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vllm.distributed.kv_transfer.kv_connector.v1.umbp.connector ¶

vLLM-facing shared UMBP connector.

Classes:

UMBPStoreConnector ¶

Bases: KVConnectorBase_V1

vLLM connector lifecycle shared by every UMBP runtime mode.

Source code in vllm/distributed/kv_transfer/kv_connector/v1/umbp/connector.py
class UMBPStoreConnector(KVConnectorBase_V1):
    """vLLM connector lifecycle shared by every UMBP runtime mode."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        role: KVConnectorRole,
        kv_cache_config: KVCacheConfig,
    ) -> None:
        super().__init__(vllm_config, role, kv_cache_config)
        topology = RankTopology.from_vllm_config(vllm_config)
        runtime_config = UMBPRuntimeConfig.from_vllm(
            vllm_config
        ).resolve_for_rank_count(topology.rank_count)
        if topology.pp_size > 1:
            raise NotImplementedError(
                f"{runtime_config.mode} UMBP does not support pipeline parallelism"
            )
        runtime = UMBPRuntimeFactory.build(runtime_config)
        self._runtime = runtime
        self._topology = topology
        self._store_submitted = False
        self._namespace: UMBPNamespace | None = None
        self.connector_scheduler: UMBPStoreConnectorScheduler | None = None
        self.connector_worker: UMBPStoreConnectorWorker | None = None
        if role == KVConnectorRole.SCHEDULER:
            self.connector_scheduler = UMBPStoreConnectorScheduler(
                vllm_config, kv_cache_config, None, None, topology
            )
            namespace = UMBPNamespace.configured(vllm_config)
            if namespace is not None:
                self._bind_scheduler_namespace(namespace)
        else:
            self._namespace = UMBPNamespace.from_vllm_config(
                vllm_config, kv_cache_config
            )
            layout = KVLayoutPlanner.from_kv_cache_config(kv_cache_config)
            self.connector_worker = UMBPStoreConnectorWorker(
                runtime.create_worker_handle(
                    self._namespace.value, topology, layout.describe(topology)
                ),
                layout,
                codec=self._codec(self._namespace),
            )

    def _codec(self, namespace: UMBPNamespace) -> BlockIdentityCodec:
        topology = self._topology
        return BlockIdentityCodec(
            namespace=namespace,
            tp_rank=topology.tp_rank,
            pp_rank=topology.pp_rank,
            pcp_rank=topology.pcp_rank,
            dcp_rank=topology.dcp_rank,
        )

    def _bind_scheduler_namespace(self, namespace: UMBPNamespace) -> None:
        assert self.connector_scheduler is not None
        self._namespace = namespace
        # Only workers see per-layer specs, so only they plan byte ranges.
        layout = KVLayoutDescriptor(regions=(), topology=self._topology)
        self.connector_scheduler.bind_namespace(
            self._runtime.create_scheduler_handle(
                namespace.value, self._topology, layout
            ),
            self._codec(namespace),
        )

    def get_handshake_metadata(self) -> UMBPHandshakeMetadata | None:
        if self.connector_worker is None or self._namespace is None:
            return None
        return UMBPHandshakeMetadata(self._namespace.value)

    def set_xfer_handshake_metadata(
        self, metadata: dict[int, KVConnectorHandshakeMetadata]
    ) -> None:
        if self.connector_scheduler is None or self._namespace is not None:
            return
        # Reports are keyed by worker rank, but ranks may share a key (PCP
        # without DCP), so require agreement rather than one per rank.
        namespaces = {
            report.namespace
            for report in metadata.values()
            if isinstance(report, UMBPHandshakeMetadata)
        }
        if len(namespaces) != 1:
            raise ValueError(
                "UMBP workers must report exactly one namespace, got "
                f"{sorted(namespaces)}"
            )
        self._bind_scheduler_namespace(UMBPNamespace(namespaces.pop()))

    def get_num_new_matched_tokens(
        self, request: Request, num_computed_tokens: int
    ) -> tuple[int | None, bool]:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.get_num_new_matched_tokens(
            request, num_computed_tokens
        )

    def update_state_after_alloc(
        self,
        request: Request,
        blocks: KVCacheBlocks,
        num_external_tokens: int,
    ) -> None:
        assert self.connector_scheduler is not None
        self.connector_scheduler.update_state_after_alloc(
            request, blocks, num_external_tokens
        )

    def bind_gpu_block_pool(self, gpu_block_pool: Any) -> None:
        assert self.connector_scheduler is not None
        self.connector_scheduler.bind_gpu_block_pool(gpu_block_pool)

    def build_connector_meta(
        self, scheduler_output: SchedulerOutput
    ) -> KVConnectorMetadata:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.build_connector_meta(scheduler_output)

    def request_finished(
        self, request: Request, block_ids: list[int]
    ) -> tuple[bool, dict[str, Any] | None]:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.request_finished(request, (block_ids,))

    def update_connector_output(self, connector_output: Any) -> None:
        assert self.connector_scheduler is not None
        self.connector_scheduler.update_connector_output(connector_output)

    def has_pending_push_work(self) -> bool:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.has_pending_push_work()

    def reset_cache(self) -> bool | None:
        if self.connector_scheduler is None:
            return None
        return self.connector_scheduler.reset_store()

    def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]) -> None:
        assert self.connector_worker is not None
        self.connector_worker.register_kv_caches(kv_caches)

    def bind_connector_metadata(self, connector_metadata: KVConnectorMetadata) -> None:
        super().bind_connector_metadata(connector_metadata)
        self._store_submitted = False

    def clear_connector_metadata(self) -> None:
        # No-forward steps skip the model runner's wait_for_save hook.
        if self.connector_worker is not None and self.has_connector_metadata():
            self.wait_for_save()
        super().clear_connector_metadata()

    def handle_preemptions(self, kv_connector_metadata: KVConnectorMetadata) -> None:
        assert self.connector_worker is not None
        assert isinstance(kv_connector_metadata, UMBPConnectorMetadata)
        self.connector_worker.handle_preemptions(kv_connector_metadata)

    def start_load_kv(self, forward_context: ForwardContext, **kwargs: Any) -> None:
        del kwargs
        assert self.connector_worker is not None
        metadata = self._get_connector_metadata()
        assert isinstance(metadata, UMBPConnectorMetadata)
        self.connector_worker.start_load_kv(forward_context, metadata)

    def wait_for_layer_load(self, layer_name: str) -> None:
        assert self.connector_worker is not None
        self.connector_worker.wait_for_layer_load(layer_name)

    def save_kv_layer(
        self,
        layer_name: str,
        kv_layer: torch.Tensor,
        attn_metadata: AttentionMetadata,
        **kwargs: Any,
    ) -> None:
        # Stores are submitted once per step, from wait_for_save.
        return

    def wait_for_save(self) -> None:
        if self._store_submitted:
            return
        assert self.connector_worker is not None
        metadata = self._get_connector_metadata()
        assert isinstance(metadata, UMBPConnectorMetadata)
        self.connector_worker.enqueue_stores(metadata)
        self.connector_worker.wait_for_save()
        self._store_submitted = True

    def get_finished(
        self, finished_req_ids: set[str]
    ) -> tuple[set[str] | None, set[str] | None]:
        assert self.connector_worker is not None
        return self.connector_worker.get_finished(finished_req_ids)

    def get_transfer_results(
        self, finished_req_ids: set[str]
    ) -> KVConnectorTransferResults:
        assert self.connector_worker is not None
        finished_sending, finished_recving = self.connector_worker.get_finished(
            finished_req_ids
        )
        return KVConnectorTransferResults(
            finished_sending=set(finished_sending or ()),
            finished_recving=set(finished_recving or ()),
        )

    def get_block_ids_with_load_errors(self) -> set[int]:
        assert self.connector_worker is not None
        return self.connector_worker.get_block_ids_with_load_errors()

    def build_connector_worker_meta(self) -> UMBPConnectorWorkerMetadata:
        assert self.connector_worker is not None
        return self.connector_worker.build_connector_worker_meta()

    def shutdown(self) -> None:
        if self.connector_scheduler is not None:
            self.connector_scheduler.close()
        if self.connector_worker is not None:
            self.connector_worker.close()