debug: add weight_scale_2 shape/value logging in weight transform

src/nvfp4_megamoe_kernel/weight_transform.py

@@ -75,6 +75,23 @@ def transform_nvfp4_weights_for_mega_moe(
     l1_weight, l1_weight_scale = l1_tuple
     l2_weight, l2_weight_scale = l2_tuple
 
+    # Debug: verify weight_scale_2 shape and values
+    if l1_weight_scale_2 is not None:
+        l1_gs = l1_weight_scale_2
+        print(f"[weight_transform] L1 weight_scale_2: shape={l1_gs.shape} dtype={l1_gs.dtype} "
+              f"min={l1_gs.min().item():.6f} max={l1_gs.max().item():.6f} "
+              f"numel={l1_gs.numel()}")
+        # Check if all experts have the same global scale (expected for ModelOpt NVFP4)
+        if l1_gs.numel() > 1:
+            unique = l1_gs.flatten().unique()
+            print(f"[weight_transform] L1 weight_scale_2 unique values: {len(unique)} "
+                  f"samples: {l1_gs.flatten()[:8].tolist()}")
+    if l2_weight_scale_2 is not None:
+        l2_gs = l2_weight_scale_2
+        print(f"[weight_transform] L2 weight_scale_2: shape={l2_gs.shape} dtype={l2_gs.dtype} "
+              f"min={l2_gs.min().item():.6f} max={l2_gs.max().item():.6f} "
+              f"numel={l2_gs.numel()}")
+
     # Fold global scales into block scales
     # Both L1 and L2 use per-expert global scales (shape (E,1) or (E,)).
     # The logical_widths branch was wrong: it treated gs as per-projection