Fix 50% comparison mismatch in sort_chunks_by_index (#2566)

* force initialization to int32
Signed-off-by: tdophung <tdophung@nvidia.com>

* address greptile comment
Signed-off-by: tdophung <tdophung@nvidia.com>

---------
Signed-off-by: tdophung <tdophung@nvidia.com>

tests/jax/test_permutation.py

@@ -97,7 +97,9 @@ def reference_make_row_id_map(
 
     # Compute total tokens per expert and expert offsets
     tokens_per_expert = jnp.sum(routing_map, axis=0)
-    expert_offsets = jnp.concatenate([jnp.array([0]), jnp.cumsum(tokens_per_expert)[:-1]])
+    expert_offsets = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(tokens_per_expert)[:-1].astype(jnp.int32)]
+    )
 
     # Compute destination rows for all (token, expert) pairs
     # dest_row[i, j] = expert_offsets[j] + cumsum_per_expert[i, j] - 1 if routed, else -1
@@ -115,7 +117,9 @@ def reference_make_row_id_map(
 
     # Gather the sorted destination rows and expert indices using advanced indexing
     # Create indices for gathering
-    token_idx = jnp.broadcast_to(jnp.arange(num_tokens)[:, None], (num_tokens, num_experts))
+    token_idx = jnp.broadcast_to(
+        jnp.arange(num_tokens, dtype=jnp.int32)[:, None], (num_tokens, num_experts)
+    )
     sorted_dest_rows = dest_rows_all[token_idx, sorted_expert_indices]
 
     # Build row_id_map: [dest_row_0, ..., dest_row_{E-1}, expert_idx_0, ..., expert_idx_{E-1}, n_routed]
@@ -373,11 +377,15 @@ def reference_make_chunk_sort_map(
         Row ID map for chunk sorting of shape [num_tokens,].
     """
     # Compute source chunk boundaries (cumulative sum of original split_sizes)
-    src_cumsum = jnp.concatenate([jnp.array([0]), jnp.cumsum(split_sizes)])
+    src_cumsum = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(split_sizes).astype(jnp.int32)]
+    )
 
     # Compute destination chunk boundaries based on sorted order
     sorted_sizes = split_sizes[sorted_indices]
-    dest_cumsum = jnp.concatenate([jnp.array([0]), jnp.cumsum(sorted_sizes)])
+    dest_cumsum = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(sorted_sizes).astype(jnp.int32)]
+    )
 
     # For each source chunk, compute its destination offset
     # inverse_indices[i] = position of chunk i in sorted order
@@ -386,7 +394,7 @@ def reference_make_chunk_sort_map(
 
     # Create row_id_map: for each token position, compute its destination
     # First, figure out which chunk each position belongs to
-    position_indices = jnp.arange(num_tokens)
+    position_indices = jnp.arange(num_tokens, dtype=jnp.int32)
 
     # chunk_ids[i] = which chunk position i belongs to
     chunk_ids = jnp.searchsorted(src_cumsum[1:], position_indices, side="right")