# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
KVConnectorBase_V1 Class for Distributed KV Cache & Hidden State
communication in vLLM v1

The class provides the following primitives:
    Scheduler-side: runs in the scheduler, binds metadata, which
    is used by the worker-side to load/save KV cache.
        get_num_new_matched_tokens() - get number of new tokens
            that exist in the remote KV cache. Might be called multiple
            times for a given request and should be side-effect free.
        update_state_after_alloc() - update KVConnector state after
            temporary buffer alloc by the CacheManager.
        update_connector_output() - update KVConnector state after
            output is received from worker-side connectors.
        request_finished() - called once when a request is finished,
            with the computed kv cache blocks for the request.
            Returns whether KV cache should be freed now or if the
            connector now assumes responsibility for freeing the
            the blocks asynchronously. Also optionally returns KV
            transfer params.
        take_events() - returns new KV events that were collected
            by the connector since the last call.

    Worker-side: runs in each worker, loads/saves KV cache to/from
    the Connector based on the metadata.
        handle_preemptions() - called if there are preempted requests,
            before their blocks are overwritten

        start_load_kv() - starts loading all KVs (maybe async)
        wait_for_layer_load() - blocks until layer i load is done

        save_kv_layer() - starts saving KV for layer i (maybe async)
        wait_for_save() - blocks until all saves are done

        get_finished() - called with ids of finished requests, returns
            ids of requests that have completed async sending/recving.
"""

import enum
from abc import ABC, abstractmethod
from collections.abc import Callable, Iterable
from typing import TYPE_CHECKING, Any, Literal

import torch

from vllm.logger import init_logger
from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.outputs import KVConnectorOutput

if TYPE_CHECKING:
    from vllm.config import VllmConfig
    from vllm.distributed.kv_events import KVCacheEvent, KVConnectorKVEvents
    from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
        KVConnectorPromMetrics,
        KVConnectorStats,
        PromMetric,
        PromMetricT,
    )
    from vllm.forward_context import ForwardContext
    from vllm.v1.core.kv_cache_manager import KVCacheBlocks
    from vllm.v1.kv_cache_interface import KVCacheConfig
    from vllm.v1.request import Request

# s_tensor_list, d_tensor_list, s_indices, d_indices, direction
CopyBlocksOp = Callable[
    [
        dict[str, torch.Tensor],
        dict[str, torch.Tensor],
        list[int],
        list[int],
        Literal["h2d", "d2h"],
    ],
    None,
]

logger = init_logger(__name__)


class SupportsHMA(ABC):
    """
    The class that indicates the corresponding connector supports hybrid memory
    allocator (HMA).
    This is required to use the connector together with hybrid memory allocator.
    """

    @abstractmethod
    def request_finished_all_groups(
        self,
        request: "Request",
        block_ids: tuple[list[int], ...],
    ) -> tuple[bool, dict[str, Any] | None]:
        """
        Called exactly once when a request has finished for all kv cache groups,
        before its blocks are freed for each group.

        NOTE(Kuntai): This function is only supported by connectors that support HMA.

        The connector may assumes responsibility for freeing the blocks
        asynchronously by returning True.

        Returns:
            True if the request is being saved/sent asynchronously and blocks
            should not be freed until the request_id is returned from
            get_finished().
            Optional KVTransferParams to be included in the request outputs
            returned by the engine.
        """
        raise NotImplementedError


def supports_hma(connector: Any) -> bool:
    if isinstance(connector, type):
        return issubclass(connector, SupportsHMA)
    else:
        return isinstance(connector, SupportsHMA)


class KVConnectorRole(enum.Enum):
    # Connector running in the scheduler process
    SCHEDULER = 0

    # Connector running in the worker process
    WORKER = 1


class KVConnectorHandshakeMetadata(ABC):  # noqa: B024
    """
    Metadata used for out of band connector handshake between
    P/D workers. This needs to serializable.
    """

    pass


class KVConnectorMetadata(ABC):  # noqa: B024
    """
    Abstract Metadata used to communicate between the
    Scheduler KVConnector and Worker KVConnector.
    """

    pass


class KVConnectorBase_V1(ABC):
    """
    Base class for KV connectors.
    """

    @property
    def prefer_cross_layer_blocks(self) -> bool:
        """
        Indicates whether this connector prefers KV blocks that hold KV data for all
        layers, which can speed up KV data transfers. Defaults to False.
        """
        return False

    def __init__(
        self,
        vllm_config: "VllmConfig",
        role: KVConnectorRole,
        kv_cache_config: "KVCacheConfig | None" = None,
    ):
        logger.warning(
            "Initializing KVConnectorBase_V1. This API is experimental and "
            "subject to change in the future as we iterate the design."
        )
        self._connector_metadata: KVConnectorMetadata | None = None
        self._vllm_config = vllm_config
        if vllm_config.kv_transfer_config is not None:
            self._kv_transfer_config = vllm_config.kv_transfer_config
        else:
            raise ValueError("kv_transfer_config must be set for KVConnectorBase_V1")
        self._kv_cache_config = kv_cache_config
        if self._kv_cache_config is None:
            logger.warning(
                "KVConnectorBase_V1 initialized without kv_cache_config. "
                "This is deprecated - please update your connector to accept "
                "kv_cache_config as the third constructor argument and pass it "
                "to super().__init__()."
            )
        self._role = role

    @property
    def role(self) -> KVConnectorRole:
        return self._role

    # ==============================
    # Worker-side methods
    # ==============================

    def bind_connector_metadata(self, connector_metadata: KVConnectorMetadata) -> None:
        """Set the connector metadata from the scheduler.

        This function should be called by the model runner every time
        before the model execution. The metadata will be used for runtime
        KV cache loading and saving.

        Args:
            connector_metadata (dict): the connector metadata.
        """
        self._connector_metadata = connector_metadata

    def clear_connector_metadata(self) -> None:
        """Clear the connector metadata.

        This function should be called by the model runner every time
        after the model execution.
        """
        self._connector_metadata = None

    def _get_connector_metadata(self) -> KVConnectorMetadata:
        """Get the connector metadata.

        This function should only be called inside the connector.

        Returns:
            ConnectorMetadata: the connector metadata.
        """
        # Should only be called while set to valid metadata.
        assert self._connector_metadata is not None
        return self._connector_metadata

    def has_connector_metadata(self) -> bool:
        """Check whether the connector metadata is currently set.

        Returns:
            bool: True if connector metadata exists, False otherwise.
        """
        return self._connector_metadata is not None

    def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
        """
        Initialize with the KV caches. Useful for pre-registering the
        KV Caches in the KVConnector (e.g. for NIXL).

        Args:
            kv_caches: dictionary of layer names, kv cache
        """
        return

    def register_cross_layers_kv_cache(
        self, kv_cache: torch.Tensor, attn_backend: type["AttentionBackend"]
    ):
        """
        Initialize with a single KV cache tensor used by all layers.
        The first dimension should be num_layers.
        This function will only be called for models with uniform layers,
        and only if the prefers_cross_layer_blocks is set to True.
        Only one of the functions
        {register_kv_caches, register_cross_layers_kv_cache} will be called.

        Args:
            kv_cache: a cross-layers kv cache tensor
            attn_backend: The attention backend that corresponds to all layers
        """
        return

    def set_host_xfer_buffer_ops(self, copy_operation: CopyBlocksOp):
        """
        Set the xPU-specific ops for copying KV between host and device.
        Needed when host buffer is used for kv transfer (e.g., in NixlConnector)
        """
        return

    def handle_preemptions(self, preempted_req_ids: set[str]):
        """
        Handle preempted requests BEFORE their blocks are overwritten.
        Needed for connectors which use async saves (e.g., OffloadingConnector)
        """
        return

    @abstractmethod
    def start_load_kv(self, forward_context: "ForwardContext", **kwargs: Any) -> None:
        """
        Start loading the KV cache from the connector to vLLM's paged
        KV buffer. This is called from the forward context before the
        forward pass to enable async loading during model execution.

        Args:
            forward_context (ForwardContext): the forward context.
            **kwargs: additional arguments for the load operation

        Note:
            The number of elements in kv_caches and layer_names should be
            the same.

        """
        pass

    @abstractmethod
    def wait_for_layer_load(self, layer_name: str) -> None:
        """
        Block until the KV for a specific layer is loaded into vLLM's
        paged buffer. This is called from within attention layer to ensure
        async copying from start_load_kv is complete.

        This interface will be useful for layer-by-layer pipelining.

        Args:
            layer_name: the name of that layer
        """
        pass

    @abstractmethod
    def save_kv_layer(
        self,
        layer_name: str,
        kv_layer: torch.Tensor,
        attn_metadata: "AttentionMetadata",
        **kwargs: Any,
    ) -> None:
        """
        Start saving a layer of KV cache from vLLM's paged buffer
        to the connector. This is called from within attention layer to
        enable async copying during execution.

        Args:
            layer_name (str): the name of the layer.
            kv_layer (torch.Tensor): the paged KV buffer of the current
                layer in vLLM.
            attn_metadata (AttentionMetadata): the attention metadata.
            **kwargs: additional arguments for the save operation.
        """
        pass

    @abstractmethod
    def wait_for_save(self):
        """
        Block until all the save operations is done. This is called
        as the forward context exits to ensure that the async saving
        from save_kv_layer is complete before finishing the forward.

        This prevents overwrites of paged KV buffer before saving done.
        """
        pass

    def get_finished(
        self, finished_req_ids: set[str]
    ) -> tuple[set[str] | None, set[str] | None]:
        """
        Notifies worker-side connector ids of requests that have
        finished generating tokens on the worker.
        The scheduler process (via the Executors) will use this output
        to track which workers are done.

        Returns:
            ids of requests that have finished asynchronous transfer
            (requests that previously returned True from request_finished()),
            tuple of (sending/saving ids, recving/loading ids).
            The finished saves/sends req ids must belong to a set provided in a
            call to this method (this call or a prior one).
        """
        return None, None

    def get_block_ids_with_load_errors(self) -> set[int]:
        """
        Get the set of block IDs that failed to load.

        Returns:
            Set of block IDs that encountered load errors.
            Empty set if no load errors occurred.

        Notes:
            - Applies to both sync- and async-loading requests.
            - Async loading: failed blocks may be reported in any forward pass
              up to and including the pass where the request ID is returned by
              `get_finished()`. Even if failures occur, the request must still
              be reported via `get_finished()`, and the failed block IDs must
              appear here no later than that same pass.
            - Sync loading: failed blocks should be reported in the forward
              pass in which they are detected.
        """
        return set()

    def shutdown(self):
        """
        Shutdown the connector. This is called when the worker process
        is shutting down to ensure that all the async operations are
        completed and the connector is cleaned up properly.
        """
        return None

    def get_kv_connector_stats(self) -> "KVConnectorStats | None":
        """
        Get the KV connector stats collected during the last interval.
        """
        return None

    def get_kv_connector_kv_cache_events(self) -> "KVConnectorKVEvents | None":
        """
        Get the KV connector kv cache events collected during the last interval.
        This function should be called by the model runner every time after the
        model execution and before cleanup.
        """
        return None

    def get_handshake_metadata(self) -> KVConnectorHandshakeMetadata | None:
        """
        Get the KVConnector handshake metadata for this connector.
        This metadata is used for out-of-band connector handshake
        between P/D workers.

        Returns:
            KVConnectorHandshakeMetadata: the handshake metadata.
            None if no handshake metadata is available.
        """
        return None

    # ==============================
    # Scheduler-side methods
    # ==============================

    @abstractmethod
    def get_num_new_matched_tokens(
        self,
        request: "Request",
        num_computed_tokens: int,
    ) -> tuple[int | None, bool]:
        """
        Get number of new tokens that can be loaded from the
        external KV cache beyond the num_computed_tokens.

        Args:
            request (Request): the request object.
            num_computed_tokens (int): the number of locally
                computed tokens for this request

        Returns:
            A tuple with the following elements:
                - An optional number of tokens that can be loaded from the
                  external KV cache beyond what is already computed.
                  If None, it means that the connector needs more time to
                  determine the number of matched tokens, and the scheduler
                  should query for this request again later.
                - `True` if external KV cache tokens will be loaded
                  asynchronously (between scheduler steps). Must be
                  'False' if the first element is 0.

        Notes:
            The connector should only consider the largest prefix of prompt-
            tokens for which KV cache is actually available at the time of the
            call. If the cache cannot be loaded for some tokens (e.g., due to
            connectivity issues or eviction), those tokens must not be taken
            into account.
        """
        pass

    @abstractmethod
    def update_state_after_alloc(
        self, request: "Request", blocks: "KVCacheBlocks", num_external_tokens: int
    ):
        """
        Update KVConnector state after block allocation.

        If get_num_new_matched_tokens previously returned True for a
        request, this function may be called twice for that same request -
        first when blocks are allocated for the connector tokens to be
        asynchronously loaded into, and second when any additional blocks
        are allocated, after the load/transfer is complete.

        Args:
            request (Request): the request object.
            blocks (KVCacheBlocks): the blocks allocated for the request.
            num_external_tokens (int): the number of tokens that will be
                loaded from the external KV cache.
        """
        pass

    @abstractmethod
    def build_connector_meta(
        self, scheduler_output: SchedulerOutput
    ) -> KVConnectorMetadata:
        """
        Build the connector metadata for this step.

        This function should NOT modify fields in the scheduler_output.
        Also, calling this function will reset the state of the connector.

        Args:
            scheduler_output (SchedulerOutput): the scheduler output object.
        """
        pass

    def update_connector_output(self, connector_output: KVConnectorOutput):
        """
        Update KVConnector state from worker-side connectors output.

        Args:
            connector_output (KVConnectorOutput): the worker-side
                connectors output.
        """
        return

    def request_finished(
        self,
        request: "Request",
        block_ids: list[int],
    ) -> tuple[bool, dict[str, Any] | None]:
        """
        Called exactly once when a request has finished, before its blocks are
        freed.

        The connector may assumes responsibility for freeing the blocks
        asynchronously by returning True.

        Returns:
            True if the request is being saved/sent asynchronously and blocks
            should not be freed until the request_id is returned from
            get_finished().
            Optional KVTransferParams to be included in the request outputs
            returned by the engine.
        """
        return False, None

    def take_events(self) -> Iterable["KVCacheEvent"]:
        """
        Take the KV cache events from the connector.

        Yields:
            New KV cache events since the last call.
        """
        return ()

    @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.
        """

        if cls is KVConnectorBase_V1:
            raise TypeError(
                "get_required_kvcache_layout should not be called "
                "on the abstract base class"
            )
        return None

    @classmethod
    def requires_piecewise_for_cudagraph(cls, extra_config: dict[str, Any]) -> bool:
        """
        Check if this connector requires PIECEWISE CUDA graph mode.

        Connectors that use asynchronous layer-by-layer operations
        (wait_for_layer_load/save_kv_layer) should override this method
        to return True when those operations are enabled. These operations
        cannot be captured in CUDA graphs and will be skipped during replay,
        causing data races. PIECEWISE mode allows Python code to execute
        between graph pieces, ensuring proper synchronization.

        Args:
            extra_config: The kv_connector_extra_config dict from
                KVTransferConfig.

        Returns:
            True if this connector requires PIECEWISE CUDA graph mode,
            False otherwise.
        """
        return False

    def get_finished_count(self) -> int | None:
        """
        Get the count of requests expected to complete send/receive operations
        via this connector. This method is used to initialize the
        KVOutputAggregator, overwriting the default world_size.

        Returns:
            int: expected sending or receiving completion count.
        """

        return None

    @classmethod
    def build_kv_connector_stats(
        cls, data: dict[str, Any] | None = None
    ) -> "KVConnectorStats | None":
        """
        KVConnectorStats resolution method. This method allows dynamically
        registered connectors to return their own KVConnectorStats object,
        which can implement custom aggregation logic on the data dict.
        """
        return None

    def set_xfer_handshake_metadata(
        self, metadata: dict[int, KVConnectorHandshakeMetadata]
    ) -> None:
        """
        Set the KV connector handshake metadata for this connector.

        Args:
            metadata (KVConnectorHandshakeMetadata): the handshake metadata to set.
        """
        return None

    @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 | None":
        """
        Create a KVConnectorPromMetrics subclass which should register
        per-connector Prometheus metrics and implement observe() to
        expose connector transfer stats via Prometheus.
        """
        return None

    def reset_cache(self) -> bool | None:
        """
        Reset the connector's internal cache.

        Returns:
            bool: True if the cache was successfully reset, False otherwise.
        """
        logger.debug(
            "Connector cache reset requested, but %s does not implement reset_cache().",
            type(self).__name__,
        )

        return None
