from __future__ import annotations

import itertools
import time
from collections import defaultdict
from collections.abc import Iterable
from typing import Any, Optional, Union

from vllm.config import VllmConfig
from vllm.distributed.kv_events import EventPublisherFactory, KVEventBatch
from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
from vllm.distributed.kv_transfer.kv_connector.v1 import (
    KVConnectorBase_V1,
    KVConnectorRole,
)
from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
from vllm.distributed.kv_transfer.kv_connector.v1.multi_connector import MultiConnector
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.v1.core.encoder_cache_manager import (
    EncoderCacheManager,
    compute_encoder_budget,
)
from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager
from vllm.v1.core.sched.interface import SchedulerInterface
from vllm.v1.core.sched.output import CachedRequestData, NewRequestData, SchedulerOutput
from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
from vllm.v1.core.sched.utils import check_stop, remove_all
from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.metrics.stats import SchedulerStats
from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus
from vllm.v1.spec_decode.metrics import SpecDecodingStats
from vllm.v1.structured_output import StructuredOutputManager

from ucm.sparse.base import INVALID_SLOT, UcmSparseRole
from ucm.sparse.state import ensure_ucm_sparse_initialized, get_ucm_sparse
from ucm.utils import Config

logger = init_logger(__name__)


class Scheduler(SchedulerInterface):

    def __init__(
        self,
        vllm_config: VllmConfig,
        kv_cache_config: KVCacheConfig,
        structured_output_manager: StructuredOutputManager,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        include_finished_set: bool = False,
        log_stats: bool = False,
    ) -> None:
        self.vllm_config = vllm_config
        self.scheduler_config = vllm_config.scheduler_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.kv_cache_config = kv_cache_config
        self.kv_events_config = vllm_config.kv_events_config
        self.parallel_config = vllm_config.parallel_config
        self.log_stats = log_stats
        self.structured_output_manager = structured_output_manager
        self.is_encoder_decoder = vllm_config.model_config.is_encoder_decoder

        # include_finished_set controls whether a separate set of finished
        # request ids should be included in the EngineCoreOutputs returned
        # by update_from_outputs(). This is currently used in the multi-engine
        # case to track request lifetimes efficiently.
        self.finished_req_ids_dict: Optional[dict[int, set[str]]] = (
            defaultdict(set) if include_finished_set else None
        )

        # Scheduling constraints.
        self.max_num_running_reqs = self.scheduler_config.max_num_seqs
        self.max_num_scheduled_tokens = self.scheduler_config.max_num_batched_tokens
        self.max_model_len = self.scheduler_config.max_model_len
        self.enable_kv_cache_events = (
            self.kv_events_config is not None
            and self.kv_events_config.enable_kv_cache_events
        )

        # Create KVConnector for the Scheduler. Note that each Worker
        # will have a corresponding KVConnector with Role=WORKER.
        # KV Connector pushes/pull of remote KVs for P/D and offloading.
        self.connector = None
        self.ucm_sparse = None
        if self.vllm_config.kv_transfer_config is not None:
            assert len(self.kv_cache_config.kv_cache_groups) == 1, (
                "Multiple KV cache groups are not currently supported "
                "with KV connectors"
            )
            assert not self.is_encoder_decoder, (
                "Encoder-decoder models are not currently supported "
                "with KV connectors"
            )
            self.connector = KVConnectorFactory.create_connector(
                config=self.vllm_config, role=KVConnectorRole.SCHEDULER
            )

            # Initialize UCM Sparse if available
            ucm_config = Config(self.vllm_config.kv_transfer_config)
            ucm_sparse_config = ucm_config.get_config().get("ucm_sparse_config")
            if ucm_sparse_config:
                ensure_ucm_sparse_initialized(vllm_config, role=UcmSparseRole.SCHEDULER)
                self.ucm_sparse = get_ucm_sparse()
                logger.info(
                    "UCM Sparse initialized successfully: {}".format(self.ucm_sparse)
                )

        self.kv_event_publisher = EventPublisherFactory.create(
            self.kv_events_config,
            self.parallel_config.data_parallel_rank,
        )

        num_gpu_blocks = self.cache_config.num_gpu_blocks
        assert num_gpu_blocks is not None and num_gpu_blocks > 0

        self.block_size = self.cache_config.block_size

        self.dcp_world_size = vllm_config.parallel_config.decode_context_parallel_size
        # Note(hc): The scheduler’s block_size must be multiplied
        # by dcp_world_size, since block hashes are computed on the
        # original full token sequence at a granularity of
        # original_block_size × dcp_world_size.
        if self.dcp_world_size > 1:
            self.block_size *= self.dcp_world_size

        # req_id -> Request
        self.requests: dict[str, Request] = {}
        # Scheduling policy
        if self.scheduler_config.policy == "priority":
            self.policy = SchedulingPolicy.PRIORITY
        elif self.scheduler_config.policy == "fcfs":
            self.policy = SchedulingPolicy.FCFS
        else:
            raise ValueError(
                f"Unknown scheduling policy: {self.scheduler_config.policy}"
            )
        # Priority queues for requests.
        self.waiting = create_request_queue(self.policy)
        self.running: list[Request] = []

        # The request IDs that are finished in between the previous and the
        # current steps. This is used to notify the workers about the finished
        # requests so that they can free the cached states for those requests.
        # This is flushed at the end of each scheduling step.
        self.finished_req_ids: set[str] = set()

        # KV Connector: requests in process of async KV loading or recving
        self.finished_recving_kv_req_ids: set[str] = set()

        # Encoder-related.
        # Calculate encoder cache size if applicable
        # NOTE: For now we use the same budget for both compute and space.
        # This can be changed when we make encoder cache for embedding caching
        # across requests.
        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=vllm_config.model_config,
            scheduler_config=vllm_config.scheduler_config,
            mm_registry=mm_registry,
        )

        # NOTE(woosuk): Here, "encoder" includes the vision encoder (and
        # projector if needed) for MM models as well as encoder-decoder
        # transformers.
        self.max_num_encoder_input_tokens = encoder_compute_budget
        # NOTE: For the models without encoder (e.g., text-only models),
        # the encoder cache will not be initialized because cache size is 0
        # for these models.
        self.encoder_cache_manager = EncoderCacheManager(cache_size=encoder_cache_size)

        speculative_config = vllm_config.speculative_config
        self.use_eagle = False
        self.num_spec_tokens = self.num_lookahead_tokens = 0
        if speculative_config:
            self.num_spec_tokens = speculative_config.num_speculative_tokens
            if speculative_config.use_eagle():
                self.use_eagle = True
                self.num_lookahead_tokens = self.num_spec_tokens

        # Create the KV cache manager.
        self.kv_cache_manager = KVCacheManager(
            kv_cache_config=kv_cache_config,
            max_model_len=self.max_model_len,
            enable_caching=self.cache_config.enable_prefix_caching,
            use_eagle=self.use_eagle,
            log_stats=self.log_stats,
            enable_kv_cache_events=self.enable_kv_cache_events,
            dcp_world_size=self.dcp_world_size,
        )
        self.use_pp = self.parallel_config.pipeline_parallel_size > 1

    def schedule(self) -> SchedulerOutput:
        # NOTE(woosuk) on the scheduling algorithm:
        # There's no "decoding phase" nor "prefill phase" in the scheduler.
        # Each request just has the num_computed_tokens and
        # num_tokens_with_spec. num_tokens_with_spec =
        # len(prompt_token_ids) + len(output_token_ids) + len(spec_token_ids).
        # At each step, the scheduler tries to assign tokens to the requests
        # so that each request's num_computed_tokens can catch up its
        # num_tokens_with_spec. This is general enough to cover
        # chunked prefills, prefix caching, speculative decoding,
        # and the "jump decoding" optimization in the future.

        scheduled_new_reqs: list[Request] = []
        scheduled_resumed_reqs: list[Request] = []
        scheduled_running_reqs: list[Request] = []
        preempted_reqs: list[Request] = []

        req_to_new_blocks: dict[str, KVCacheBlocks] = {}
        num_scheduled_tokens: dict[str, int] = {}
        token_budget = self.max_num_scheduled_tokens
        # Encoder-related.
        scheduled_encoder_inputs: dict[str, list[int]] = {}
        encoder_compute_budget = self.max_num_encoder_input_tokens
        # Spec decode-related.
        scheduled_spec_decode_tokens: dict[str, list[int]] = {}

        # For logging.
        scheduled_timestamp = time.monotonic()

        # First, schedule the RUNNING requests.
        req_index = 0
        req_sparsed_slots: dict[str, int] = {}
        while req_index < len(self.running) and token_budget > 0:
            request = self.running[req_index]

            num_slots_sparsed = INVALID_SLOT
            if self.ucm_sparse:
                num_slots_sparsed = self.ucm_sparse.estimate_num_slots_sparsed(request)
            req_sparsed_slots.update({request.request_id: num_slots_sparsed})

            num_new_tokens = (
                request.num_tokens_with_spec
                + request.num_output_placeholders
                - request.num_computed_tokens
            )
            if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens:
                num_new_tokens = self.scheduler_config.long_prefill_token_threshold
            num_new_tokens = min(num_new_tokens, token_budget)

            # Make sure the input position does not exceed the max model len.
            # This is necessary when using spec decoding.
            num_new_tokens = min(
                num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens
            )

            # Schedule encoder inputs.
            encoder_inputs_to_schedule = None
            new_encoder_compute_budget = encoder_compute_budget
            if request.has_encoder_inputs:
                (
                    encoder_inputs_to_schedule,
                    num_new_tokens,
                    new_encoder_compute_budget,
                ) = self._try_schedule_encoder_inputs(
                    request,
                    request.num_computed_tokens,
                    num_new_tokens,
                    encoder_compute_budget,
                )

            if num_new_tokens == 0:
                # The request cannot be scheduled because one of the following
                # reasons:
                # 1. No new tokens to schedule. This may happen when
                #    (1) PP>1 and we have already scheduled all prompt tokens
                #    but they are not finished yet.
                #    (2) Async scheduling and the request has reached to either
                #    its max_total_tokens or max_model_len.
                # 2. The encoder budget is exhausted.
                # 3. The encoder cache is exhausted.
                # NOTE(woosuk): Here, by doing `continue` instead of `break`,
                # we do not strictly follow the FCFS scheduling policy and
                # allow the lower-priority requests to be scheduled.
                req_index += 1
                continue

            while True:
                new_blocks = self.kv_cache_manager.allocate_slots(
                    request,
                    num_new_tokens,
                    num_lookahead_tokens=self.num_lookahead_tokens,
                    num_slots_sparsed=num_slots_sparsed,
                )
                if new_blocks is None:
                    # The request cannot be scheduled.
                    # Preempt the lowest-priority request.
                    if self.policy == SchedulingPolicy.PRIORITY:
                        preempted_req = max(
                            self.running,
                            key=lambda r: (r.priority, r.arrival_time),
                        )
                        self.running.remove(preempted_req)
                        if preempted_req in scheduled_running_reqs:
                            scheduled_running_reqs.remove(preempted_req)
                    else:
                        preempted_req = self.running.pop()

                    self.kv_cache_manager.free(preempted_req)
                    self.encoder_cache_manager.free(preempted_req)
                    preempted_req.status = RequestStatus.PREEMPTED
                    preempted_req.num_computed_tokens = 0
                    if self.log_stats:
                        preempted_req.record_event(
                            EngineCoreEventType.PREEMPTED, scheduled_timestamp
                        )

                    self.waiting.prepend_request(preempted_req)
                    preempted_reqs.append(preempted_req)
                    if preempted_req == request:
                        # No more request to preempt.
                        can_schedule = False
                        break
                else:
                    # The request can be scheduled.
                    can_schedule = True
                    break
            if not can_schedule:
                break
            assert new_blocks is not None

            # Schedule the request.
            scheduled_running_reqs.append(request)
            req_to_new_blocks[request.request_id] = new_blocks
            num_scheduled_tokens[request.request_id] = num_new_tokens
            token_budget -= num_new_tokens
            req_index += 1

            # Speculative decode related.
            if request.spec_token_ids:
                num_scheduled_spec_tokens = (
                    num_new_tokens + request.num_computed_tokens - request.num_tokens
                )
                if num_scheduled_spec_tokens > 0:
                    # Trim spec_token_ids list to num_scheduled_spec_tokens.
                    del request.spec_token_ids[num_scheduled_spec_tokens:]
                    scheduled_spec_decode_tokens[request.request_id] = (
                        request.spec_token_ids
                    )

            # Encoder-related.
            if encoder_inputs_to_schedule:
                scheduled_encoder_inputs[request.request_id] = (
                    encoder_inputs_to_schedule
                )
                # Allocate the encoder cache.
                for i in encoder_inputs_to_schedule:
                    self.encoder_cache_manager.allocate(request, i)
                encoder_compute_budget = new_encoder_compute_budget

        # Record the LoRAs in scheduled_running_reqs
        scheduled_loras: set[int] = set()
        if self.lora_config:
            scheduled_loras = set(
                req.lora_request.lora_int_id
                for req in scheduled_running_reqs
                if req.lora_request and req.lora_request.lora_int_id > 0
            )
            assert len(scheduled_loras) <= self.lora_config.max_loras

        # Use a temporary RequestQueue to collect requests that need to be
        # skipped and put back at the head of the waiting queue later
        skipped_waiting_requests = create_request_queue(self.policy)

        # Next, schedule the WAITING requests.
        num_external_computed_tokens_per_req: dict[str, int] = {}
        if not preempted_reqs:
            while self.waiting and token_budget > 0:
                if len(self.running) == self.max_num_running_reqs:
                    break

                request = self.waiting.peek_request()

                num_slots_sparsed = INVALID_SLOT
                if self.ucm_sparse:
                    num_slots_sparsed = self.ucm_sparse.estimate_num_slots_sparsed(
                        request
                    )
                req_sparsed_slots.update({request.request_id: num_slots_sparsed})

                # KVTransfer: skip request if still waiting for remote kvs.
                if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
                    is_ready = self._update_waiting_for_remote_kv(request)
                    if is_ready:
                        request.status = RequestStatus.WAITING
                    else:
                        logger.debug(
                            "%s is still in WAITING_FOR_REMOTE_KVS state.",
                            request.request_id,
                        )
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                # Skip request if the structured output request is still waiting
                # for FSM compilation.
                if request.status == RequestStatus.WAITING_FOR_FSM:
                    structured_output_req = request.structured_output_request
                    if structured_output_req and structured_output_req.grammar:
                        request.status = RequestStatus.WAITING
                    else:
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                # Check that adding the request still respects the max_loras
                # constraint.
                if (
                    self.lora_config
                    and request.lora_request
                    and (
                        len(scheduled_loras) == self.lora_config.max_loras
                        and request.lora_request.lora_int_id not in scheduled_loras
                    )
                ):
                    # Scheduling would exceed max_loras, skip.
                    self.waiting.pop_request()
                    skipped_waiting_requests.prepend_request(request)
                    continue

                num_external_computed_tokens = 0
                load_kv_async = False

                # Get already-cached tokens.
                if request.num_computed_tokens == 0:
                    # Get locally-cached tokens.
                    new_computed_blocks, num_new_local_computed_tokens = (
                        self.kv_cache_manager.get_computed_blocks(request)
                    )

                    # Get externally-cached tokens if using a KVConnector.
                    if self.connector is not None:
                        num_external_computed_tokens, load_kv_async = (
                            self.connector.get_num_new_matched_tokens(
                                request, num_new_local_computed_tokens
                            )
                        )

                        if num_external_computed_tokens is None:
                            # The request cannot be scheduled because
                            # the KVConnector couldn't determine
                            # the number of matched tokens.
                            self.waiting.pop_request()
                            skipped_waiting_requests.prepend_request(request)
                            continue

                    num_external_computed_tokens_per_req.update(
                        {request.request_id: num_external_computed_tokens}
                    )

                    # Total computed tokens (local + external).
                    num_computed_tokens = (
                        num_new_local_computed_tokens + num_external_computed_tokens
                    )
                # KVTransfer: WAITING reqs have num_computed_tokens > 0
                # after async KV recvs are completed.
                else:
                    new_computed_blocks = (
                        self.kv_cache_manager.create_empty_block_list()
                    )
                    num_new_local_computed_tokens = 0
                    num_computed_tokens = request.num_computed_tokens

                encoder_inputs_to_schedule = None
                new_encoder_compute_budget = encoder_compute_budget

                # KVTransfer: loading remote KV, do not allocate for new work.
                if load_kv_async:
                    assert num_external_computed_tokens > 0
                    num_new_tokens = 0
                # Number of tokens to be scheduled.
                else:
                    # We use `request.num_tokens` instead of
                    # `request.num_prompt_tokens` to consider the resumed
                    # requests, which have output tokens.
                    num_new_tokens = request.num_tokens - num_computed_tokens
                    if (
                        0
                        < self.scheduler_config.long_prefill_token_threshold
                        < num_new_tokens
                    ):
                        num_new_tokens = (
                            self.scheduler_config.long_prefill_token_threshold
                        )

                    # chunked prefill has to be enabled explicitly to allow
                    # pooling requests to be chunked
                    if (
                        not self.scheduler_config.chunked_prefill_enabled
                        and num_new_tokens > token_budget
                    ):
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                    num_new_tokens = min(num_new_tokens, token_budget)
                    assert num_new_tokens > 0

                    # Schedule encoder inputs.
                    if request.has_encoder_inputs:
                        (
                            encoder_inputs_to_schedule,
                            num_new_tokens,
                            new_encoder_compute_budget,
                        ) = self._try_schedule_encoder_inputs(
                            request,
                            num_computed_tokens,
                            num_new_tokens,
                            encoder_compute_budget,
                        )
                        if num_new_tokens == 0:
                            # The request cannot be scheduled.
                            break

                # Handles an edge case when P/D Disaggregation
                # is used with Spec Decoding where an
                # extra block gets allocated which
                # creates a mismatch between the number
                # of local and remote blocks.
                effective_lookahead_tokens = (
                    0 if request.num_computed_tokens == 0 else self.num_lookahead_tokens
                )

                # Determine if we need to allocate cross-attention blocks.
                if self.is_encoder_decoder and request.has_encoder_inputs:
                    # TODO(russellb): For Whisper, we know that the input is
                    # always padded to the maximum length. If we support other
                    # encoder-decoder models, this will need to be updated if we
                    # want to only allocate what is needed.
                    num_encoder_tokens = (
                        self.scheduler_config.max_num_encoder_input_tokens
                    )
                else:
                    num_encoder_tokens = 0

                new_blocks = self.kv_cache_manager.allocate_slots(
                    request,
                    num_new_tokens + num_external_computed_tokens,
                    num_new_local_computed_tokens,
                    new_computed_blocks,
                    num_lookahead_tokens=effective_lookahead_tokens,
                    delay_cache_blocks=load_kv_async,
                    num_encoder_tokens=num_encoder_tokens,
                    num_slots_sparsed=num_slots_sparsed,
                )

                if new_blocks is None:
                    # The request cannot be scheduled.
                    break

                # KVTransfer: the connector uses this info to determine
                # if a load is needed. Note that
                # This information is used to determine if a load is
                # needed for this request.
                if self.connector is not None:
                    self.connector.update_state_after_alloc(
                        request,
                        new_computed_blocks + new_blocks,
                        num_external_computed_tokens,
                    )

                # Request was already popped from self.waiting
                # unless it was re-added above due to new_blocks being None.
                request = self.waiting.pop_request()
                if load_kv_async:
                    # If loading async, allocate memory and put request
                    # into the WAITING_FOR_REMOTE_KV state.
                    skipped_waiting_requests.prepend_request(request)
                    request.status = RequestStatus.WAITING_FOR_REMOTE_KVS
                    continue

                req_index += 1
                self.running.append(request)
                if self.log_stats:
                    request.record_event(
                        EngineCoreEventType.SCHEDULED, scheduled_timestamp
                    )
                if request.status == RequestStatus.WAITING:
                    scheduled_new_reqs.append(request)
                elif request.status == RequestStatus.PREEMPTED:
                    scheduled_resumed_reqs.append(request)
                else:
                    raise RuntimeError(f"Invalid request status: {request.status}")

                if self.lora_config and request.lora_request:
                    scheduled_loras.add(request.lora_request.lora_int_id)
                req_to_new_blocks[request.request_id] = (
                    self.kv_cache_manager.get_blocks(request.request_id)
                )
                num_scheduled_tokens[request.request_id] = num_new_tokens
                token_budget -= num_new_tokens
                request.status = RequestStatus.RUNNING
                request.num_computed_tokens = num_computed_tokens
                # Count the number of prefix cached tokens.
                if request.num_cached_tokens < 0:
                    request.num_cached_tokens = num_computed_tokens
                # Encoder-related.
                if encoder_inputs_to_schedule:
                    scheduled_encoder_inputs[request.request_id] = (
                        encoder_inputs_to_schedule
                    )
                    # Allocate the encoder cache.
                    for i in encoder_inputs_to_schedule:
                        self.encoder_cache_manager.allocate(request, i)
                    encoder_compute_budget = new_encoder_compute_budget

        # Put back any skipped requests at the head of the waiting queue
        if skipped_waiting_requests:
            self.waiting.prepend_requests(skipped_waiting_requests)

        # Check if the scheduling constraints are satisfied.
        total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
        assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
        assert token_budget >= 0
        assert len(self.running) <= self.max_num_running_reqs
        # Since some requests in the RUNNING queue may not be scheduled in
        # this step, the total number of scheduled requests can be smaller than
        # len(self.running).
        assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(
            scheduled_running_reqs
        ) <= len(self.running)

        # Get the longest common prefix among all requests in the running queue.
        # This can be potentially used for cascade attention.
        num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
        if self.running:
            any_request = self.running[0]
            num_common_prefix_blocks = (
                self.kv_cache_manager.get_num_common_prefix_blocks(
                    any_request, len(self.running)
                )
            )

        # Construct the scheduler output.
        new_reqs_data = [
            NewRequestData.from_request(
                req, req_to_new_blocks[req.request_id].get_block_ids()
            )
            for req in scheduled_new_reqs
        ]
        cached_reqs_data = self._make_cached_request_data(
            scheduled_running_reqs,
            scheduled_resumed_reqs,
            num_scheduled_tokens,
            scheduled_spec_decode_tokens,
            req_to_new_blocks,
        )
        scheduled_requests = (
            scheduled_new_reqs + scheduled_running_reqs + scheduled_resumed_reqs
        )
        structured_output_request_ids, grammar_bitmask = self.get_grammar_bitmask(
            scheduled_requests, scheduled_spec_decode_tokens
        )
        scheduler_output = SchedulerOutput(
            scheduled_new_reqs=new_reqs_data,
            scheduled_cached_reqs=cached_reqs_data,
            num_scheduled_tokens=num_scheduled_tokens,
            total_num_scheduled_tokens=total_num_scheduled_tokens,
            scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
            scheduled_encoder_inputs=scheduled_encoder_inputs,
            num_common_prefix_blocks=num_common_prefix_blocks,
            req_sparsed_slots=req_sparsed_slots,
            num_external_computed_tokens_per_req=num_external_computed_tokens_per_req,
            # finished_req_ids is an existing state in the scheduler,
            # instead of being newly scheduled in this step.
            # It contains the request IDs that are finished in between
            # the previous and the current steps.
            finished_req_ids=self.finished_req_ids,
            free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
            structured_output_request_ids=structured_output_request_ids,
            grammar_bitmask=grammar_bitmask,
        )

        # NOTE(Kuntai): this function is designed for multiple purposes:
        # 1. Plan the KV cache store
        # 2. Wrap up all the KV cache load / save ops into an opaque object
        # 3. Clear the internal states of the connector
        if self.connector is not None:
            meta = self.connector.build_connector_meta(scheduler_output)
            scheduler_output.kv_connector_metadata = meta

        # collect KV cache events from KV cache manager
        events = self.kv_cache_manager.take_events()

        # collect KV cache events from connector
        if self.connector is not None:
            connector_events = self.connector.take_events()
            if connector_events:
                if events is None:
                    events = list(connector_events)
                else:
                    events.extend(connector_events)

        # publish collected KV cache events
        if events:
            batch = KVEventBatch(ts=time.time(), events=events)
            self.kv_event_publisher.publish(batch)

        self._update_after_schedule(scheduler_output)
        return scheduler_output

    def add_request(self, request: Request) -> None:
        self.waiting.add_request(request)
        self.requests[request.request_id] = request

        if self.ucm_sparse:
            self.ucm_sparse.request_begin(request.request_id, request.prompt_token_ids)

        if self.log_stats:
            request.record_event(EngineCoreEventType.QUEUED)

    def _free_request(self, request: Request) -> Optional[dict[str, Any]]:
        assert request.is_finished()

        if self.ucm_sparse:
            self.ucm_sparse.request_finished_in_scheduler(request.request_id)

        delay_free_blocks, kv_xfer_params = self._connector_finished(request)
        self.encoder_cache_manager.free(request)
        request_id = request.request_id
        self.finished_req_ids.add(request_id)
        if self.finished_req_ids_dict is not None:
            self.finished_req_ids_dict[request.client_index].add(request_id)

        if not delay_free_blocks:
            self._free_blocks(request)

        return kv_xfer_params
