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use dim order in arm test #7697

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use dim order in arm test #7697

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4 changes: 1 addition & 3 deletions backends/arm/test/models/test_mobilenet_v2_arm.py
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Expand Up @@ -47,9 +47,7 @@ class TestMobileNetV2(unittest.TestCase):
"executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
}

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

def test_mv2_tosa_MI(self):
(
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4 changes: 1 addition & 3 deletions backends/arm/test/ops/test_add.py
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Expand Up @@ -51,9 +51,7 @@ def __init__(self):
def forward(self, x, y):
return x + y

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

def _test_add_tosa_MI_pipeline(
self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
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4 changes: 1 addition & 3 deletions backends/arm/test/ops/test_linear.py
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Expand Up @@ -108,9 +108,7 @@
class TestLinear(unittest.TestCase):
"""tests the linear operation y = Ax + b"""

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

class Linear(torch.nn.Module):
def __init__(
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4 changes: 1 addition & 3 deletions backends/arm/test/ops/test_maximum.py
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Expand Up @@ -38,9 +38,7 @@ def __init__(self):
def forward(self, x, y):
return torch.maximum(x, y)

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

def _test_maximum_tosa_MI_pipeline(
self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
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4 changes: 1 addition & 3 deletions backends/arm/test/ops/test_minimum.py
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Expand Up @@ -38,9 +38,7 @@ def __init__(self):
def forward(self, x, y):
return torch.minimum(x, y)

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

def _test_minimum_tosa_MI_pipeline(
self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
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4 changes: 1 addition & 3 deletions backends/arm/test/ops/test_sum.py
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Expand Up @@ -47,9 +47,7 @@ class Sum(torch.nn.Module):
def forward(self, x: torch.Tensor, dim: int, keepdim: bool):
return x.sum(dim=dim, keepdim=keepdim)

_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
_skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend.
)
_edge_compile_config: EdgeCompileConfig = EdgeCompileConfig()

def _test_sum_tosa_MI_pipeline(
self, module: torch.nn.Module, test_data: tuple[exampledata_t]
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3 changes: 0 additions & 3 deletions backends/arm/test/tester/arm_tester.py
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Expand Up @@ -205,8 +205,6 @@ def to_edge(
if config is not None:
to_edge_stage.edge_compile_conf = config

# TODO(T182928844): Delegate dim order op to backend.
to_edge_stage.edge_compile_conf._skip_dim_order = True
return super().to_edge(to_edge_stage)

def partition(self, partition_stage: Optional[Partition] = None):
Expand All @@ -232,7 +230,6 @@ def to_edge_transform_and_lower(
to_edge_and_lower_stage.partitioners = partitioners
if edge_compile_config is not None:
to_edge_and_lower_stage.edge_compile_conf = edge_compile_config
to_edge_and_lower_stage.edge_compile_conf._skip_dim_order = True
return super().to_edge_transform_and_lower(to_edge_and_lower_stage)

def to_executorch(self, to_executorch_stage: Optional[ToExecutorch] | None = None):
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