# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from typing import Any

import torch

from vllm.config import VllmConfig
from vllm.distributed.kv_events import KVCacheEvent
from vllm.distributed.kv_transfer.kv_connector.v1 import (
    KVConnectorBase_V1,
    KVConnectorRole,
    SupportsHMA,
)
from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
    KVConnectorPromMetrics,
    KVConnectorStats,
    PromMetric,
    PromMetricT,
)
from vllm.distributed.kv_transfer.kv_connector.v1.offloading.common import (
    OffloadingConnectorMetadata,
    OffloadingWorkerMetadata,
)
from vllm.distributed.kv_transfer.kv_connector.v1.offloading.metrics import (
    OffloadingConnectorStats,
    OffloadPromMetrics,
)
from vllm.distributed.kv_transfer.kv_connector.v1.offloading.scheduler import (
    OffloadingConnectorScheduler,
)
from vllm.distributed.kv_transfer.kv_connector.v1.offloading.worker import (
    OffloadingConnectorWorker,
)
from vllm.forward_context import ForwardContext
from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.kv_offload.factory import OffloadingSpecFactory
from vllm.v1.outputs import KVConnectorOutput
from vllm.v1.request import Request


class OffloadingConnector(KVConnectorBase_V1, SupportsHMA):
    @property
    def prefer_cross_layer_blocks(self) -> bool:
        return True

    def __init__(
        self,
        vllm_config: VllmConfig,
        role: KVConnectorRole,
        kv_cache_config: KVCacheConfig,
    ):
        super().__init__(vllm_config, role, kv_cache_config)

        spec = OffloadingSpecFactory.create_spec(vllm_config, kv_cache_config)

        self.connector_scheduler: OffloadingConnectorScheduler | None = None
        self.connector_worker: OffloadingConnectorWorker | None = None
        if role == KVConnectorRole.SCHEDULER:
            self.connector_scheduler = OffloadingConnectorScheduler(spec)
        elif role == KVConnectorRole.WORKER:
            self.connector_worker = OffloadingConnectorWorker(spec)

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

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

    def register_cross_layers_kv_cache(
        self, kv_cache: torch.Tensor, attn_backend: type[AttentionBackend]
    ):
        assert self.connector_worker is not None
        self.connector_worker.register_cross_layers_kv_cache(kv_cache, attn_backend)

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

    def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None:
        assert self.connector_worker is not None
        assert isinstance(self._connector_metadata, OffloadingConnectorMetadata)
        self.connector_worker.start_kv_transfers(self._connector_metadata)

    def wait_for_layer_load(self, layer_name: str) -> None:
        pass

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

    def wait_for_save(self):
        # Store deferral is handled in get_finished(), which always runs even
        # when wait_for_save() is skipped (e.g. kv_connector_no_forward).
        pass

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

        # Defer store jobs to the next step's start_kv_transfers. Done here
        # (rather than wait_for_save) so stores are queued even on steps where
        # wait_for_save is skipped.
        self.connector_worker.prepare_store_kv(self._connector_metadata)

        return self.connector_worker.get_finished(finished_req_ids)

    def build_connector_worker_meta(self) -> OffloadingWorkerMetadata | None:
        if self.connector_worker is not None:
            return self.connector_worker.build_connector_worker_meta()
        return None

    def on_new_request(self, request: "Request") -> None:
        assert self.connector_scheduler is not None
        self.connector_scheduler.on_new_request(request)

    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
    ):
        assert self.connector_scheduler is not None
        return self.connector_scheduler.update_state_after_alloc(
            request, blocks, num_external_tokens
        )

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

    def update_connector_output(self, connector_output: KVConnectorOutput):
        assert self.connector_scheduler is not None
        self.connector_scheduler.update_connector_output(connector_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)

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

    def take_events(self) -> Iterable[KVCacheEvent]:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.take_events()

    @classmethod
    def get_required_kvcache_layout(cls, vllm_config: VllmConfig) -> str | None:
        return "HND"

    def reset_cache(self) -> bool | None:
        assert self.connector_scheduler is not None
        self.connector_scheduler.reset_cache()
        return True

    def get_kv_connector_stats(self) -> KVConnectorStats | None:
        if self.connector_scheduler is not None:
            return self.connector_scheduler.get_stats()
        return None

    @classmethod
    def build_kv_connector_stats(
        cls, data: dict[str, Any] | None = None
    ) -> KVConnectorStats | None:
        return (
            OffloadingConnectorStats(data=data)
            if data is not None
            else OffloadingConnectorStats()
        )

    @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[object]],
    ) -> KVConnectorPromMetrics:
        return OffloadPromMetrics(
            vllm_config, metric_types, labelnames, per_engine_labelvalues
        )
