[Disagg][Perf] Use CUDA event sync instead of blocking `tolist` to avoid...

[Disagg][Perf] Use CUDA event sync instead of blocking `tolist` to avoid unintentional copy ops blocking across different CUDA streams, improving disagg TTIT/TTFT (#22760)
Signed-off-by: Zijing Liu <liuzijing2014@gmail.com>
Signed-off-by: Zijing Liu <liuzijing2014@users.noreply.github.com>

vllm/v1/worker/gpu_model_runner.py

@@ -316,6 +316,12 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # Cached outputs.
         self._draft_token_ids: Optional[Union[list[list[int]],
                                               torch.Tensor]] = None
+        self.transfer_event = torch.cuda.Event()
+        self.sampled_token_ids_pinned_cpu = torch.empty(
+            (self.max_model_len, 1),
+            dtype=torch.int64,
+            device="cpu",
+            pin_memory=True)
 
     def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
         return CpuGpuBuffer(*args,
@@ -1691,7 +1697,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         max_gen_len = sampled_token_ids.shape[-1]
         if max_gen_len == 1:
             # No spec decode tokens.
-            valid_sampled_token_ids = sampled_token_ids.tolist()
+            valid_sampled_token_ids = self._to_list(sampled_token_ids)
         else:
             # Includes spec decode tokens.
             valid_sampled_token_ids = self.rejection_sampler.parse_output(
@@ -3233,3 +3239,18 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     mamba_type=mamba_module.mamba_type)
 
         return kv_cache_spec
+
+    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
+        # This is a short term mitigation for issue mentioned in
+        # https://github.com/vllm-project/vllm/issues/22754.
+        # `tolist` would trigger a cuda wise stream sync, which
+        # would block other copy ops from other cuda streams.
+        # A cuda event sync would avoid such a situation. Since
+        # this is in the critical path of every single model
+        # forward loop, this has caused perf issue for a disagg
+        # setup.
+        pinned = self.sampled_token_ids_pinned_cpu[:sampled_token_ids.shape[0]]
+        pinned.copy_(sampled_token_ids, non_blocking=True)
+        self.transfer_event.record()
+        self.transfer_event.synchronize()
+        return pinned.tolist()