v1.0
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114
config/kv_transfer.py
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114
config/kv_transfer.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import hashlib
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import uuid
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from dataclasses import field
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from typing import Any, Literal, get_args
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from pydantic.dataclasses import dataclass
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from vllm.config.utils import config
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KVProducer = Literal["kv_producer", "kv_both"]
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KVConsumer = Literal["kv_consumer", "kv_both"]
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KVRole = Literal[KVProducer, KVConsumer]
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@config
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@dataclass
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class KVTransferConfig:
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"""Configuration for distributed KV cache transfer."""
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kv_connector: str | None = None
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"""The KV connector for vLLM to transmit KV caches between vLLM instances.
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"""
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engine_id: str | None = None
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"""The engine id for KV transfers."""
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kv_buffer_device: str = "cuda"
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"""The device used by kv connector to buffer the KV cache. Choices are
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'cuda' and 'cpu'."""
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kv_buffer_size: float = 1e9
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"""The buffer size for TorchDistributedConnector. Measured in number of
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bytes. Recommended value: 1e9 (about 1GB)."""
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kv_role: KVRole | None = None
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"""Whether this vLLM instance produces, consumes KV cache, or both. Choices
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are 'kv_producer', 'kv_consumer', and 'kv_both'."""
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kv_rank: int | None = None
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"""The rank of this vLLM instance in the KV cache transfer. Typical value:
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0 for prefill instance, 1 for decode instance.
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Currently only 1P1D is supported."""
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kv_parallel_size: int = 1
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"""The number of parallel instances for KV cache transfer. For
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P2pNcclConnector, this should be 2."""
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kv_ip: str = "127.0.0.1"
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"""The KV connector ip, used to build distributed connection."""
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kv_port: int = 14579
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"""The KV connector port, used to build distributed connection."""
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kv_connector_extra_config: dict[str, Any] = field(default_factory=dict)
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"""any extra config that the connector may need."""
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kv_connector_module_path: str | None = None
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"""The Python module path to dynamically load the KV connector from.
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Only supported in V1."""
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enable_permute_local_kv: bool = False
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"""Experiment feature flag to enable HND to NHD KV Transfer"""
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def compute_hash(self) -> str:
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"""
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WARNING: Whenever a new field is added to this config,
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ensure that it is included in the factors list if
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it affects the computation graph.
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Provide a hash that uniquely identifies all the configs
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that affect the structure of the computation
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graph from input ids/embeddings to the final hidden states,
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excluding anything before input ids/embeddings and after
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the final hidden states.
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"""
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# no factors to consider.
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# this config will not affect the computation graph.
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factors: list[Any] = []
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hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
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return hash_str
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def __post_init__(self) -> None:
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if self.engine_id is None:
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self.engine_id = str(uuid.uuid4())
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if self.kv_role is not None and self.kv_role not in get_args(KVRole):
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raise ValueError(
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f"Unsupported kv_role: {self.kv_role}. "
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f"Supported roles are {get_args(KVRole)}"
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)
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if self.kv_connector is not None and self.kv_role is None:
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raise ValueError(
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"Please specify kv_role when kv_connector "
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f"is set, supported roles are {get_args(KVRole)}"
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)
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@property
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def is_kv_transfer_instance(self) -> bool:
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return self.kv_connector is not None and self.kv_role in get_args(KVRole)
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@property
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def is_kv_producer(self) -> bool:
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return self.kv_connector is not None and self.kv_role in get_args(KVProducer)
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@property
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def is_kv_consumer(self) -> bool:
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return self.kv_connector is not None and self.kv_role in get_args(KVConsumer)
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def get_from_extra_config(self, key, default) -> Any:
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return self.kv_connector_extra_config.get(key, default)
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