Arm backend: Test fixes for TOSA on Arm64

Signed-off-by: Zingo Andersen <Zingo.Andersen@arm.com>
Change-Id: Ia8b796cb91c92ff45b36478d3b6904b25314f00c

Author: zingo

backends/arm/test/common.py

@@ -5,6 +5,7 @@
 
 
 import os
+import platform
 
 from datetime import datetime
 
@@ -27,6 +28,10 @@
 from executorch.backends.arm.vgf import VgfCompileSpec
 
 
+def is_aarch64_host() -> bool:
+    return platform.machine().lower() in ("aarch64", "arm64")
+
+
 def get_time_formatted_path(path: str, log_prefix: str) -> str:
     """Returns the log path with the current time appended to it. Used for
     debugging.

backends/arm/test/models/Qwen3_VL/test_qwen3_vl_layers.py

@@ -488,6 +488,16 @@ def test_qwen3_vl_tosa_FP(test_case: Qwen3VLTestCase):
 @common.parametrize(
     "test_case",
     TOSA_BF16_TEST_CASES,
+    xfails=(
+        {
+            "vision_patch_embed": (
+                "MLETORCH-2048: Large bf16 patch embedding mismatch on aarch64",
+                AssertionError,
+            ),
+        }
+        if common.is_aarch64_host()
+        else None
+    ),
 )
 def test_qwen3_vl_tosa_FP_bf16(test_case: Qwen3VLTestCase):
     model, inputs = test_case.model_cls.prepare_model_and_inputs()

backends/arm/test/models/test_mobilenet_v3_arm.py

@@ -45,6 +45,7 @@ def test_mv3_tosa_FP():
     pipeline.run()
 
 
+# Different atol for TOSA on ARM (MLETORCH-2048: Large bf16 patch embedding mismatch on aarch64)
 @pytest.mark.slow
 def test_mv3_tosa_FP_fp16():
     input_tensor_fp16 = torch.rand(
@@ -57,7 +58,7 @@ def test_mv3_tosa_FP_fp16():
         aten_op=[],
         exir_op=[],
         use_to_edge_transform_and_lower=True,
-        atol=6e-2,
+        atol=6.5e-2 if common.is_aarch64_host() else 6e-2,
     )
     pipeline.run()
 

backends/arm/test/models/test_resnet18.py

@@ -49,6 +49,12 @@ def test_resnet_18_tosa_FP():
     pipeline.run()
 
 
+@pytest.mark.xfail(
+    common.is_aarch64_host(),
+    reason="MLETORCH-2048: Large bf16 ResNet18 mismatch on aarch64",
+    raises=AssertionError,
+    strict=True,
+)
 def test_resnet_18_tosa_FP_bf16():
     bf16_model = resnet18(weights=ResNet18_Weights).eval()
     bf16_model = bf16_model.to(torch.bfloat16)