[Attention] Refactor AttentionMetadata Preparation for Encoder-only Models (#23154)

Signed-off-by: Chen Zhang <zhangch99@outlook.com>

tests/v1/worker/test_gpu_model_runner.py

@@ -680,6 +680,7 @@ def test_init_kv_cache_with_kv_sharing_valid():
         kv_cache_spec[layer_0].page_size_bytes
 
     runner.initialize_kv_cache(kv_cache_config)
+    kv_cache_config_after_init = runner.kv_cache_config
 
     layer_0_kv = vllm_ctx[layer_0].kv_cache[0]
     layer_1_kv = vllm_ctx[layer_1].kv_cache[0]
@@ -687,10 +688,12 @@ def test_init_kv_cache_with_kv_sharing_valid():
     assert id(layer_1_kv) == id(layer_0_kv)
 
     # check layer 1 added to kv cache group's layer names
-    assert len(kv_cache_config.kv_cache_groups) == 1
-    assert len(kv_cache_config.kv_cache_groups[0].layer_names) == 2
-    assert kv_cache_config.kv_cache_groups[0].layer_names[0] == layer_0
-    assert kv_cache_config.kv_cache_groups[0].layer_names[1] == layer_1
+    assert len(kv_cache_config_after_init.kv_cache_groups) == 1
+    assert len(kv_cache_config_after_init.kv_cache_groups[0].layer_names) == 2
+    assert kv_cache_config_after_init.kv_cache_groups[0].layer_names[
+        0] == layer_0
+    assert kv_cache_config_after_init.kv_cache_groups[0].layer_names[
+        1] == layer_1
 
 
 def test_hybrid_attention_mamba_tensor_shapes(monkeypatch):

vllm/attention/layers/chunked_local_attention.py

@@ -6,12 +6,13 @@ from typing import List, Optional
 import torch
 
 from vllm import envs
-from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata)
 from vllm.attention.selector import get_attn_backend
 from vllm.config import CacheConfig, QuantizationConfig
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata, make_local_attention_virtual_batches,
-    subclass_attention_backend, subclass_attention_metadata_builder)
+    subclass_attention_backend)
 
 from ..layer import Attention
 
@@ -24,21 +25,23 @@ def create_chunked_local_attention_backend(
 ) -> type[AttentionBackend]:
     prefix = f"ChunkedLocalAttention_{attention_chunk_size}_{block_size}_"
 
-    def build_preprocess_fn(cm: CommonAttentionMetadata):
-        return make_local_attention_virtual_batches(attention_chunk_size, cm,
-                                                    block_size)
+    underlying_builder = underlying_attn_backend.get_builder_cls()
+
+    class ChunkedLocalAttentionBuilder(underlying_builder):  # type: ignore
+
+        def build(self,
+                  common_prefix_len: int,
+                  common_attn_metadata: CommonAttentionMetadata,
+                  fast_build: bool = False) -> AttentionMetadata:
+            common_attn_metadata = make_local_attention_virtual_batches(
+                attention_chunk_size, common_attn_metadata, block_size)
+            return super().build(common_prefix_len, common_attn_metadata,
+                                 fast_build)
 
-    # Dynamically create a new attention backend that wraps the
-    # underlying attention backend but applies
-    # `make_local_attention_virtual_batches` before calling `build(...)`
-    builder_cls = subclass_attention_metadata_builder(
-        name_prefix=prefix,
-        builder_cls=underlying_attn_backend.get_builder_cls(),
-        build_preprocess_fn=build_preprocess_fn)
     attn_backend = subclass_attention_backend(
         name_prefix=prefix,
         attention_backend_cls=underlying_attn_backend,
-        builder_cls=builder_cls)
+        builder_cls=ChunkedLocalAttentionBuilder)
 
     return attn_backend
 

vllm/attention/layers/encoder_only_attention.py

@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import functools
+from copy import copy
+from typing import Optional
+
+import torch
+from transformers import CacheConfig
+
+from vllm import envs
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata, AttentionType)
+from vllm.attention.layer import Attention
+from vllm.attention.selector import get_attn_backend
+from vllm.v1.attention.backends.utils import (CommonAttentionMetadata,
+                                              subclass_attention_backend)
+
+
+@functools.lru_cache
+def create_encoder_only_attention_backend(
+    underlying_attn_backend: AttentionBackend, ) -> type[AttentionBackend]:
+    prefix = "EncoderOnlyAttention_"
+    underlying_builder = underlying_attn_backend.get_builder_cls()
+
+    class EncoderOnlyAttentionBuilder(underlying_builder):  # type: ignore
+
+        def build(self,
+                  common_prefix_len: int,
+                  common_attn_metadata: CommonAttentionMetadata,
+                  fast_build: bool = False) -> AttentionMetadata:
+            new_common_attn_metadata = copy(common_attn_metadata)
+            new_common_attn_metadata.causal = False
+            return super().build(common_prefix_len, new_common_attn_metadata,
+                                 fast_build)
+
+    attn_backend = subclass_attention_backend(
+        name_prefix=prefix,
+        attention_backend_cls=underlying_attn_backend,
+        builder_cls=EncoderOnlyAttentionBuilder)
+
+    return attn_backend
+
+
+class EncoderOnlyAttention(Attention):
+    """
+    Encoder attention is a special case that doesn't need a KV Cache.
+    """
+
+    def __init__(self,
+                 num_heads: int,
+                 head_size: int,
+                 scale: float,
+                 cache_config: Optional[CacheConfig] = None,
+                 attn_type: Optional[str] = None,
+                 **kwargs):
+        dtype = torch.get_default_dtype()
+
+        if cache_config is not None:
+            kv_cache_dtype = cache_config.cache_dtype
+            block_size = cache_config.block_size
+        else:
+            kv_cache_dtype = "auto"
+            block_size = 16
+
+        if envs.VLLM_USE_V1:
+            underlying_attn_backend = get_attn_backend(head_size, dtype,
+                                                       kv_cache_dtype,
+                                                       block_size)
+
+            attn_backend = create_encoder_only_attention_backend(
+                underlying_attn_backend)
+        else:
+            # in v0 encoder only attention is handled inside the backends
+            attn_backend = None
+
+        if attn_type is not None:
+            assert attn_type == AttentionType.ENCODER_ONLY, \
+                "EncoderOnlyAttention only supports AttentionType.ENCODER_ONLY"
+
+        super().__init__(num_heads=num_heads,
+                         head_size=head_size,
+                         scale=scale,
+                         cache_config=cache_config,
+                         attn_backend=attn_backend,
+                         attn_type=AttentionType.ENCODER_ONLY,
+                         **kwargs)

vllm/model_executor/models/bert.py

@@ -8,7 +8,7 @@ import torch
 from torch import nn
 from transformers import BertConfig
 
-from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, PoolerConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
@@ -239,14 +239,13 @@ class BertSelfAttention(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.qkv_proj")
 
-        self.attn = Attention(num_heads=self.num_heads,
-                              head_size=self.head_dim,
-                              scale=self.scaling,
-                              num_kv_heads=self.num_kv_heads,
-                              cache_config=cache_config,
-                              quant_config=quant_config,
-                              prefix=f"{prefix}.attn",
-                              attn_type=AttentionType.ENCODER_ONLY)
+        self.attn = EncoderOnlyAttention(num_heads=self.num_heads,
+                                         head_size=self.head_dim,
+                                         scale=self.scaling,
+                                         num_kv_heads=self.num_kv_heads,
+                                         cache_config=cache_config,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.attn")
 
     def forward(
         self,

vllm/model_executor/models/bert_with_rope.py

@@ -7,7 +7,7 @@ import torch
 from torch import nn
 from transformers import PretrainedConfig
 
-from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
@@ -119,14 +119,13 @@ class BertWithRopeAttention(nn.Module):
 
         self.rotary_emb = get_rope(**rotary_kwargs)
 
-        self.attn = Attention(num_heads=self.num_heads,
-                              head_size=self.head_dim,
-                              scale=self.scaling,
-                              num_kv_heads=self.num_kv_heads,
-                              cache_config=cache_config,
-                              quant_config=quant_config,
-                              prefix=f"{prefix}.attn",
-                              attn_type=AttentionType.ENCODER_ONLY)
+        self.attn = EncoderOnlyAttention(num_heads=self.num_heads,
+                                         head_size=self.head_dim,
+                                         scale=self.scaling,
+                                         num_kv_heads=self.num_kv_heads,
+                                         cache_config=cache_config,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.attn")
 
         self.out_proj = RowParallelLinear(input_size=hidden_size,
                                           output_size=hidden_size,

vllm/model_executor/models/llama.py

@@ -31,6 +31,7 @@ from torch import nn
 from transformers import LlamaConfig
 
 from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
@@ -173,7 +174,10 @@ class LlamaAttention(nn.Module):
             if is_sliding:
                 sliding_window = config.sliding_window
 
-        self.attn = Attention(
+        attn_cls = (EncoderOnlyAttention
+                    if attn_type == AttentionType.ENCODER_ONLY else Attention)
+
+        self.attn = attn_cls(
             self.num_heads,
             self.head_dim,
             self.scaling,

vllm/model_executor/models/modernbert.py

@@ -7,7 +7,7 @@ import torch
 from torch import nn
 from transformers import ModernBertConfig
 
-from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
@@ -104,12 +104,12 @@ class ModernBertAttention(nn.Module):
                                                     head_size=self.head_dim,
                                                     dim=self.head_dim,
                                                     base=rope_theta)
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              self.scaling,
-                              prefix=f"{layer_id}.attn",
-                              attn_type=AttentionType.ENCODER_ONLY,
-                              per_layer_sliding_window=sliding_window)
+        self.attn = EncoderOnlyAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            prefix=f"{layer_id}.attn",
+            per_layer_sliding_window=sliding_window)
         self.Wo = RowParallelLinear(config.hidden_size,
                                     config.hidden_size,
                                     bias=config.attention_bias)

vllm/model_executor/models/qwen2.py

@@ -32,6 +32,7 @@ from torch import nn
 from transformers import Qwen2Config
 
 from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
@@ -159,7 +160,9 @@ class Qwen2Attention(nn.Module):
             rope_scaling=rope_scaling,
             dual_chunk_attention_config=dual_chunk_attention_config,
         )
-        self.attn = Attention(
+        attn_cls = (EncoderOnlyAttention
+                    if attn_type == AttentionType.ENCODER_ONLY else Attention)
+        self.attn = attn_cls(
             self.num_heads,
             self.head_dim,
             self.scaling,

vllm/v1/attention/backends/utils.py

@@ -5,8 +5,7 @@ import enum
 import functools
 from abc import abstractmethod
 from dataclasses import dataclass, make_dataclass
-from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Generic, Optional,
-                    TypeVar)
+from typing import TYPE_CHECKING, Any, ClassVar, Generic, Optional, TypeVar
 
 import numpy as np
 import torch
@@ -543,35 +542,6 @@ def make_local_attention_virtual_batches(
     )
 
 
-def subclass_attention_metadata_builder(
-    name_prefix: str,
-    builder_cls: type[AttentionMetadataBuilder[M]],
-    build_preprocess_fn: Callable[[CommonAttentionMetadata],
-                                  CommonAttentionMetadata],
-) -> type[AttentionMetadataBuilder[M]]:
-    """
-    Return a new subclass of `builder_cls` whose .build(...) method
-    first calls build_preprocess_fn(common_attn_metadata) on the metadata.
-    """
-    name: str = name_prefix + builder_cls.__name__  # type: ignore
-
-    def build(self,
-              common_prefix_len: int,
-              common_attn_metadata: CommonAttentionMetadata,
-              fast_build: bool = False):
-        return builder_cls.build(self, common_prefix_len,
-                                 build_preprocess_fn(common_attn_metadata),
-                                 fast_build)
-
-    Wrapped = type(
-        name,
-        (builder_cls, ),  # inherit from the original
-        {
-            "build": build,
-        })
-    return Wrapped  # type: ignore
-
-
 def subclass_attention_backend(
         name_prefix: str, attention_backend_cls: type[AttentionBackend],
         builder_cls: type[AttentionMetadataBuilder[M]]

vllm/v1/kv_cache_interface.py

@@ -203,6 +203,14 @@ class MambaSpec(KVCacheSpec):
         return self.page_size_bytes
 
 
+@dataclass(frozen=True)
+class EncoderOnlyAttentionSpec(AttentionSpec):
+
+    def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
+        # Encoder-only layers do not need KV cache
+        return 0
+
+
 @dataclass
 class KVCacheTensor:
     """

vllm/v1/worker/gpu_model_runner.py

@@ -8,6 +8,7 @@ import time
 from collections import defaultdict
 from collections.abc import Iterator
 from contextlib import contextmanager
+from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Optional, Union, cast
 
 import numpy as np
@@ -62,9 +63,10 @@ from vllm.v1.attention.backends.utils import (
 from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
 from vllm.v1.kv_cache_interface import (AttentionSpec,
                                         ChunkedLocalAttentionSpec,
+                                        EncoderOnlyAttentionSpec,
                                         FullAttentionSpec, KVCacheConfig,
-                                        KVCacheSpec, MambaSpec,
-                                        SlidingWindowSpec)
+                                        KVCacheGroupSpec, KVCacheSpec,
+                                        MambaSpec, SlidingWindowSpec)
 from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, DraftTokenIds,
                              LogprobsTensors, ModelRunnerOutput)
 from vllm.v1.pool.metadata import PoolingMetadata
@@ -136,7 +138,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 cache_config.cache_dtype]
 
         self.is_pooling_model = model_config.pooler_config is not None
-        self.is_encoder_only_model = False
         self.is_multimodal_raw_input_supported = (
             model_config.is_multimodal_raw_input_supported)
         self.max_model_len = model_config.max_model_len
@@ -345,6 +346,11 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         self.reorder_batch_threshold: Optional[int] = None
 
+        # Attention layers that are only in the KVCacheConfig of the runner
+        # (e.g., KV sharing, encoder-only attention), but not in the
+        # KVCacheConfig of the scheduler.
+        self.runner_only_attn_layers: set[str] = set()
+
         # Cached outputs.
         self._draft_token_ids: Optional[Union[list[list[int]],
                                               torch.Tensor]] = None
@@ -834,23 +840,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         attn_metadata: dict[str, Any] = {}
 
-        # Prepare encoder attention metadata separately
-        # (encoder layers are not in KV cache groups)
-        if self.is_encoder_only_model:
-
-            per_layer_metadata = \
-                self._build_encoder_only_attn_metadata(
-                scheduler_output)
-
-            # Add encoder attention metadata for all encoder layers
-            attention_layers = get_layers_from_vllm_config(
-                self.vllm_config, Attention)
-            for layer_name, attn_module in attention_layers.items():
-                if attn_module.attn_type == AttentionType.ENCODER_ONLY:
-                    common_attn_metadata, encoder_attn_metadata =\
-                        per_layer_metadata[layer_name]
-                    attn_metadata[layer_name] = encoder_attn_metadata
-
         # Used in the below loop.
         query_start_loc_cpu = self.query_start_loc_cpu[:num_reqs + 1]
         seq_lens_cpu = self.seq_lens_cpu[:num_reqs]
@@ -863,13 +852,33 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         for kv_cache_group_id, kv_cache_group_spec in enumerate(
                 self.kv_cache_config.kv_cache_groups):
 
-            blk_table = self.input_batch.block_table[kv_cache_group_id]
-            blk_table_tensor = blk_table.get_device_tensor()[:num_reqs]
-            slot_mapping = blk_table.slot_mapping[:total_num_scheduled_tokens]
+            if isinstance(kv_cache_group_spec.kv_cache_spec,
+                          EncoderOnlyAttentionSpec):
+                # Encoder-only layers do not have KV cache, so we need to
+                # create a dummy block table and slot mapping for them.
+                blk_table_tensor = torch.zeros(
+                    (num_reqs, 1),
+                    dtype=torch.int32,
+                    pin_memory=self.pin_memory,
+                    device="cpu").to(self.device, non_blocking=True)
+                slot_mapping = torch.zeros((total_num_scheduled_tokens, ),
+                                           dtype=torch.int32,
+                                           pin_memory=self.pin_memory,
+                                           device="cpu").to(self.device,
+                                                            non_blocking=True)
+                num_common_prefix_blocks = 0
+            else:
+                blk_table = self.input_batch.block_table[kv_cache_group_id]
+                blk_table_tensor = blk_table.get_device_tensor()[:num_reqs]
+                slot_mapping = blk_table.slot_mapping[:
+                                                      total_num_scheduled_tokens]
 
-            # Fill unused with -1. Needed for reshape_and_cache in full cuda
-            # graph mode.
-            blk_table.slot_mapping[total_num_scheduled_tokens:].fill_(-1)
+                # Fill unused with -1. Needed for reshape_and_cache in full cuda
+                # graph mode.
+                blk_table.slot_mapping[total_num_scheduled_tokens:].fill_(-1)
+                num_common_prefix_blocks = (
+                    scheduler_output.
+                    num_common_prefix_blocks[kv_cache_group_id])
 
             common_attn_metadata = CommonAttentionMetadata(
                 query_start_loc=query_start_loc,
@@ -897,8 +906,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 if self.cascade_attn_enabled:
                     common_prefix_len = self._compute_cascade_attn_prefix_len(
                         num_scheduled_tokens,
-                        scheduler_output.
-                        num_common_prefix_blocks[kv_cache_group_id],
+                        num_common_prefix_blocks,
                         kv_cache_group_spec.kv_cache_spec,
                         builder,
                     )
@@ -2812,49 +2820,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # Calculate reorder batch threshold (if neeeded)
         self.calculate_reorder_batch_threshold()
 
-        if len(self.attn_groups) > 0:
-            return
-
-        # Check if model is encoder-only
-        block_size = self.vllm_config.cache_config.block_size
-        use_mla = self.vllm_config.model_config.use_mla
-        attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
-        for layer_name, attn_module in attn_layers.items():
-
-            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
-                if attn_module.sliding_window is None:
-                    attn_spec: AttentionSpec = FullAttentionSpec(
-                        block_size=block_size,
-                        num_kv_heads=attn_module.num_kv_heads,
-                        head_size=attn_module.head_size,
-                        dtype=self.kv_cache_dtype,
-                        use_mla=use_mla)
-                else:
-                    attn_spec = SlidingWindowSpec(
-                        block_size=block_size,
-                        num_kv_heads=attn_module.num_kv_heads,
-                        head_size=attn_module.head_size,
-                        dtype=self.kv_cache_dtype,
-                        sliding_window=attn_module.sliding_window,
-                        use_mla=use_mla)
-                attn_specs[attn_spec].append(layer_name)
-
-            else:
-                raise ValueError("Expected only encoder-only layers")
-
-        if len(attn_specs) > 0:
-            total_layers = 0
-            for attn_spec, layer_names in attn_specs.items():
-
-                attn_backends = get_attn_backends_for_layers(layer_names)
-                total_layers += len(layer_names)
-
-                self.attn_groups.append(
-                    create_attn_groups(attn_backends, attn_spec))
-            assert total_layers == len(attn_layers), \
-                "All or none of the layers are expected to be encoder-only"
-            self.is_encoder_only_model = True
-
     def initialize_cudagraph_capture(self) -> None:
         min_cg_support = AttentionCGSupport.ALWAYS
         min_cg_builder_name = None
@@ -3002,7 +2967,10 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         layer_names = set()
         for group in kv_cache_config.kv_cache_groups:
-            layer_names.update(group.layer_names)
+            for layer_name in group.layer_names:
+                if layer_name in self.runner_only_attn_layers:
+                    continue
+                layer_names.add(layer_name)
         assert layer_names == set(kv_cache_raw_tensors.keys(
         )), "Some layers are not correctly initialized"
         return kv_cache_raw_tensors
@@ -3040,6 +3008,8 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         for kv_cache_spec, group in self._kv_cache_spec_attn_group_iterator():
             attn_backend = group.backend
             for layer_name in group.layer_names:
+                if layer_name in self.runner_only_attn_layers:
+                    continue
                 raw_tensor = kv_cache_raw_tensors[layer_name]
                 assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
                 num_blocks = (raw_tensor.numel() //
@@ -3161,6 +3131,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 kv_cache_config.kv_cache_groups,
                 kv_caches,
                 self.attn_groups,
+                self.runner_only_attn_layers,
             )
             attn_layers = get_layers_from_vllm_config(self.vllm_config,
                                                       Attention)
@@ -3185,8 +3156,10 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             kv_cache_config: Configuration for the KV cache, including the KV
             cache size of each layer
         """
+        kv_cache_config = deepcopy(kv_cache_config)
         self.kv_cache_config = kv_cache_config
         self.may_reinitialize_input_batch(kv_cache_config)
+        self.may_add_encoder_only_layers_to_kv_cache_config()
         self.initialize_attn_backend(kv_cache_config)
         kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)
 
@@ -3199,6 +3172,33 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         if has_kv_transfer_group():
             get_kv_transfer_group().register_kv_caches(kv_caches)
 
+    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
+        """
+        Add encoder-only layers to the KV cache config.
+        """
+        block_size = self.vllm_config.cache_config.block_size
+        use_mla = self.vllm_config.model_config.use_mla
+        encoder_only_attn_specs: dict[AttentionSpec,
+                                      list[str]] = defaultdict(list)
+        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
+        for layer_name, attn_module in attn_layers.items():
+            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
+                attn_spec = EncoderOnlyAttentionSpec(
+                    block_size=block_size,
+                    num_kv_heads=attn_module.num_kv_heads,
+                    head_size=attn_module.head_size,
+                    dtype=self.kv_cache_dtype,
+                    use_mla=use_mla)
+                encoder_only_attn_specs[attn_spec].append(layer_name)
+                self.runner_only_attn_layers.add(layer_name)
+        if len(encoder_only_attn_specs) > 0:
+            assert len(
+                encoder_only_attn_specs
+            ) == 1, "Only support one encoder-only attention spec now"
+            spec, layer_names = encoder_only_attn_specs.popitem()
+            self.kv_cache_config.kv_cache_groups.append(
+                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec))
+
     def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
         """
         Generates the KVCacheSpec by parsing the kv cache format from each
@@ -3287,70 +3287,3 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     mamba_type=mamba_module.mamba_type)
 
         return kv_cache_spec
-
-    def _build_encoder_only_attn_metadata(
-            self, scheduler_output: "SchedulerOutput") -> \
-                dict[str, tuple[CommonAttentionMetadata, Any]]:
-        """Prepare encoder attention metadata for encoder-only models.
-
-        Args:
-            scheduler_output: Scheduler output
-
-        Returns:
-            dict[str, Any]: Encoder attention metadata
-        """
-        num_reqs = self.input_batch.num_reqs
-        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
-
-        # Get the number of scheduled tokens for each request.
-        req_ids = self.input_batch.req_ids
-        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
-        max_num_scheduled_tokens = max(tokens)
-
-        dummy_block_table = torch.zeros((num_reqs, 1),
-                                        dtype=torch.int32,
-                                        pin_memory=self.pin_memory,
-                                        device="cpu").to(self.device,
-                                                         non_blocking=True)
-        dummy_slot_mapping = torch.zeros((total_num_scheduled_tokens, ),
-                                         dtype=torch.int32,
-                                         pin_memory=self.pin_memory,
-                                         device="cpu").to(self.device,
-                                                          non_blocking=True)
-
-        group_metadata = dict[str, tuple[CommonAttentionMetadata, Any]]()
-
-        for attn_group_list in self.attn_groups:
-
-            assert len(attn_group_list) == 1
-            attn_group = attn_group_list[0]
-
-            # Use the first attention metadata builder
-            # to create encoder attention metadata
-            builder = attn_group.metadata_builder
-
-            common_metadata = CommonAttentionMetadata(
-                query_start_loc=self.query_start_loc[:num_reqs + 1],
-                query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs + 1],
-                seq_lens=self.seq_lens[:num_reqs],
-                seq_lens_cpu=self.seq_lens_cpu[:num_reqs],
-                num_computed_tokens_cpu=self.input_batch.
-                num_computed_tokens_cpu_tensor[:num_reqs],
-                num_reqs=num_reqs,
-                num_actual_tokens=total_num_scheduled_tokens,
-                max_query_len=max_num_scheduled_tokens,
-                max_seq_len=self.seq_lens_cpu[:num_reqs].max().item(),
-                block_table_tensor=dummy_block_table,
-                slot_mapping=dummy_slot_mapping,
-                causal=False,
-            )
-
-            metadata = builder.build(
-                common_prefix_len=0,  # No cascade for encoder
-                common_attn_metadata=common_metadata,
-            )
-
-            for layer_name in attn_group.layer_names:
-                group_metadata[layer_name] = (common_metadata, metadata)
-
-        return group_metadata

vllm/v1/worker/utils.py

@@ -204,6 +204,7 @@ def initialize_kv_cache_for_kv_sharing(
     kv_caches: dict[str, torch.Tensor],
     # Optional for now to avoid breaking TPU
     attn_groups: Optional[list[list[AttentionGroup]]] = None,
+    runner_only_attn_layers: Optional[set[str]] = None,
 ) -> None:
     """
     Sets up KV cache sharing by reusing the allocated KV caches in `kv_caches`
@@ -250,6 +251,9 @@ def initialize_kv_cache_for_kv_sharing(
             attn_groups[kv_cache_group_idx][attn_group_idx].layer_names.append(
                 layer_name)
 
+        if runner_only_attn_layers is not None:
+            runner_only_attn_layers.add(layer_name)
+
 
 def bind_kv_cache(
     kv_caches: dict[str, torch.Tensor],