diff --git a/torchtitan/distributed/utils.py b/torchtitan/distributed/utils.py
index 838cfda9c..c80dd3bdb 100644
--- a/torchtitan/distributed/utils.py
+++ b/torchtitan/distributed/utils.py
@@ -434,9 +434,7 @@ def _clip_grad_norm_with_ep(
     if math.isinf(norm_type):
         total_norm = torch.maximum(ep_grads_total_norm, non_ep_grads_total_norm)
     else:
-        total_norm = (
-            ep_grads_total_norm**norm_type + non_ep_grads_total_norm**norm_type
-        )
+        total_norm = ep_grads_total_norm**norm_type + non_ep_grads_total_norm**norm_type
         total_norm **= 1.0 / norm_type
 
     if pp_mesh is not None:
@@ -462,9 +460,23 @@ def cp_shard(
     order_sensitive_buffers_seq_dims: dict[str, int],
 ):
     from torch.distributed.tensor.experimental._attention import _context_parallel_shard
+    from torch.distributed.tensor.experimental._load_balancer import (
+        _HeadTailLoadBalancer,
+        _PTRRLoadBalancer,
+    )
     from torch.nn.attention.flex_attention import BlockMask
 
     load_balancer = None
+    """
+    seq_length = inputs.shape[1]
+    load_balancer = _HeadTailLoadBalancer(
+        seq_length, cp_mesh.size(0), cp_mesh.device_type
+    )
+
+    assert isinstance(attention_masks, BlockMask)
+    load_balancer = _PTRRLoadBalancer(attention_masks, cp_mesh.size(0))
+    """
+
     inputs, labels = _context_parallel_shard(
         mesh=cp_mesh,
         buffers=(inputs, labels),