diff --git a/tests/unit/test_d1_3_layout_diag.py b/tests/unit/test_d1_3_layout_diag.py
index 76c148b7..0405d468 100644
--- a/tests/unit/test_d1_3_layout_diag.py
+++ b/tests/unit/test_d1_3_layout_diag.py
@@ -81,16 +81,19 @@ def main():
 
             # PV A-operand SMEM layout (sP)
             p_smem_s = utils.sm100.make_smem_layout_a(pv_mma, pv_mma_tiler, BFloat16, 1)
-            # p_smem_s.outer IS a layout, not a shape
-            sP_alloc = cute.make_tensor(cute.find(p_smem_s.outer, 0), p_smem_s.outer)
+            # p_smem_s.outer is the layout. Use it directly for shape queries.
+            # Can't actually allocate SMEM here (that's the SmemAllocator's job in the kernel)
+            # But we can create a tensor with a dummy pointer for shape analysis.
+            sP_alloc = cute.make_tensor(0, p_smem_s.outer)
             print(f"  sP shape: {cute.shape(sP_alloc)}")
             sP_2d = cute.group_modes(sP_alloc, 0, 3)
             print(f"  sP_2d shape: {cute.shape(sP_2d)}  rank={len(cute.shape(sP_2d))}")
             print(f"  sP_2d layout: {sP_2d.layout}")
+            print(f"  sP_2d layout: {sP_2d.layout}")
 
             # PV A-operand fragments
-            tP_tmem = cute.make_tensor(tStS.iterator, p_smem_s.outer)  # dummy tmem ptr
-            tP_smem = cute.make_tensor(cute.find(p_smem_s.outer, 0), p_smem_s.outer)
+            tP_tmem = cute.make_tensor(0, p_smem_s.outer)  # dummy ptr
+            tP_smem = cute.make_tensor(0, p_smem_s.outer)
             tOrP_tmem = pv_thr.make_fragment_A(tP_tmem)
             tCrP_smem = pv_mma.make_fragment_A(tP_smem)
             tOrP_smem = pv_thr.make_fragment_A(tP_smem)