[Perf] Warmup forward_native sampler kernel

vllm/v1/worker/gpu_model_runner.py

@@ -5701,6 +5701,26 @@ def _dummy_sampler_run(
             sampler_output = self.sampler(
                 logits=logits, sampling_metadata=dummy_metadata
             )
+            # Also warm forward_native (taken when generators dict is non-empty),
+            # but skip the extra call in 'processed_logits' / 'processed_logprobs'
+            # modes — there TopKTopPSampler binds forward = forward_native at
+            # init time, so the warmup call is redundant and only inflates peak
+            # memory during profile_run.
+            # No .clone() of logits: warmup output is discarded, so any in-place
+            # mutation by forward_native does not affect correctness.
+            if self.sampler.logprobs_mode not in (
+                "processed_logits",
+                "processed_logprobs",
+            ):
+                self.sampler(
+                    logits=logits,
+                    sampling_metadata=replace(
+                        dummy_metadata,
+                        generators={
+                            0: torch.Generator(device=self.device).manual_seed(0)
+                        },
+                    ),
+                )
         except RuntimeError as e:
             if "out of memory" in str(e):
                 raise RuntimeError(