[Misc][PD] Fix get_attn_backend usage in transfer connectors (#31988)

Signed-off-by: NickLucche <nlucches@redhat.com>

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -1439,7 +1439,7 @@ def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
         patch(f"{nixl_module}.NixlWrapper") as mock_nixl_wrapper,
         patch(f"{nixl_module}.threading.Event"),
         patch(f"{nixl_module}.threading.Thread") as mock_thread,
-        patch(f"{nixl_module}.get_attn_backend") as mock_get_attn_backend,
+        patch(f"{nixl_module}.get_current_attn_backend") as mock_get_attn_backend,
     ):
         # Ensure get_attn_backend returns the correct value due to
         # _cached_get_attn_backend returning the backend from previous

vllm/distributed/kv_transfer/kv_connector/utils.py

@@ -6,13 +6,14 @@ KV cache helper for store.
 
 from collections.abc import Iterator
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import torch
 
-from vllm.config import get_current_vllm_config
+from vllm.config import VllmConfig, get_current_vllm_config, get_layers_from_vllm_config
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.logger import init_logger
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.v1.attention.backend import AttentionBackend
 from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput
 
@@ -433,3 +434,26 @@ class TpKVTopology:
     ) -> list[int]:
         remote_tp_size = self.remote_tp_size[remote_engine_id]
         return self.get_target_remote_ranks(remote_tp_size)
+
+
+def get_current_attn_backend(vllm_config: VllmConfig):
+    layer_type = cast(type[Any], AttentionLayerBase)
+    layers = get_layers_from_vllm_config(vllm_config, layer_type, None)
+    if layers:
+        backend = next(iter(layers.values())).get_attn_backend()
+    else:
+        # Fallback for tests, when static_forward_context is empty.
+        logger.debug(
+            "No layers found in the vLLM config. "
+            "Falling back to default attention backend."
+        )
+        from vllm.v1.attention.selector import get_attn_backend
+
+        backend = get_attn_backend(
+            head_size=vllm_config.model_config.get_head_size(),
+            dtype=vllm_config.model_config.dtype,
+            kv_cache_dtype=vllm_config.cache_config.cache_dtype,
+            block_size=vllm_config.cache_config.block_size,
+            use_mla=vllm_config.model_config.use_mla,
+        )
+    return backend

vllm/distributed/kv_transfer/kv_connector/v1/mooncake_connector.py

@@ -16,7 +16,10 @@ import zmq.asyncio
 
 from vllm import envs
 from vllm.config import VllmConfig
-from vllm.distributed.kv_transfer.kv_connector.utils import TpKVTopology
+from vllm.distributed.kv_transfer.kv_connector.utils import (
+    TpKVTopology,
+    get_current_attn_backend,
+)
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
     KVConnectorMetadata,
@@ -32,7 +35,6 @@ from vllm.logger import init_logger
 from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
 from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
-from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 
@@ -468,13 +470,9 @@ class MooncakeConnectorWorker:
         self.cache_config = vllm_config.cache_config
         self.use_mla = self.model_config.use_mla
 
-        backend = get_attn_backend(
-            self.model_config.get_head_size(),
-            self.model_config.dtype,
-            self.cache_config.cache_dtype,
-            self.block_size,
-            use_mla=self.use_mla,
-        )
+        # Get the attention backend from the first layer
+        # NOTE (NickLucche) models with multiple backends are not supported yet
+        backend = get_current_attn_backend(vllm_config)
         self.backend_name = backend.get_name()
         self.kv_cache_layout = get_kv_cache_layout()
         logger.debug("Detected attention backend %s", self.backend_name)

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -24,6 +24,7 @@ from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     EngineId,
     TpKVTopology,
+    get_current_attn_backend,
     kv_postprocess_blksize_and_layout_on_receive,
     kv_postprocess_blksize_on_receive,
     kv_postprocess_layout_on_receive,
@@ -53,7 +54,6 @@ from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_path, make_zmq_socket
 from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
-from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.worker.block_table import BlockTable
 
@@ -957,13 +957,10 @@ class NixlConnectorWorker:
         self.block_window_per_layer: list[int | None] = []
         self.use_mla = self.model_config.use_mla
 
-        backend = get_attn_backend(
-            self.model_config.get_head_size(),
-            self.model_config.dtype,
-            self.cache_config.cache_dtype,
-            self.block_size,
-            use_mla=self.use_mla,
-        )
+        # Get the attention backend from the first layer
+        # NOTE (NickLucche) models with multiple backends are not supported yet
+        backend = get_current_attn_backend(vllm_config)
+
         self.backend_name = backend.get_name()
         self.kv_cache_layout = get_kv_cache_layout()
         self.host_buffer_kv_cache_layout = self.kv_cache_layout