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Merged
oscarandersson8218 merged 2 commits into from
Apr 16, 2026
Merged

Arm backend: Use TOSA-context when adding passes #18442

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936fc09
Arm backend: Use TOSA-context when adding passes
oscarandersson8218 Mar 19, 2026
6c74b96
Add per-test case timeouts to flow tests
oscarandersson8218 Apr 14, 2026
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161 changes: 83 additions & 78 deletions backends/arm/_passes/arm_pass_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -159,6 +159,7 @@
from executorch.exir import ExportedProgram
from executorch.exir.pass_base import ExportPass
from executorch.exir.pass_manager import PassManager
from torch._export.utils import _get_shape_env_from_gm
from torch.fx import GraphModule
from torch.fx.passes.infra.pass_base import PassResult
from torch.nn.modules import Module
Expand Down Expand Up @@ -335,10 +336,12 @@ def add_passes(self, passes: Sequence[ExportPass | None]):
if p is not None:
self.add_pass(p)

def _tosa_context(self, graph_module: GraphModule) -> TosaLoweringContext:
shape_env = _get_shape_env_from_gm(graph_module)
return TosaLoweringContext(self.tosa_spec, shape_env)

def _transform(self, graph_module: GraphModule):
shape_env = graph_module.shape_env
with TosaLoweringContext(self.tosa_spec, shape_env):
return self(graph_module).graph_module
return self(graph_module).graph_module

def add_pass(self, pipeline_pass):
if type(pipeline_pass) in self._skip_pass_types:
Expand Down Expand Up @@ -532,87 +535,89 @@ def transform_to_backend_pipeline(
f"No pass pipeline found for TOSA specification: {self.tosa_spec}"
)

return self._tosa_pipeline(exported_program, graph_module)
with self._tosa_context(graph_module):
return self._tosa_pipeline(exported_program, graph_module)

def transform_for_annotation_pipeline(self, graph_module: GraphModule):
# Preprocessing passes
self.add_pass(RemoveGraphAssertsPass(tfa_pass=True))
self.add_pass(ConstantFoldingPass())

# Transformation passes (pre scalar -> tensor)
self.add_passes(
[
DecomposeIndexCopyPass(tfa_pass=True),
DecomposeSelectScatterPass(tfa_pass=True),
DecomposeSliceScatterPass(tfa_pass=True),
ConvertInt64ConstOpsToInt32Pass(tfa_pass=True),
ConvertInt64OutputOpsToInt32Pass(tfa_pass=True),
InsertInt32CastsAfterInt64PlaceholdersPass(tfa_pass=True),
DecomposeEmbeddingPass(tfa_pass=True),
DecomposeScaledDotProductAttentionPass(tfa_pass=True),
DecomposeRoundPass(tfa_pass=True),
DecomposeLogitPass(tfa_pass=True),
PromoteBoolOperandsPass(tfa_pass=True),
DecomposeSignPass(tfa_pass=True),
DecomposeTrilPass(tfa_pass=True),
DecomposeAddmmPass(tfa_pass=True),
DecomposeRemainderPass(tfa_pass=True),
DecomposeFloorDividePass(tfa_pass=True),
DecomposeDivTensorModePass(tfa_pass=True),
DecomposeWhereScalarOtherPass(tfa_pass=True),
DecomposeEinsumPass(tfa_pass=True),
RewriteInplaceArithmeticPass(tfa_pass=True),
DecomposeAddSubAlphaPass(tfa_pass=True),
DecomposeLeakyReLUPass(tfa_pass=True),
DecomposeGroupNormPass(tfa_pass=True),
DecomposeLayerNormPass(tfa_pass=True),
DecomposeVarPass(tfa_pass=True),
DecomposeMeanDimPass(graph_module, self.tosa_spec, tfa_pass=True),
]
)
with self._tosa_context(graph_module):
# Preprocessing passes
self.add_pass(RemoveGraphAssertsPass(tfa_pass=True))
self.add_pass(ConstantFoldingPass())

# Transformation passes (pre scalar -> tensor)
self.add_passes(
[
DecomposeIndexCopyPass(tfa_pass=True),
DecomposeSelectScatterPass(tfa_pass=True),
DecomposeSliceScatterPass(tfa_pass=True),
ConvertInt64ConstOpsToInt32Pass(tfa_pass=True),
ConvertInt64OutputOpsToInt32Pass(tfa_pass=True),
InsertInt32CastsAfterInt64PlaceholdersPass(tfa_pass=True),
DecomposeEmbeddingPass(tfa_pass=True),
DecomposeScaledDotProductAttentionPass(tfa_pass=True),
DecomposeRoundPass(tfa_pass=True),
DecomposeLogitPass(tfa_pass=True),
PromoteBoolOperandsPass(tfa_pass=True),
DecomposeSignPass(tfa_pass=True),
DecomposeTrilPass(tfa_pass=True),
DecomposeAddmmPass(tfa_pass=True),
DecomposeRemainderPass(tfa_pass=True),
DecomposeFloorDividePass(tfa_pass=True),
DecomposeDivTensorModePass(tfa_pass=True),
DecomposeWhereScalarOtherPass(tfa_pass=True),
DecomposeEinsumPass(tfa_pass=True),
RewriteInplaceArithmeticPass(tfa_pass=True),
DecomposeAddSubAlphaPass(tfa_pass=True),
DecomposeLeakyReLUPass(tfa_pass=True),
DecomposeGroupNormPass(tfa_pass=True),
DecomposeLayerNormPass(tfa_pass=True),
DecomposeVarPass(tfa_pass=True),
DecomposeMeanDimPass(graph_module, self.tosa_spec, tfa_pass=True),
]
)

# Scalars -> tensors
self.add_passes(
[
ReplaceScalarWithTensorByProfilePass(tfa_pass=True),
ScalarsToAttributePass(tfa_pass=True),
ControlFlowConstInlinePass(tfa_pass=True),
]
)
# Scalars -> tensors
self.add_passes(
[
ReplaceScalarWithTensorByProfilePass(tfa_pass=True),
ScalarsToAttributePass(tfa_pass=True),
ControlFlowConstInlinePass(tfa_pass=True),
]
)

# Transformation passes (post scalar removal)
self.add_passes(
[
NormalizeWhileInitialArgsPass(use_exir_clone=False, tfa_pass=True),
DecomposeGruPass(tfa_pass=True),
DecomposeLstmPass(tfa_pass=True),
DecomposeRnnPass(tfa_pass=True),
DecomposeNotEqualPass(tfa_pass=True),
DecomposeCosineSimilarityPass(tfa_pass=True),
DecomposeGluPass(tfa_pass=True),
DecomposeDivPass(tfa_pass=True),
DecomposeLinalgVectorNormPass(tfa_pass=True),
DecomposeSqrtPass(tfa_pass=True),
DecomposeAdaptiveAvgPool2dPass(tfa_pass=True),
DecomposeAvgPool2dPass(tfa_pass=True),
DecomposeSoftmaxPass(
tfa_pass=True,
),
ConvertMinMaxPass(tfa_pass=True),
AccumulateIndexPutPass(tfa_pass=True),
DecomposeMatmulPass(tfa_pass=True),
]
)
# Transformation passes (post scalar removal)
self.add_passes(
[
NormalizeWhileInitialArgsPass(use_exir_clone=False, tfa_pass=True),
DecomposeGruPass(tfa_pass=True),
DecomposeLstmPass(tfa_pass=True),
DecomposeRnnPass(tfa_pass=True),
DecomposeNotEqualPass(tfa_pass=True),
DecomposeCosineSimilarityPass(tfa_pass=True),
DecomposeGluPass(tfa_pass=True),
DecomposeDivPass(tfa_pass=True),
DecomposeLinalgVectorNormPass(tfa_pass=True),
DecomposeSqrtPass(tfa_pass=True),
DecomposeAdaptiveAvgPool2dPass(tfa_pass=True),
DecomposeAvgPool2dPass(tfa_pass=True),
DecomposeSoftmaxPass(
tfa_pass=True,
),
ConvertMinMaxPass(tfa_pass=True),
AccumulateIndexPutPass(tfa_pass=True),
DecomposeMatmulPass(tfa_pass=True),
]
)

# Postprocessing passes
self.add_passes(
[
ReplaceInfAndLimitValuesPass(tfa_pass=True),
DecomposeMaskedFillPass(tfa_pass=True),
]
)
# Postprocessing passes
self.add_passes(
[
ReplaceInfAndLimitValuesPass(tfa_pass=True),
DecomposeMaskedFillPass(tfa_pass=True),
]
)

return self._transform(graph_module)
return self._transform(graph_module)

def __call__(self, module: Module) -> PassResult:
try:
Expand Down
35 changes: 25 additions & 10 deletions backends/test/suite/conftest.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,8 @@
# Copyright 2026 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import os
from typing import Any

Expand All @@ -9,19 +14,29 @@
from executorch.backends.test.suite.runner import run_test


FLOW_TEST_CASE_TIMEOUTS = {
"backends/test/suite/models/": 1200,
"backends/test/suite/operators/": 120,
}


def pytest_collection_modifyitems(config, items):
for item in items:
callspec = getattr(item, "callspec", None)
if callspec is None:
continue
flow = callspec.params.get("test_runner")
if not isinstance(flow, TestFlow):
continue
test_name = item.originalname or item.name
if flow.should_skip_test(test_name):
item.add_marker(
pytest.mark.skip(reason=f"Skipped by {flow.name} skip_patterns")
)
if callspec is not None:
flow = callspec.params.get("test_runner")
if isinstance(flow, TestFlow):
test_name = item.originalname or item.name
if flow.should_skip_test(test_name):
item.add_marker(
pytest.mark.skip(reason=f"Skipped by {flow.name} skip_patterns")
)

item_path = str(getattr(item, "path", ""))
for suite_prefix, timeout_s in FLOW_TEST_CASE_TIMEOUTS.items():
if suite_prefix in item_path:
item.add_marker(pytest.mark.timeout(timeout_s))
break


def pytest_configure(config):
Expand Down
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