fix(args): auto-default --attention-backend to TURBO_ATTN for plugin kv dtypes

tests/engine/test_arg_utils.py

@@ -889,9 +889,12 @@ def test_dp_rank_shards_user_assigned_gpu_ids(self):
             )
 
 
-def test_plugin_kv_cache_dtype_auto_selects_custom_attention_backend():
+def test_plugin_kv_cache_dtype_auto_selects_turbo_attn_backend():
     """A plugin-registered kv-cache dtype should auto-default the attention
-    backend to CUSTOM, but only when the user did not pass an explicit choice.
+    backend to TURBO_ATTN (the slot such plugins register into), but only when
+    the user did not pass an explicit choice. The worker's load-time lifecycle
+    hooks dispatch only to a named backend, so without this a
+    ``--kv-cache-dtype tkv`` boot never ran the plugin's ``on_model_loaded``.
     """
     import torch
 
@@ -907,7 +910,7 @@ def test_plugin_kv_cache_dtype_auto_selects_custom_attention_backend():
     try:
         parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
 
-        # 1. Plugin dtype + no --attention-backend → CUSTOM auto-selected.
+        # 1. Plugin dtype + no --attention-backend → TURBO_ATTN auto-selected.
         args = parser.parse_args(
             [
                 "--model",
@@ -919,7 +922,7 @@ def test_plugin_kv_cache_dtype_auto_selects_custom_attention_backend():
         engine_args = EngineArgs.from_cli_args(args)
         vllm_config = engine_args.create_engine_config()
         assert vllm_config.attention_config.backend == (
-            AttentionBackendEnum.CUSTOM
+            AttentionBackendEnum.TURBO_ATTN
         )
 
         # 2. Plugin dtype + explicit --attention-backend → user choice honoured.

vllm/config/cache.py

@@ -84,7 +84,7 @@ def is_plugin_cache_dtype(name: str) -> bool:
     """Return True if ``name`` was registered via ``register_cache_dtype``.
 
     Used by arg post-processing to auto-default ``--attention-backend`` to
-    ``CUSTOM`` when the user selects a plugin-registered KV cache dtype.
+    ``TURBO_ATTN`` when the user selects a plugin-registered KV cache dtype.
     """
     return name in _PLUGIN_CACHE_DTYPES
 

vllm/engine/arg_utils.py

@@ -73,6 +73,7 @@
     MambaCacheMode,
     MambaDType,
     PrefixCachingHashAlgo,
+    is_plugin_cache_dtype,
 )
 from vllm.config.device import Device
 from vllm.config.kernel import IrOpPriorityConfig, LinearBackend, MoEBackend
@@ -2406,6 +2407,28 @@ def create_engine_config(
                 self.attention_backend
             )
 
+        # Plugin-registered KV cache dtypes (e.g. tkv) ship their own
+        # AttentionBackend, registered under AttentionBackendEnum.TURBO_ATTN
+        # via register_backend(). When the user selects such a dtype without
+        # --attention-backend, default to TURBO_ATTN: the platform would pick
+        # it anyway, but only a NAMED backend reaches the lifecycle hooks the
+        # worker dispatches before its attention groups exist
+        # (gpu_worker._backends_in_use -> on_model_loaded, adjust_kv_budget;
+        # kv_cache_manager -> on_kv_manager_created). An explicit choice
+        # (--attention-backend or --attention-config.backend) is honoured.
+        if attention_config.backend is None and is_plugin_cache_dtype(
+            resolved_cache_dtype
+        ):
+            from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+            attention_config.backend = AttentionBackendEnum.TURBO_ATTN
+            logger.info(
+                "Auto-selecting attention backend TURBO_ATTN for "
+                "plugin-registered kv-cache dtype %r. Pass "
+                "--attention-backend to override.",
+                resolved_cache_dtype,
+            )
+
         # TurboQuant requires FlashAttention 2 — FA3 boundary layers assert
         # FlashAttentionImpl which fails with TurboQuantAttentionImpl.
         if resolved_cache_dtype.startswith("turboquant_") and (