Eliminate duplicated softmax recomputation in indexer loss.

By inserting `jax.lax.optimization_barrier` after the head aggregation step
in `calculate_indexer_loss`, we force the compiler to reuse the head-aggregated
intermediate tensor for the subsequent sequence reduction
instead of recomputing the entire softmax pipeline from raw QK scores.

TAG=agy
CONV=5ff94b54-4171-4309-8704-6046df05eb13

Author: zcjhao

src/maxtext/layers/attention_mla.py

@@ -1077,6 +1077,8 @@ def calculate_indexer_loss(
 
     # Aggregate heads: [b, h, t, s] -> [b, t, s]
     attention_probs = jnp.sum(attention_probs, axis=1)
+    # Force materialization and prevent fusion across this point to reuse the intermediate tensor
+    attention_probs = jax.lax.optimization_barrier(attention_probs)
     # L1 normalize aggregated target distribution
     attention_probs = attention_probs / (jnp.sum(attention_probs, axis=-1, keepdims=True) + EPS)