[Model Runner V2] fix draft attention metadata generation

vllm/v1/worker/gpu/attn_utils.py

@@ -30,7 +30,10 @@ def get_kv_cache_spec(vllm_config: VllmConfig) -> dict[str, KVCacheSpec]:
 
 
 def init_attn_backend(
-    kv_cache_config: KVCacheConfig, vllm_config: VllmConfig, device: torch.device
+    kv_cache_config: KVCacheConfig,
+    vllm_config: VllmConfig,
+    device: torch.device,
+    active_layer_names: set[str] | None = None,
 ):
     attn_backends: dict[str, type[AttentionBackend]] = {}
     attn_groups: list[list[AttentionGroup]] = []
@@ -39,6 +42,8 @@ def init_attn_backend(
         kv_cache_config.kv_cache_groups
     ):
         layer_names = kv_cache_group_spec.layer_names
+        if active_layer_names is not None:
+            layer_names = list(active_layer_names.intersection(layer_names))
 
         layer_type = cast(type[Any], AttentionLayerBase)
         attn_layers = get_layers_from_vllm_config(vllm_config, layer_type, layer_names)

vllm/v1/worker/gpu/model_runner.py

@@ -351,7 +351,6 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
             self.speculator.set_attn(
                 self.model_state,
                 self.kv_cache_config,
-                self.attn_groups,
                 self.block_tables,
             )
 

vllm/v1/worker/gpu/spec_decode/eagle/speculator.py

@@ -5,15 +5,17 @@
 import torch
 import torch.nn as nn
 
-from vllm.config import VllmConfig
+from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.config.compilation import CUDAGraphMode
 from vllm.forward_context import BatchDescriptor, set_forward_context
 from vllm.logger import init_logger
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.triton_utils import tl, triton
 from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.worker.gpu.attn_utils import (
     build_attn_metadata,
     build_slot_mappings_by_layer,
+    init_attn_backend,
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.dp_utils import sync_cudagraph_and_dp_padding
@@ -22,7 +24,6 @@
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
 from vllm.v1.worker.gpu.spec_decode.eagle.cudagraph import EagleCudaGraphManager
 from vllm.v1.worker.gpu.spec_decode.eagle.utils import load_eagle_model
-from vllm.v1.worker.utils import AttentionGroup
 
 logger = init_logger(__name__)
 
@@ -87,18 +88,35 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
         )
 
     def load_model(self, target_model: nn.Module) -> None:
+        target_attn_layer_names = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        ).keys()
+
         self.model = load_eagle_model(target_model, self.vllm_config)
 
+        all_attn_layers = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        ).keys()
+        self.draft_attn_layer_names = set(all_attn_layers) - set(
+            target_attn_layer_names
+        )
+
     def set_attn(
         self,
         model_state: ModelState,
         kv_cache_config: KVCacheConfig,
-        attn_groups: list[list[AttentionGroup]],
         block_tables: BlockTables,
     ) -> None:
         self.model_state = model_state
         self.kv_cache_config = kv_cache_config
-        self.attn_groups = attn_groups
+        _, self.attn_groups = init_attn_backend(
+            kv_cache_config,
+            self.vllm_config,
+            self.device,
+            active_layer_names=self.draft_attn_layer_names,
+        )
         self.block_tables = block_tables
 
     @torch.inference_mode()