From 41f9e7a8eaa5d434c34eb1e796a435cb2cd1b283 Mon Sep 17 00:00:00 2001
From: Sangwon Ha <sangwon.ha@arm.com>
Date: Tue, 15 Sep 2026 13:30:11 +0100
Subject: [PATCH] Arm backend: Narrow VGF stepped-slice xfail

Limit the MLCE-1969 xfail to Linux with emulation layer 0.10.0
using the Interval planner. Allow macOS and BestFit/Linear runs
to report PASSED while retaining coverage for the reproduced bug.

Authored with assistance from OpenAI Codex.

Signed-off-by: Sangwon Ha <sangwon.ha@arm.com>
Change-Id: Id048a104c3f5e3dd9def16644aae6759e5cc1d59
---
 backends/arm/test/ops/test_slice.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/backends/arm/test/ops/test_slice.py b/backends/arm/test/ops/test_slice.py
index fde8d2fa1b6..5746edc24db 100644
--- a/backends/arm/test/ops/test_slice.py
+++ b/backends/arm/test/ops/test_slice.py
@@ -5,6 +5,8 @@
 # LICENSE file in the root directory of this source tree.
 
 
+import os
+import sys
 from typing import Tuple
 
 import torch
@@ -386,15 +388,22 @@ def test_slice_tensor_u85_INT_step(test_data: Tuple):
 @common.parametrize(
     "test_data",
     test_data_step_int | test_data_step_fp,
-    xfails={
-        "arange_fp32_2d_step4": common.xfail_if_model_converter_version(
-            ">=0.10.0",
-            reason=(
-                "MLCE-1969: Emlayer 0.10 Interval memory planner corrupts "
-                "multi-input CONCAT output"
+    xfails=(
+        {
+            "arange_fp32_2d_step4": common.xfail_if_dependency_version(
+                "ai-ml-emulation-layer-for-vulkan",
+                "==0.10.0",
+                reason=(
+                    "MLCE-1969: Emlayer 0.10.0 Interval memory planner corrupts "
+                    "multi-input CONCAT output"
+                ),
+                raises=AssertionError,
             ),
-        ),
-    },
+        }
+        if sys.platform == "linux"
+        and os.environ.get("VMEL_MEMORY_PLANNER") not in ("BestFit", "Linear")
+        else {}
+    ),
 )
 @common.SkipIfNoModelConverter
 def test_slice_tensor_vgf_no_quant_step(test_data: Tuple):