[Bugfix] Split attention groups by num_heads_q for spec-decode drafts

.buildkite/test_areas/spec_decode.yaml

@@ -32,6 +32,7 @@ steps:
   source_file_dependencies:
     - vllm/v1/spec_decode/
     - vllm/v1/worker/gpu/spec_decode/
+    - vllm/v1/attention/backends/
     - vllm/transformers_utils/configs/speculators/
     - tests/v1/e2e/spec_decode/
   commands:

vllm/v1/worker/gpu_model_runner.py

@@ -6556,8 +6556,21 @@ def initialize_attn_backend(
         assert len(self.attn_groups) == 0, "Attention backends are already initialized"
 
         class AttentionGroupKey(NamedTuple):
+            """Deduplication key for attention groups within a KV cache group.
+
+            Splits on per-rank ``num_heads_q`` in addition to backend + spec
+            so layers with different Q-head counts (e.g. a spec-decode draft
+            with fewer attention heads than its target) get separate metadata
+            builders. The builders' scratch (e.g. ``softmax_segm_*`` in
+            ``triton_attn``, ``num_qo_heads`` in FlashInfer) is sized by
+            ``num_heads_q`` and assumes uniformity within the group; see
+            ``get_num_attention_heads_from_layers`` in
+            ``vllm/v1/attention/backends/utils.py``.
+            """
+
             attn_backend: type[AttentionBackend]
             kv_cache_spec: KVCacheSpec
+            num_heads_q: int
 
         def get_attn_backends_for_group(
             kv_cache_group_spec: KVCacheGroupSpec,
@@ -6586,9 +6599,16 @@ def get_attn_backends_for_group(
                 layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                 if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
                     layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
-                key = (full_cls_name, layer_kv_cache_spec)
+                # Non-Attention layer types (e.g. Mamba1, ShortConv) do not
+                # expose ``num_heads``; fall back to 0 so they cluster as
+                # before. Such layers never coexist with Attention in a
+                # single KV cache group (different KVCacheSpec), so the
+                # fallback can never spuriously merge them with attention
+                # layers.
+                num_heads_q = getattr(layers[layer_name], "num_heads", 0)
+                key = (full_cls_name, layer_kv_cache_spec, num_heads_q)
                 attn_backends[key] = AttentionGroupKey(
-                    attn_backend, layer_kv_cache_spec
+                    attn_backend, layer_kv_cache_spec, num_heads_q
                 )
                 attn_backend_layers[key].append(layer_name)
             return (
@@ -6601,11 +6621,11 @@ def create_attn_groups(
             kv_cache_group_id: int,
         ) -> list[AttentionGroup]:
             attn_groups: list[AttentionGroup] = []
-            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
+            for key, layer_names in attn_backends_map.items():
                 attn_group = AttentionGroup(
-                    attn_backend,
+                    key.attn_backend,
                     layer_names,
-                    kv_cache_spec,
+                    key.kv_cache_spec,
                     kv_cache_group_id,
                 )