[Model Runner V2][Perf] align dummy_run tokens to uniform decode for dp cudagraph (#35376)

Signed-off-by: zhuhaoran <zhuhaoran.zhr@alibaba-inc.com>

vllm/v1/worker/gpu/model_runner.py

@@ -39,6 +39,7 @@ from vllm.model_executor.model_loader import get_model_loader
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import SupportedTask
+from vllm.utils.math_utils import cdiv
 from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
@@ -327,9 +328,22 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
     @torch.inference_mode()
     def _dummy_run(
-        self, num_tokens: int, *args, skip_attn: bool = True, **kwargs
+        self,
+        num_tokens: int,
+        *args,
+        skip_attn: bool = True,
+        uniform_decode: bool = False,
+        **kwargs,
     ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
         # Create a dummy scheduler output.
+        if uniform_decode:
+            # Align tokens to uniform_decode_query_len for cudagraph
+            # compatibility across DP ranks.
+            query_len = self.cudagraph_manager.uniform_decode_query_len
+            num_reqs = min(cdiv(num_tokens, query_len), self.max_num_reqs)
+            num_tokens = num_reqs * query_len
+            num_tokens_per_request = [query_len] * num_reqs
+        else:
             num_reqs = min(num_tokens, self.max_num_reqs)
             num_tokens_per_request = [num_tokens // num_reqs] * num_reqs
             num_tokens_per_request[-1] += num_tokens % num_reqs