[KVConnector] Migrate the LMCache integration code to be vLLM native

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

@@ -3,14 +3,19 @@
 from typing import TYPE_CHECKING, Any
 
 import torch
-from lmcache.integration.vllm.vllm_v1_adapter import LMCacheConnectorV1Impl
+from lmcache.integration.vllm.vllm_v1_adapter import (
+    LMCacheConnectorV1Impl as LMCacheConnectorLatestImpl,
+)
 
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
     KVConnectorMetadata,
     KVConnectorRole,
 )
+from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration import (
+    vllm_v1_adapter as _adapter,
+)
 from vllm.logger import init_logger
 from vllm.v1.core.sched.output import SchedulerOutput
 
@@ -26,7 +31,18 @@
 class LMCacheConnectorV1(KVConnectorBase_V1):
     def __init__(self, vllm_config: "VllmConfig", role: KVConnectorRole):
         super().__init__(vllm_config=vllm_config, role=role)
-        self._lmcache_engine = LMCacheConnectorV1Impl(vllm_config, role, self)
+        assert vllm_config.kv_transfer_config is not None
+        use_native = vllm_config.kv_transfer_config.get_from_extra_config(
+            "use_native", False
+        )
+        if use_native:
+            logger.info("Initializing native LMCache connector")
+            cls = _adapter.LMCacheConnectorV1Impl
+        else:
+            logger.info("Initializing latest dev LMCache connector")
+            cls = LMCacheConnectorLatestImpl
+
+        self._lmcache_engine = cls(vllm_config, role, self)
 
     # ==============================
     # Worker-side methods

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/utils.py

@@ -0,0 +1,221 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Standard
+import os
+import threading
+from typing import TYPE_CHECKING, Union
+
+import torch
+from lmcache.config import LMCacheEngineConfig as Config
+from lmcache.logging import init_logger
+from lmcache.v1.config import LMCacheEngineConfig as V1Config
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+    from vllm.multimodal.inputs import PlaceholderRange
+    from vllm.v1.core.sched.output import NewRequestData
+    from vllm.v1.request import Request
+
+logger = init_logger(__name__)
+ENGINE_NAME = "vllm-instance"
+
+# Thread-safe singleton storage
+_config_instance: Config | V1Config | None = None
+_config_lock = threading.Lock()
+
+
+def is_false(value: str) -> bool:
+    """Check if the given string value is equivalent to 'false'."""
+    return value.lower() in ("false", "0", "no", "n", "off")
+
+
+def lmcache_get_or_create_config() -> Config | V1Config:
+    """Get the LMCache configuration from the environment variable
+    `LMCACHE_CONFIG_FILE`. If the environment variable is not set, this
+    function will return the default configuration.
+
+    This function is thread-safe and implements singleton pattern,
+    ensuring the configuration is loaded only once.
+    """
+    global _config_instance
+
+    # Double-checked locking for thread-safe singleton
+    if _config_instance is None:
+        with _config_lock:
+            if _config_instance is None:  # Check again within lock
+                if is_false(os.getenv("LMCACHE_USE_EXPERIMENTAL", "True")):
+                    logger.warning(
+                        "Detected LMCACHE_USE_EXPERIMENTAL is set to False. "
+                        "Using legacy configuration is deprecated and will "
+                        "be remove soon! Please set LMCACHE_USE_EXPERIMENTAL "
+                        "to True."
+                    )
+                    LMCacheEngineConfig = Config  # type: ignore[assignment]
+                else:
+                    LMCacheEngineConfig = V1Config  # type: ignore[assignment]
+
+                if "LMCACHE_CONFIG_FILE" not in os.environ:
+                    logger.warning(
+                        "No LMCache configuration file is set. Trying to read"
+                        " configurations from the environment variables."
+                    )
+                    logger.warning(
+                        "You can set the configuration file through "
+                        "the environment variable: LMCACHE_CONFIG_FILE"
+                    )
+                    _config_instance = LMCacheEngineConfig.from_env()
+                else:
+                    config_file = os.environ["LMCACHE_CONFIG_FILE"]
+                    logger.info("Loading LMCache config file %s", config_file)
+                    _config_instance = LMCacheEngineConfig.from_file(config_file)
+                    # Update config from environment variables
+                    _config_instance.update_config_from_env()
+    return _config_instance
+
+
+def hex_hash_to_int16(s: str) -> int:
+    """
+    Convert a hex hash string to a 16-bit integer.
+    """
+    return int(s, 16) & 0xFFFF
+
+
+def apply_mm_hashes_to_token_ids(
+    token_ids: torch.Tensor,
+    mm_hashes: list[str],
+    mm_positions: list["PlaceholderRange"],
+) -> torch.Tensor:
+    """
+    Overwrite token_ids in-place for multimodal placeholders using
+    efficient slice assignments.
+    """
+    n = token_ids.size(0)
+    for hash_str, placeholder in zip(mm_hashes, mm_positions):
+        start, length = placeholder.offset, placeholder.length
+        if start >= n:
+            continue
+        end = min(start + length, n)
+        token_ids[start:end] = hex_hash_to_int16(hash_str)
+    return token_ids
+
+
+def mla_enabled(model_config: "ModelConfig") -> bool:
+    return (
+        hasattr(model_config, "use_mla")
+        and isinstance(model_config.use_mla, bool)
+        and model_config.use_mla
+    )
+
+
+def create_lmcache_metadata(
+    vllm_config=None, model_config=None, parallel_config=None, cache_config=None
+):
+    """
+    Create LMCacheEngineMetadata from vLLM configuration.
+
+    This function extracts common metadata creation logic that was duplicated
+    across multiple files.
+
+    Args:
+        vllm_config (VllmConfig): vLLM configuration object containing model,
+                                  parallel, and cache configs (alternative to
+                                  individual config parameters)
+        model_config (ModelConfig): Model configuration (alternative to
+                                    vllm_config)
+        parallel_config (ParallelConfig): Parallel configuration (alternative
+                                          to vllm_config)
+        cache_config (CacheConfig): Cache configuration (alternative to
+                                    vllm_config)
+    """
+    # Third Party
+    # First Party
+    from lmcache.config import LMCacheEngineMetadata
+
+    from vllm.utils import get_kv_cache_torch_dtype
+
+    config = lmcache_get_or_create_config()
+    # Support both vllm_config object and individual config parameters
+    if vllm_config is not None:
+        model_cfg = vllm_config.model_config
+        parallel_cfg = vllm_config.parallel_config
+        cache_cfg = vllm_config.cache_config
+    else:
+        if model_config is None or parallel_config is None or cache_config is None:
+            raise ValueError(
+                "Either vllm_config must be provided, or all of "
+                "model_config, parallel_config, and cache_config must be provided."
+            )
+        model_cfg = model_config
+        parallel_cfg = parallel_config
+        cache_cfg = cache_config
+
+    # Get KV cache dtype
+    kv_dtype = get_kv_cache_torch_dtype(cache_cfg.cache_dtype, model_cfg.dtype)
+
+    # Check if MLA is enabled
+    use_mla = mla_enabled(model_cfg)
+
+    # Construct KV shape (for memory pool)
+    num_layer = model_cfg.get_num_layers(parallel_cfg)
+    chunk_size = config.chunk_size
+    num_kv_head = model_cfg.get_num_kv_heads(parallel_cfg)
+    head_size = model_cfg.get_head_size()
+    kv_shape = (num_layer, 1 if use_mla else 2, chunk_size, num_kv_head, head_size)
+
+    # Create metadata
+    metadata = LMCacheEngineMetadata(
+        model_cfg.model,
+        parallel_cfg.world_size,
+        parallel_cfg.rank,
+        "vllm",
+        kv_dtype,
+        kv_shape,
+        use_mla,
+    )
+
+    return metadata, config
+
+
+def extract_mm_features(
+    request: Union["Request", "NewRequestData"], modify: bool = False
+) -> tuple[list[str], list["PlaceholderRange"]]:
+    """
+    Normalize multimodal information from a Request into parallel lists.
+
+    This helper reads either:
+      1) `request.mm_features` (objects each exposing `.identifier` and
+      `.mm_position`), or
+      2) legacy fields `request.mm_hashes` and `request.mm_positions`.
+
+    It returns two equally sized lists: the multimodal hash identifiers and
+    their corresponding positions. If the request contains no multimodal info,
+    it returns `([], [])`.
+
+    Args:
+        request (Request): The source object.
+        modify (bool):
+            Controls copy semantics for the legacy-path return values.
+            - If True and legacy fields are used, shallow-copies are returned so
+              the caller can mutate the lists without affecting `request`.
+            - If False, the original legacy sequences are returned as-is
+              (zero-copy); treat them as read-only.
+
+    Returns:
+        tuple[list[str], list[PlaceholderRange]]: (`mm_hashes`, `mm_positions`).
+        May be `([], [])` when no multimodal data is present.
+    """
+    if getattr(request, "mm_features", None):
+        mm_hashes, mm_positions = zip(
+            *((f.identifier, f.mm_position) for f in request.mm_features)
+        )
+        return (list(mm_hashes), list(mm_positions))
+    elif getattr(request, "mm_hashes", None):
+        if modify:
+            return (
+                request.mm_hashes.copy(),  # type: ignore
+                request.mm_positions.copy(),  # type: ignore
+            )
+        else:
+            return (request.mm_hashes, request.mm_positions)  # type: ignore
+    else:
+        return ([], [])