[core] Gate the overlap WAR barrier on forward reads to recover decode throughput (#28363)

Co-authored-by: thanhhao98 <31717833+thanhhao98@users.noreply.github.com>

python/sglang/srt/model_executor/model_runner.py

@@ -542,6 +542,11 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         # Init forward stream for overlap schedule
         self.forward_stream = torch.get_device_module(self.device).Stream()
 
+        # WAR fast-path: a decode-graph forward publishes a fresh event here after
+        # load_batch; the scheduler's WAR barrier waits on it (then clears it)
+        # instead of the whole-forward wait_stream. None -> whole-forward fallback.
+        self.war_fastpath_read_done_event: Optional[torch.cuda.Event] = None
+
         # CPU offload
         set_offloader(create_offloader_from_server_args(server_args, dp_rank=dp_rank))
 

python/sglang/srt/model_executor/runner/decode_cuda_graph_runner.py

@@ -979,6 +979,17 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
         )
         with timer_ctx, self.backend.replay_session():
             self.load_batch(forward_batch, pp_proxy_tensors)
+            # Snapshot built -- publish a read-done event for the WAR barrier.
+            # Only plain DECODE: the captured decode graph reads only its static
+            # snapshot, so the forward is done reading the shared pool here. Spec
+            # verify (target_verify/dllm_extend) replays on this runner too, but
+            # those are NOT the step's last shared-buffer-reading phase (eagle
+            # publishes from draft_extend; ngram/dflash must not publish here),
+            # and some verify graphs may read beyond the snapshot in replay.
+            if forward_batch.forward_mode.is_decode():
+                read_done = self.device_module.Event()
+                read_done.record()
+                self.model_runner.war_fastpath_read_done_event = read_done
             output = self.backend.replay(self._replay_graph_key, forward_batch)
 
         if isinstance(output, LogitsProcessorOutput):