use dim order in arm test

Summary: as title

Differential Revision: D68177095

Author: Gasoonjia

backends/arm/test/models/test_mobilenet_v2_arm.py

@@ -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):
         (

backends/arm/test/ops/test_add.py

@@ -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]

backends/arm/test/ops/test_linear.py

@@ -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__(

backends/arm/test/ops/test_maximum.py

@@ -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]

backends/arm/test/ops/test_minimum.py

@@ -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]

backends/arm/test/ops/test_sum.py

@@ -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]

backends/arm/test/tester/arm_tester.py

@@ -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):
@@ -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):