[Bugfix] Warm up Triton autotuner for GDN layers during V1 profiling

vllm/model_executor/models/qwen3_next.py

@@ -645,6 +645,101 @@ def forward(
         core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
         output[:num_tokens], _ = self.out_proj(core_attn_out)
 
+    def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
+        """Warm up GDN prefill kernels during V1 profiling.
+
+        During V1 profile runs, ``_forward_core`` returns early because
+        ``attn_metadata`` is ``None``, so the autotuned kernels used by
+        ``chunk_gated_delta_rule`` (e.g. ``solve_tril``,
+        ``chunk_scaled_dot_kkt``) are never invoked.  After profiling,
+        vLLM allocates KV cache using most of the remaining GPU memory.
+        When the first real inference triggers the autotuner it OOMs
+        because there is not enough memory left for benchmarking.
+
+        This method runs minimal forward passes through
+        ``chunk_gated_delta_rule`` with small dummy tensors to force
+        autotuning while GPU memory is still plentiful.  The autotuner
+        results are cached globally, so only the first layer incurs
+        actual benchmarking cost.
+
+        Most kernels use a fixed ``BT = chunk_size`` (64), but
+        ``chunk_fwd_kernel_o`` recomputes ``BT`` from the sequence
+        length: ``min(64, max(16, next_power_of_2(T)))``.  Since ``BT``
+        is part of its autotune key, we run warmup passes with T = 16,
+        32, and 64 to cover all possible ``BT`` values.
+
+        The decode path uses ``fused_sigmoid_gating_delta_rule_update``
+        which has fixed kernel parameters (no autotuning), so only the
+        prefill (chunked) path needs warming up.
+        """
+        if hasattr(self, "_prefill_kernels_warmed_up"):
+            return
+        self._prefill_kernels_warmed_up = True
+
+        device = mixed_qkv.device
+        dtype = mixed_qkv.dtype
+        num_k_heads = self.num_k_heads // self.tp_size
+        num_v_heads = self.num_v_heads // self.tp_size
+        _, state_dtype = self.get_state_dtype()
+
+        # Run warmup for each possible BT value of chunk_fwd_kernel_o:
+        #   T=16 → BT=16, T=32 → BT=32, T=64 → BT=64.
+        # Other kernels always use BT=chunk_size(64), so their autotune
+        # cache is populated on the first pass and reused thereafter.
+        for T in (16, 32, 64):
+            q = torch.randn(
+                1, T, num_k_heads, self.head_k_dim, device=device, dtype=dtype
+            )
+            k = torch.randn(
+                1, T, num_k_heads, self.head_k_dim, device=device, dtype=dtype
+            )
+            v = torch.randn(
+                1, T, num_v_heads, self.head_v_dim, device=device, dtype=dtype
+            )
+            g = torch.randn(1, T, num_v_heads, device=device, dtype=dtype)
+            beta = torch.randn(1, T, num_v_heads, device=device, dtype=dtype)
+            state = torch.zeros(
+                1,
+                num_v_heads,
+                self.head_v_dim,
+                self.head_k_dim,
+                device=device,
+                dtype=state_dtype,
+            )
+            cu_seqlens = torch.tensor([0, T], device=device, dtype=torch.long)
+
+            try:
+                self.chunk_gated_delta_rule(
+                    q=q,
+                    k=k,
+                    v=v,
+                    g=g,
+                    beta=beta,
+                    initial_state=state,
+                    output_final_state=False,
+                    cu_seqlens=cu_seqlens,
+                    use_qk_l2norm_in_kernel=True,
+                )
+            except Exception:
+                logger.warning(
+                    "GDN prefill kernel warmup (T=%d) failed for "
+                    "layer %s. First inference may OOM due to "
+                    "autotuner.",
+                    T,
+                    self.prefix,
+                    exc_info=True,
+                )
+            else:
+                logger.debug(
+                    "GDN prefill kernel warmup (T=%d) completed for layer %s",
+                    T,
+                    self.prefix,
+                )
+            finally:
+                del q, k, v, g, beta, state, cu_seqlens
+
+        torch.accelerator.empty_cache()
+
     def _forward_core(
         self,
         mixed_qkv: torch.Tensor,
@@ -659,7 +754,9 @@ def _forward_core(
         attn_metadata: AttentionMetadata = forward_context.attn_metadata
 
         if attn_metadata is None:
-            # V1 profile run
+            # V1 profile run — warm up prefill kernels so that
+            # autotuning completes before KV cache allocation.
+            self._warmup_prefill_kernels(mixed_qkv)
             return
 
         assert isinstance(attn_metadata, dict)