Arm backend: Add test for DeiT Tiny for TOSA BI

Add test case for the DeiT Tiny model for the TOSA BI profile. At this
time the output of the model differs from the reference implementation
by a mean absolute error of around 2.5, which is too high. An internal
ticket has been raised to resolve this issue.

Signed-off-by: Martin Lindström <[email protected]>
Change-Id: I9f3223068d35fa6f3e485e0fb12ad49bb8b3d534

Author: Martin Lindström

backends/arm/test/models/test_deit_tiny_arm.py

@@ -11,7 +11,10 @@
 
 import torch
 
-from executorch.backends.arm.test.tester.test_pipeline import TosaPipelineMI
+from executorch.backends.arm.test.tester.test_pipeline import (
+    TosaPipelineBI,
+    TosaPipelineMI,
+)
 
 from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 from torchvision import transforms
@@ -42,3 +45,16 @@ def test_deit_tiny_tosa_MI():
         qtol=1,
     )
     pipeline.run()
+
+
+def test_deit_tiny_tosa_BI():
+    pipeline = TosaPipelineBI[input_t](
+        deit_tiny,
+        model_inputs,
+        aten_op=[],
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+        atol=3.0,  # This needs to go down: MLETORCH-956
+        qtol=1,
+    )
+    pipeline.run()