[MISC] Move bind_kv_cache to worker module

tests/v1/test_utils.py

@@ -3,7 +3,7 @@
 
 import torch
 
-from vllm.v1.utils import bind_kv_cache
+from vllm.v1.worker.utils import bind_kv_cache
 
 
 def test_bind_kv_cache():

vllm/v1/utils.py

@@ -4,7 +4,6 @@
 import multiprocessing
 import time
 import weakref
-from collections import defaultdict
 from collections.abc import Sequence
 from multiprocessing import connection
 from multiprocessing.process import BaseProcess
@@ -14,14 +13,12 @@
 import torch
 
 from vllm.logger import init_logger
-from vllm.model_executor.models.utils import extract_layer_index
 from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                   usage_message)
 from vllm.utils import (get_open_port, get_open_zmq_ipc_path, get_tcp_uri,
                         kill_process_tree)
 
 if TYPE_CHECKING:
-    from vllm.attention.layer import Attention
     from vllm.v1.engine.coordinator import DPCoordinator
     from vllm.v1.engine.utils import (CoreEngineActorManager,
                                       CoreEngineProcManager)
@@ -275,51 +272,6 @@ def shutdown(procs: list[BaseProcess]):
             kill_process_tree(pid)
 
 
-def bind_kv_cache(
-    kv_caches: dict[str, torch.Tensor],
-    forward_context: dict[str, "Attention"],
-    runner_kv_caches: list[torch.Tensor],
-) -> None:
-    """
-    Bind the allocated KV cache to both ModelRunner and forward context so
-    that the KV cache can be used in the forward pass.
-
-    This function:
-      1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
-         kv_caches.
-      2) Associates each attention layer in the `forward_context` with its 
-         corresponding KV cache in kv_caches.
-
-    Args:
-        kv_caches: The allocated kv_caches with layer names as keys.
-        forward_context: The global forward context containing all Attention 
-        layers with layer names as keys.
-        runner_kv_caches: The kv_cache declared by ModelRunner.
-    """
-    # Bind kv_caches to ModelRunner
-    assert len(runner_kv_caches) == 0
-
-    # Convert kv_caches dict to a list of tensors in the order of layer_index.
-    index2name = defaultdict(list)
-    for layer_name in kv_caches:
-        index2name[extract_layer_index(layer_name)].append(layer_name)
-
-    for layer_index in sorted(index2name.keys()):
-        layer_names = index2name[layer_index]
-        if len(layer_names) > 1:
-            # One typical case is encoder-decoder model, e.g., bart.
-            # The cross attention and self attention in the same decoder layer
-            # has different layer_name but the same layer_index.
-            raise NotImplementedError
-        layer_name = layer_names[0]
-        runner_kv_caches.append(kv_caches[layer_name])
-
-    # Bind kv_caches to forward context
-    for layer_name, kv_cache in kv_caches.items():
-        # NOTE: Use list because of v0 PP virtual engine.
-        forward_context[layer_name].kv_cache = [kv_cache]
-
-
 def copy_slice(from_tensor: torch.Tensor, to_tensor: torch.Tensor,
                length: int) -> torch.Tensor:
     """

vllm/v1/worker/gpu_model_runner.py

@@ -62,13 +62,13 @@
 from vllm.v1.spec_decode.medusa import MedusaProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
-from vllm.v1.utils import bind_kv_cache
 from vllm.v1.worker.block_table import BlockTable
 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 
 from ..sample.logits_processor import LogitsProcessorManager
-from .utils import (gather_mm_placeholders, initialize_kv_cache_for_kv_sharing,
+from .utils import (bind_kv_cache, gather_mm_placeholders,
+                    initialize_kv_cache_for_kv_sharing,
                     sanity_check_mm_encoder_outputs, scatter_mm_placeholders)
 
 if TYPE_CHECKING:

vllm/v1/worker/tpu_model_runner.py

@@ -42,11 +42,10 @@
                              LogprobsTensors, ModelRunnerOutput)
 from vllm.v1.sample.tpu.metadata import TPUSupportedSamplingMetadata
 from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
-from vllm.v1.utils import bind_kv_cache
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 from vllm.v1.worker.tpu_input_batch import CachedRequestState, InputBatch
 
-from .utils import (initialize_kv_cache_for_kv_sharing,
+from .utils import (bind_kv_cache, initialize_kv_cache_for_kv_sharing,
                     sanity_check_mm_encoder_outputs)
 
 if TYPE_CHECKING:

vllm/v1/worker/tpu_worker.py

@@ -25,8 +25,9 @@
 from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig,
                                         KVCacheSpec)
 from vllm.v1.outputs import ModelRunnerOutput
-from vllm.v1.utils import bind_kv_cache, report_usage_stats
+from vllm.v1.utils import report_usage_stats
 from vllm.v1.worker.tpu_model_runner import TPUModelRunner
+from vllm.v1.worker.utils import bind_kv_cache
 
 logger = init_logger(__name__)
 

vllm/v1/worker/utils.py

@@ -1,12 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Optional
+from collections import defaultdict
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
 from vllm.model_executor.models.interfaces import MultiModalEmbeddings
+from vllm.model_executor.models.utils import extract_layer_index
 from vllm.v1.kv_cache_interface import KVCacheGroupSpec
 
+if TYPE_CHECKING:
+    from vllm.attention.layer import Attention
+
 
 def sanity_check_mm_encoder_outputs(
     mm_embeddings: MultiModalEmbeddings,
@@ -110,3 +115,48 @@ def initialize_kv_cache_for_kv_sharing(
         kv_caches[layer_name] = kv_caches[target_layer_name]
         group_idx = layer_to_kv_cache_group_idx[target_layer_name]
         kv_cache_groups[group_idx].layer_names.append(layer_name)
+
+
+def bind_kv_cache(
+    kv_caches: dict[str, torch.Tensor],
+    forward_context: dict[str, "Attention"],
+    runner_kv_caches: list[torch.Tensor],
+) -> None:
+    """
+    Bind the allocated KV cache to both ModelRunner and forward context so
+    that the KV cache can be used in the forward pass.
+
+    This function:
+      1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
+         kv_caches.
+      2) Associates each attention layer in the `forward_context` with its
+         corresponding KV cache in kv_caches.
+
+    Args:
+        kv_caches: The allocated kv_caches with layer names as keys.
+        forward_context: The global forward context containing all Attention
+        layers with layer names as keys.
+        runner_kv_caches: The kv_cache declared by ModelRunner.
+    """
+    # Bind kv_caches to ModelRunner
+    assert len(runner_kv_caches) == 0
+
+    # Convert kv_caches dict to a list of tensors in the order of layer_index.
+    index2name = defaultdict(list)
+    for layer_name in kv_caches:
+        index2name[extract_layer_index(layer_name)].append(layer_name)
+
+    for layer_index in sorted(index2name.keys()):
+        layer_names = index2name[layer_index]
+        if len(layer_names) > 1:
+            # One typical case is encoder-decoder model, e.g., bart.
+            # The cross attention and self attention in the same decoder layer
+            # has different layer_name but the same layer_index.
+            raise NotImplementedError
+        layer_name = layer_names[0]
+        runner_kv_caches.append(kv_caches[layer_name])
+
+    # Bind kv_caches to forward context
+    for layer_name, kv_cache in kv_caches.items():
+        # NOTE: Use list because of v0 PP virtual engine.
+        forward_context[layer_name].kv_cache = [kv_cache]