diff --git a/torchtitan/utils.py b/torchtitan/utils.py
index 8a065153..88663c00 100644
--- a/torchtitan/utils.py
+++ b/torchtitan/utils.py
@@ -384,16 +384,18 @@ def clip_grad_norm_(
         grads, norm_type, error_if_nonfinite, foreach
     )
 
-    if pp_mesh is not None:
-        if isinstance(total_norm, DTensor):
-            # will reach here if PP + other parallelism is used. If only using PP, total_norm will be a local tensor
-
-            # if total_norm is a DTensor, the placements must be `torch.distributed._tensor.ops.math_ops._NormPartial`
-            # we can simply reduce the DTensor to get the total norm in this tensor's process group
-            # and then convert it to a local tensor
-            total_norm = total_norm.full_tensor()
+    # If total_norm is a DTensor, the placements must be `torch.distributed._tensor.ops.math_ops._NormPartial`.
+    # We can simply reduce the DTensor to get the total norm in this tensor's process group
+    # and then convert it to a local tensor.
+    # NOTE: It has two purposes:
+    #       1. to make sure the total norm is computed correctly when PP is used (see below)
+    #       2. to return a reduced total_norm tensor whose .item() would return the correct value
+    if isinstance(total_norm, DTensor):
+        # Will reach here if any non-PP parallelism is used.
+        # If only using PP, total_norm will be a local tensor.
+        total_norm = total_norm.full_tensor()
 
-        # TODO: cleanup maybe using DTensor
+    if pp_mesh is not None:
         if math.isinf(norm_type):
             dist.all_reduce(total_norm, op=dist.ReduceOp.MAX, group=pp_mesh.get_group())
         else: