vllm.distributed.kv_transfer.kv_connector.v1.multi_connector ¶

class MultiConnector(KVConnectorBase_V1):
    """
    A wrapper for using multiple KVConnectors at the same time.

    The current logic is:
    - Load KV from the first connector that advertises available tokens from
      get_num_new_matched_tokens(), based on the order in the config.
    - Save to all connectors.
    """

    def __init__(self, vllm_config: "VllmConfig", role: KVConnectorRole):
        super().__init__(vllm_config=vllm_config, role=role)

        self._connectors: list[KVConnectorBase_V1] = []
        self._ktc_kv_transfer_config = []
        for connector_cls, temp_config in self._get_connector_classes_and_configs(
            vllm_config
        ):
            self._connectors.append(connector_cls(temp_config, role))
            self._ktc_kv_transfer_config.append(temp_config.kv_transfer_config)

        # A mapping from request id to the index of the connector chosen to
        # load the request from (if any).
        self._requests_to_connector: dict[str, int] = {}

        # Keeps track of *additional* remaining async saves (beyond 1) to be
        # finished per request. Not needed for async loads since we only allow
        # a single connector to load.
        # Propagated from scheduler to worker side via the connector metadata.
        self._extra_async_saves: dict[str, int] = {}

    @classmethod
    def _get_connector_classes_and_configs(
        cls, vllm_config: "VllmConfig"
    ) -> list[tuple[type[KVConnectorBaseType], "VllmConfig"]]:
        assert vllm_config.kv_transfer_config is not None
        ktcs = vllm_config.kv_transfer_config.kv_connector_extra_config.get(
            "connectors"
        )
        assert ktcs is not None
        ret: list[tuple[type[KVConnectorBaseType], VllmConfig]] = []
        for ktc in ktcs:
            temp_config = copy.copy(vllm_config)
            engine_id = ktc.get("engine_id", vllm_config.kv_transfer_config.engine_id)
            temp_config.kv_transfer_config = KVTransferConfig(
                **ktc, engine_id=engine_id
            )
            ret.append(
                (
                    KVConnectorFactory.get_connector_class(
                        temp_config.kv_transfer_config
                    ),
                    temp_config,
                )
            )
        return ret

    def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
        for c in self._connectors:
            c.register_kv_caches(kv_caches)

    # We must override the base class method here because we need to bind
    # the metadata to each connector in the order of the connectors in the
    # MultiKVConnectorMetadata.
    def bind_connector_metadata(self, connector_metadata: KVConnectorMetadata) -> None:
        assert isinstance(connector_metadata, MultiKVConnectorMetadata)
        if connector_metadata.extra_async_saves:
            self._extra_async_saves.update(connector_metadata.extra_async_saves)
        for c, cm in zip(self._connectors, connector_metadata.metadata):
            c.bind_connector_metadata(cm)

    def clear_connector_metadata(self) -> None:
        for c in self._connectors:
            c.clear_connector_metadata()

    def shutdown(self):
        exception: Exception | None = None
        for c in self._connectors:
            try:
                c.shutdown()
            except Exception as e:
                logger.exception(
                    "Exception during connector %s shutdown.", c.__class__.__name__
                )
                exception = e
        if exception:
            raise exception

    # ==============================
    # Worker-side methods
    # ==============================
    def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None:
        for c in self._connectors:
            c.start_load_kv(forward_context, **kwargs)

    def wait_for_layer_load(self, layer_name: str) -> None:
        for c in self._connectors:
            c.wait_for_layer_load(layer_name)

    def save_kv_layer(
        self,
        layer_name: str,
        kv_layer: torch.Tensor,
        attn_metadata: "AttentionMetadata",
        **kwargs,
    ) -> None:
        for c in self._connectors:
            c.save_kv_layer(layer_name, kv_layer, attn_metadata, **kwargs)

    def wait_for_save(self):
        for c in self._connectors:
            c.wait_for_save()

    def get_finished(
        self, finished_req_ids: set[str]
    ) -> tuple[set[str] | None, set[str] | None]:
        finished_sending: set[str] = set()
        finished_recving: set[str] = set()
        for c in self._connectors:
            sending, recving = c.get_finished(finished_req_ids)
            if not recving and not sending:
                continue
            # Aggregate finished recving request ids.
            finished_recving.update(recving or ())
            # Aggregate finished sending request ids - only include
            # once we've drained the "extra" count (for cases where
            # more than one connector is async-saving the same request).
            for req_id in sending or ():
                extra_pending = self._extra_async_saves.get(req_id)
                if extra_pending is None:
                    finished_sending.add(req_id)
                    continue
                assert extra_pending > 0
                if extra_pending == 1:
                    del self._extra_async_saves[req_id]
                else:
                    self._extra_async_saves[req_id] = extra_pending - 1

        return finished_sending or None, finished_recving or None

    def get_block_ids_with_load_errors(self) -> set[int]:
        agg_block_ids: set[int] = set()
        for c in self._connectors:
            agg_block_ids |= c.get_block_ids_with_load_errors()
        return agg_block_ids

    # ==============================
    # Scheduler-side methods
    # ==============================
    def get_num_new_matched_tokens(
        self,
        request: "Request",
        num_computed_tokens: int,
    ) -> tuple[int | None, bool]:
        to_return = (0, False)
        for i, c in enumerate(self._connectors):
            toks, load_async = c.get_num_new_matched_tokens(
                request, num_computed_tokens
            )
            # If there is a connector still looking up the matches,
            # we return None to indicate that we are not done yet.
            if toks is None:
                return (None, False)
            # The first connector that has new matched tokens will be assigned
            # to this request.
            if to_return[0] == 0 and toks > 0:
                self._requests_to_connector[request.request_id] = i
                to_return = (toks, load_async)
        return to_return

    def update_state_after_alloc(
        self, request: "Request", blocks: "KVCacheBlocks", num_external_tokens: int
    ):
        chosen_connector = self._requests_to_connector.get(request.request_id, -1)
        empty_blocks = blocks.new_empty()
        for i, c in enumerate(self._connectors):
            if i == chosen_connector:
                # Forward call to the chosen connector (if any).
                c.update_state_after_alloc(request, blocks, num_external_tokens)
            else:
                # Call with empty blocks for other connectors.
                c.update_state_after_alloc(request, empty_blocks, 0)

    def build_connector_meta(
        self, scheduler_output: SchedulerOutput
    ) -> MultiKVConnectorMetadata:
        metadata = MultiKVConnectorMetadata(
            metadata=tuple(
                c.build_connector_meta(scheduler_output) for c in self._connectors
            )
        )
        if self._extra_async_saves:
            metadata.extra_async_saves = self._extra_async_saves
            self._extra_async_saves = {}
        return metadata

    def update_connector_output(self, connector_output: KVConnectorOutput):
        for c in self._connectors:
            c.update_connector_output(connector_output)

    def request_finished(
        self,
        request: "Request",
        blocks: list[int],
    ) -> tuple[bool, dict[str, Any] | None]:
        async_saves = 0
        kv_txfer_params = None
        for c in self._connectors:
            async_save, txfer_params = c.request_finished(request, blocks)
            if async_save:
                async_saves += 1
            if txfer_params is not None:
                if kv_txfer_params is not None:
                    # TODO we can probably change this to merge the dicts here,
                    # checking for key clashes.
                    raise RuntimeError(
                        "Only one connector can produce KV transfer params"
                    )
                kv_txfer_params = txfer_params
        if async_saves > 1:
            self._extra_async_saves[request.request_id] = async_saves - 1

        # Clean up other state for this request.
        self._requests_to_connector.pop(request.request_id, None)

        return async_saves > 0, kv_txfer_params

    def take_events(self) -> Iterable["KVCacheEvent"]:
        for c in self._connectors:
            yield from c.take_events()

    @classmethod
    def get_required_kvcache_layout(cls, vllm_config: "VllmConfig") -> str | None:
        """
        Get the required KV cache layout for this connector.
        Args:
            vllm_config (VllmConfig): the vllm config.

        Returns:
            str: the required KV cache layout. e.g. HND, or NHD.
            None if the connector does not require a specific layout.
        """
        assert vllm_config.kv_transfer_config is not None
        layouts: set[str] = set()
        for connector_cls, temp_config in cls._get_connector_classes_and_configs(
            vllm_config
        ):
            required_kvcache_layout = connector_cls.get_required_kvcache_layout(
                temp_config
            )
            if required_kvcache_layout is not None:
                layouts.add(required_kvcache_layout)

        if len(layouts) > 1:
            raise ValueError(
                f"KV cache layout mismatch: "
                f"found {len(layouts)} different layouts "
                f"({', '.join(layouts)})."
                f"All connectors must use the same layout."
            )
        return next(iter(layouts), None)

    @classmethod
    def build_kv_connector_stats(
        cls, data: dict[str, Any] | None = None
    ) -> KVConnectorStats | None:
        if data is None:
            return MultiKVConnectorStats()

        # data is a dict mapping connector name to their stats data.
        # The stats data can be either:
        # 1. Already-instantiated KVConnectorStats objects (same process)
        # 2. Serialized dicts (cross-process after serialization)
        # We need to reconstruct proper KVConnectorStats objects from dicts
        reconstructed_data = {}
        for connector_name, stats_value in data.items():
            # If already a KVConnectorStats object, use it directly
            if isinstance(stats_value, KVConnectorStats):
                reconstructed_data[connector_name] = stats_value
                continue

            # Otherwise, reconstruct from serialized dict
            # Get the connector class to reconstruct its stats
            connector_cls = KVConnectorFactory.get_connector_class_by_name(
                connector_name
            )

            # stats_value is the serialized dataclass which contains {'data': {...}}
            # We need to extract the inner 'data' field to avoid double-nesting
            assert isinstance(stats_value, dict) and "data" in stats_value, (
                f"Expected a dict with a 'data' field, got {stats_value}"
            )
            inner_data = stats_value["data"]

            # Use the connector's build_kv_connector_stats to reconstruct
            if reconstructed_stats := connector_cls.build_kv_connector_stats(
                data=inner_data
            ):
                reconstructed_data[connector_name] = reconstructed_stats

        return MultiKVConnectorStats(data=reconstructed_data)

    def get_kv_connector_stats(self) -> MultiKVConnectorStats | None:
        # Group connector stats by connector type.
        stats_by_connector: MultiKVConnectorStats | None = None
        for c in self._connectors:
            stats = c.get_kv_connector_stats()
            if stats is None:
                continue
            if stats_by_connector is None:
                # Lazy init to allow optional return value.
                stats_by_connector = MultiKVConnectorStats()
            stats_by_connector[c.__class__.__name__] = stats
        return stats_by_connector

    @classmethod
    def build_prom_metrics(
        cls,
        vllm_config: "VllmConfig",
        metric_types: dict[type["PromMetric"], type["PromMetricT"]],
        labelnames: list[str],
        per_engine_labelvalues: dict[int, list[str]],
    ) -> KVConnectorPromMetrics:
        prom_metrics: dict[str, KVConnectorPromMetrics] = {}
        for connector_cls, temp_config in cls._get_connector_classes_and_configs(
            vllm_config
        ):
            connector_prom = connector_cls.build_prom_metrics(
                temp_config, metric_types, labelnames, per_engine_labelvalues
            )
            if connector_prom is not None:
                prom_metrics[connector_cls.__name__] = connector_prom
        return MultiKVConnectorPromMetrics(
            vllm_config,
            metric_types,
            labelnames,
            per_engine_labelvalues,
            prom_metrics,
        )

@dataclass
class MultiKVConnectorMetadata(KVConnectorMetadata):
    metadata: tuple[KVConnectorMetadata, ...]
    extra_async_saves: dict[str, int] | None = None

class MultiKVConnectorPromMetrics(KVConnectorPromMetrics):
    def __init__(
        self,
        vllm_config: "VllmConfig",
        metric_types: dict[type[PromMetric], type[PromMetricT]],
        labelnames: list[str],
        per_engine_labelvalues: dict[int, list[str]],
        prom_metrics: dict[str, KVConnectorPromMetrics],
    ):
        super().__init__(vllm_config, metric_types, labelnames, per_engine_labelvalues)
        self._prom_metrics = prom_metrics

    def observe(self, transfer_stats_data: dict[str, Any], engine_idx: int = 0):
        for connector_id, stats_data in transfer_stats_data.items():
            assert connector_id in self._prom_metrics, (
                f"{connector_id} is not contained in the list of registered connectors "
                f"with Prometheus metrics support: {self._prom_metrics.keys()}"
            )
            self._prom_metrics[connector_id].observe(stats_data["data"], engine_idx)

@dataclass
class MultiKVConnectorStats(KVConnectorStats):
    """
    Maintain a dict of KVConnectorStats objects, one for each connector.
    This is used to aggregate the stats from all connectors separately.
    """

    def aggregate(self, other: KVConnectorStats) -> KVConnectorStats:
        for connector_id, stats in other.data.items():
            if connector_id not in self.data:
                self[connector_id] = stats
            else:
                assert isinstance(stats, type(self.data[connector_id]))
                self[connector_id] = self[connector_id].aggregate(stats)
        return self

    def reset(self):
        for stats in self.data.values():
            stats.reset()

    def reduce(self) -> dict[str, Any]:
        # TODO (NickLucche) Adjust for logging on separate lines
        return {
            connector_id: stats.reduce() for connector_id, stats in self.data.items()
        }

    def is_empty(self) -> bool:
        return all(stats.is_empty() for stats in self.data.values())

    def __getitem__(self, connector_id: str) -> KVConnectorStats:
        return self.data[connector_id]

    def __setitem__(self, connector_id: str, stats: KVConnectorStats):
        self.data[connector_id] = stats