: tuple[int] = (1, 64, 25, 5)
):
 model = KWSFinalBlock(input_shape).eval()
 return node in clone_ops or target_can_be_clone(node)

 converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")

 quantized_program = to_quantized_edge_program(model, input_shape).exported_program()

 tflite_flatbuffers_model, io_formats = converter_spy.spy_return
 exported_program: ExportedProgram = converter_spy.call_args.args[1]

 assert not graph_contains_any_of_ops(
 graph=quantized_program.graph, ops=[exir_ops.edge.aten.clone.default]
 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToNHWCPreprocess(),
 input_data=input_data,
 atol=1.0,
 def test_conv_dropout_quant(self, inplace_dropout: bool, input_shape: tuple[int]):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 tflite_output_preprocess=ToChannelFirstPreprocess(),
 input_data=input_data,
 atol=1.0,
 )

 @parameterized.expand(
 list(itertools.product([True, False], [(1, 3, 128, 128), (1, 3, 256, 256)]))
 )
 def test_conv_dropout_no_quant(
 self, inplace_dropout: bool, input_shape: tuple[int]
 ):
 model = SingleConvBlockWithDropout(
 conv_in_channels=input_shape[1], perform_inplace_dropout=inplace_dropout
 ).eval()

 edge_program = to_edge_program(model, input_shape).exported_program()

 has_clone = graph_contains_any_of_ops(
 graph=edge_program.graph,
 ops=[
 exir_ops.edge.aten.clone.default,
 exir_ops.edge.dim_order_ops._clone_dim_order.default,
 ],
 )

 # Clone with inplace=True should not produce clone edge op and vice versa
 assert inplace_dropout ^ has_clone

 def test_clone_pool_view_copy_quant(self, input_shape: tuple[int] = (1, 64, 25, 5)):
 model = KWSFinalBlock(input_shape).eval()

 with kgb.spy_on(
 EdgeProgramToIRConverter.convert_program, call_original=True
 ) as converter_spy:
 quantized_program = to_quantized_edge_program(
 model, input_shape
 ).exported_program()

 tflite_flatbuffers_model, _ = converter_spy.calls[-1].return_value
 exported_program: ExportedProgram = converter_spy.calls[-1].args[0]

 assert not graph_contains_any(
 graph=quantized_program.graph,
 condition=TestCloneConverter._node_is_clone,
 )

 input_data = (np.random.random(input_shape) * 50).astype(np.int8)
 convert_run_compare(
 exported_program,
 tfl_model=tflite_flatbuffers_model,
 tflite_input_preprocess=ToChannelLastPreprocess(),
 input_data=input_data,
 atol=1.0,
 )
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