[Bugfix][CI] Move resolving cudagraph_mode before initializing attn_metadata_builder

docs/design/cuda_graphs.md

@@ -167,7 +167,7 @@ class AttentionCGSupport(enum.Enum):
     """NO CUDA Graphs support"""
 ```
 
-Suppose we have hybrid attention backends (e.g., in mamba mixer models). In that case, we seek the minimum capability of all backends to determine the final capability of the model, and we might resolve the incompatible CUDA Graphs mode by downgrading the mode to the best fit one. For example, downgrading `FULL` mode to `FULL_AND_PIECEWISE` mode if the minimum capability is `UNIFORM_BATCH`, or `PIECEWISE` mode if the minimum capability is `NEVER` for -O3 compilation mode. For the complete fallback policy, please see the code of [initialize_cudagraph_capture][vllm.v1.worker.gpu_model_runner.GPUModelRunner.initialize_cudagraph_capture].
+Suppose we have hybrid attention backends (e.g., in mamba mixer models). In that case, we seek the minimum capability of all backends to determine the final capability of the model, and we might resolve the incompatible CUDA Graphs mode by downgrading the mode to the best fit one. For example, downgrading `FULL` mode to `FULL_AND_PIECEWISE` mode if the minimum capability is `UNIFORM_BATCH`, or `PIECEWISE` mode if the minimum capability is `NEVER` for -O3 compilation mode. For the complete fallback policy, please see the code for [this][vllm.v1.worker.gpu_model_runner.GPUModelRunner._check_and_update_cudagraph_mode].
 
 The following table lists backends that support full CUDA Graphs at the time of writing.
 

tests/compile/test_fusions_e2e.py

@@ -132,6 +132,9 @@ def test_attn_quant(
         mode = CUDAGraphMode.FULL_AND_PIECEWISE
         splitting_ops: list[str] | None = None
     else:
+        # FIXME: Llama-4-Scout-17B-16E-Instruct-FP8 + FlashInfer + Blackwell end at
+        # CUDAGraphMode.NONE here because it derives an attention backend that
+        # does not support full cudagraphs
         mode = CUDAGraphMode.FULL_DECODE_ONLY
         splitting_ops = []
 

vllm/v1/worker/gpu_model_runner.py

@@ -3751,8 +3751,6 @@ def capture_model(self) -> int:
                 "ensure `cudagraph_mode` was not manually set to `NONE`"
             )
             return 0
-        else:
-            self.initialize_cudagraph_capture()
 
         compilation_counter.num_gpu_runner_capture_triggers += 1
 
@@ -3926,7 +3924,7 @@ class AttentionGroupKey(NamedTuple):
 
         def get_attn_backends_for_group(
             kv_cache_group_spec: KVCacheGroupSpec,
-        ) -> dict[AttentionGroupKey, list[str]]:
+        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
             layers = get_layers_from_vllm_config(
                 self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
             )
@@ -3955,7 +3953,10 @@ def get_attn_backends_for_group(
                     attn_backend, layer_kv_cache_spec
                 )
                 attn_backend_layers[key].append(layer_name)
-            return {attn_backends[k]: v for k, v in attn_backend_layers.items()}
+            return (
+                {attn_backends[k]: v for k, v in attn_backend_layers.items()},
+                set(group_key.attn_backend for group_key in attn_backends.values()),
+            )
 
         def create_attn_groups(
             attn_backends_map: dict[AttentionGroupKey, list[str]],
@@ -3976,28 +3977,39 @@ def create_attn_groups(
                 attn_groups.append(attn_group)
             return attn_groups
 
+        attention_backend_maps = []
+        attention_backend_set: set[type[AttentionBackend]] = set()
         for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
             attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
-            self.attn_groups.append(create_attn_groups(attn_backends))
+            attention_backend_maps.append(attn_backends[0])
+            attention_backend_set.update(attn_backends[1])
+
+        # Resolve cudagraph_mode before actually initialize metadata_builders
+        self._check_and_update_cudagraph_mode(attention_backend_set)
+
+        for attn_backends_map in attention_backend_maps:
+            self.attn_groups.append(create_attn_groups(attn_backends_map))
 
         # Calculate reorder batch threshold (if needed)
         self.calculate_reorder_batch_threshold()
 
-    def initialize_cudagraph_capture(self) -> None:
+    def _check_and_update_cudagraph_mode(
+        self, attention_backends: set[type[AttentionBackend]]
+    ) -> None:
         """
         Resolve the cudagraph_mode when there are multiple attention
         backends with potential conflicting CUDA graph support.
         Then initialize the cudagraph_dispatcher based on the resolved
         cudagraph_mode.
         """
         min_cg_support = AttentionCGSupport.ALWAYS
-        min_cg_builder_name = None
+        min_cg_backend_name = None
 
-        for attn_group in self._attn_group_iterator():
-            builder = attn_group.get_metadata_builder()
-            if builder.cudagraph_support.value < min_cg_support.value:
-                min_cg_support = builder.cudagraph_support
-                min_cg_builder_name = builder.__class__.__name__
+        for attn_backend in attention_backends:
+            builder_cls = attn_backend.get_builder_cls()
+            if builder_cls.cudagraph_support.value < min_cg_support.value:
+                min_cg_support = builder_cls.cudagraph_support
+                min_cg_backend_name = attn_backend.__name__
         # Flexible resolve the cudagraph mode
         cudagraph_mode = self.compilation_config.cudagraph_mode
         # check cudagraph for mixed batch is supported
@@ -4007,7 +4019,7 @@ def initialize_cudagraph_capture(self) -> None:
         ):
             msg = (
                 f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
-                f"with {min_cg_builder_name} backend (support: "
+                f"with {min_cg_backend_name} backend (support: "
                 f"{min_cg_support})"
             )
             if min_cg_support == AttentionCGSupport.NEVER:
@@ -4038,7 +4050,7 @@ def initialize_cudagraph_capture(self) -> None:
         ):
             msg = (
                 f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
-                f"with {min_cg_builder_name} backend (support: "
+                f"with {min_cg_backend_name} backend (support: "
                 f"{min_cg_support})"
             )
             if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
@@ -4072,7 +4084,7 @@ def initialize_cudagraph_capture(self) -> None:
             msg = (
                 f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
                 f" with spec-decode for attention backend "
-                f"{min_cg_builder_name} (support: {min_cg_support})"
+                f"{min_cg_backend_name} (support: {min_cg_support})"
             )
             if self.compilation_config.splitting_ops_contain_attention():
                 msg += "; setting cudagraph_mode=PIECEWISE"
@@ -4094,14 +4106,14 @@ def initialize_cudagraph_capture(self) -> None:
         ):
             raise ValueError(
                 f"CUDAGraphMode.{cudagraph_mode.name} is not "
-                f"supported with {min_cg_builder_name} backend ("
+                f"supported with {min_cg_backend_name} backend ("
                 f"support:{min_cg_support}) "
                 "; please try cudagraph_mode=PIECEWISE, "
                 "and make sure compilation mode is VLLM_COMPILE"
             )
 
-        # Trigger cudagraph dispatching keys initialization here (after
-        # initializing attn backends).
+        # Trigger cudagraph dispatching keys initialization after
+        # resolved cudagraph mode.
         self.cudagraph_dispatcher.initialize_cudagraph_keys(
             self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
         )