refresh resolve_seq_lens_cpu comments (#26463)

python/sglang/srt/managers/overlap_utils.py

@@ -196,9 +196,10 @@ class FutureMap:
         batch.input_ids = -future_indices
 
     def resolve_seq_lens_cpu(self, batch: ScheduleBatch) -> None:
-        # Lazy pull from new_seq_lens_buf for spec_v2 (accept_lens not known to
+        # seq_lens_cpu may be needed on the host for kernel-launch prep (some backends).
-        # schedule). Write into both CPU and GPU so SB.seq_lens stays a faithful
+        # Run this D2H on a standalone stream to avoid chain-blocking forward_n ->
-        # seq_lens_cpu mirror.
+        # prepare_{n+1}: a sync on the schedule stream would inherit its WAR barrier and
+        # stall the host until forward_n ends.
         fi = batch.spec_info.future_indices if batch.spec_info is not None else None
         if fi is None:
             return
@@ -211,12 +212,14 @@ class FutureMap:
             batch.seq_lens_sum = int(batch.seq_lens_cpu.sum())
             return
 
-        # seq_lens_cpu off the schedule stream: D2H the relay buf on a private
+        # Mechanism: don't sync the schedule stream; gate a private stream on the
-        # stream (gated on publish), host-select via req_pool_indices_cpu.
+        # publish event and copy into the static pinned buffer.
         self.fwd_prepare_d2h_stream.wait_event(self.publish_ready)
         with torch.get_device_module(self.device).stream(self.fwd_prepare_d2h_stream):
             self.new_seq_lens_cpu_pinned.copy_(self.new_seq_lens_buf, non_blocking=True)
         self.fwd_prepare_d2h_stream.synchronize()
+
+        # FIXME: fi == batch.req_pool_indices; unify future_indices and req_pool_indices.
         batch.seq_lens_cpu = self.new_seq_lens_cpu_pinned[batch.req_pool_indices_cpu]
         batch.seq_lens_sum = int(batch.seq_lens_cpu.sum())
 
@@ -225,7 +228,7 @@ class FutureMap:
         if indices.shape[0] == 0:
             return  # DP idle
         self.new_seq_lens_buf[indices] = new_seq_lens.to(self.new_seq_lens_buf.dtype)
-        # Fast path: only spec_v2 needs the event (schedule-stream D2H sync).
+        # Only spec_v2 needs the event; it gates the seq_lens D2H on the private stream.
         if self.spec_algo.is_some():
             if self.publish_ready is None:
                 self.publish_ready = torch.get_device_module(self.device).Event()