# SPDX-License-Identifier: Apache-2.0

from __future__ import annotations

import time
from collections import deque
from collections.abc import Iterable
from typing import Optional, Union

from vllm.config import CacheConfig, LoRAConfig, ModelConfig, SchedulerConfig
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 KVCacheManager
from vllm.v1.core.sched.interface import SchedulerInterface
from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData,
                                       SchedulerOutput)
from vllm.v1.core.sched.utils import check_stop
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 ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus
from vllm.v1.spec_decode.metrics import SpecDecodingStats
from vllm.v1.structured_output import StructuredOutputManager

logger = init_logger(__name__)


class Scheduler(SchedulerInterface):

    def __init__(
        self,
        scheduler_config: SchedulerConfig,
        model_config: ModelConfig,
        cache_config: CacheConfig,
        lora_config: Optional[LoRAConfig],
        kv_cache_config: KVCacheConfig,
        structured_output_manager: StructuredOutputManager,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        include_finished_set: bool = False,
        log_stats: bool = False,
    ) -> None:
        self.scheduler_config = scheduler_config
        self.cache_config = cache_config
        self.lora_config = lora_config
        self.kv_cache_config = kv_cache_config
        self.log_stats = log_stats
        self.structured_output_manager = structured_output_manager

        # 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.include_finished_set = include_finished_set

        # 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

        # 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=cache_config.enable_prefix_caching,
            caching_hash_algo=self.cache_config.prefix_caching_hash_algo,
            log_stats=self.log_stats)
        self.block_size = self.cache_config.block_size

        # req_id -> Request
        self.requests: dict[str, Request] = {}
        # Priority queues for requests.
        self.waiting: deque[Request] = deque()
        self.running: list[Request] = []
        # The requests that have been scheduled and are being executed
        # by the executor.
        self.scheduled_req_ids: set[str] = set()

        # 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()

        # OPTIMIZATION: Cache the CachedRequestData objects to avoid creating
        # them at each scheduling step.
        # Request id -> CachedRequestData
        self._cached_reqs_data: dict[str, CachedRequestData] = {}

        # 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=model_config,
            scheduler_config=scheduler_config,
            mm_registry=mm_registry,
        )

        # NOTE(woosuk): Here, "encoder" includes the vision encoder (and
        # projector if needed). Currently, we assume that the encoder also
        # has the Transformer architecture (e.g., ViT).
        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)

    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] = []

        # NOTE: structured_output_request_ids maps
        # a request's (request that uses structured output)
        # request_id to the running request index.
        # This will helps us determine to slice the grammar bitmask
        # and only applies valid mask for requests that
        # uses structured decoding.
        structured_output_request_ids: dict[str, int] = {}

        req_to_new_block_ids: dict[str, list[int]] = {}
        num_scheduled_tokens: dict[str, int] = {}
        token_budget = self.max_num_scheduled_tokens
        # Encoder-related.
        scheduled_encoder_inputs: dict[str, list[int]] = {}
        encoder_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
        while req_index < len(self.running) and token_budget > 0:
            request = self.running[req_index]
            if request.request_id in self.scheduled_req_ids:
                # This request has already been scheduled.
                req_index += 1
                continue

            num_new_tokens = (request.num_tokens_with_spec -
                              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)
            assert num_new_tokens > 0

            # Schedule encoder inputs.
            if request.has_encoder_inputs:
                (encoder_inputs_to_schedule, num_new_tokens,
                 new_encoder_budget) = self._try_schedule_encoder_inputs(
                     request, request.num_computed_tokens, num_new_tokens,
                     encoder_budget)
                if num_new_tokens == 0:
                    # The request cannot be scheduled because the encoder budget
                    # or the encoder cache is exhausted.
                    # NOTE(woosuk): By using `continue` instead of `break` here,
                    # we intentionally relax the strict FCFS scheduling policy
                    # to allow lower-priority requests to be scheduled when a
                    # higher-priority request is blocked by encoder constraints.
                    req_index += 1
                    continue
            else:
                encoder_inputs_to_schedule = None
                new_encoder_budget = encoder_budget

            while True:
                new_blocks = self.kv_cache_manager.allocate_slots(
                    request, num_new_tokens)
                if new_blocks is None:
                    # The request cannot be scheduled.
                    # Preempt the lowest-priority request.
                    preempted_req = self.running.pop()
                    self.kv_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.appendleft(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)
            self.scheduled_req_ids.add(request.request_id)
            if request.use_structured_output:
                # PERF: in case of chunked prefill,
                # request might not include any new tokens.
                # Therefore, we might introduce some additional
                # cycle to fill in the bitmask, which could be a big no-op.
                structured_output_request_ids[request.request_id] = req_index
            req_to_new_block_ids[request.request_id] = [
                b.block_id for b in 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_budget = new_encoder_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 deque to collect requests that need to be skipped
        # and put back at the head of the waiting queue later
        skipped_waiting_requests: deque[Request] = deque()

        # Next, schedule the WAITING requests.
        if not preempted_reqs:
            while self.waiting and token_budget > 0:
                if len(self.running) == self.max_num_running_reqs:
                    break

                request = self.waiting[0]

                # 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.popleft()
                        skipped_waiting_requests.appendleft(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.popleft()
                    skipped_waiting_requests.appendleft(request)
                    continue

                # Get already-cached tokens.
                computed_blocks, num_computed_tokens = \
                    self.kv_cache_manager.get_computed_blocks(request)
                # Number of tokens to be scheduled.
                # 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)
                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_budget) = self._try_schedule_encoder_inputs(
                         request, num_computed_tokens, num_new_tokens,
                         encoder_budget)
                    if num_new_tokens == 0:
                        # The request cannot be scheduled.
                        break
                else:
                    encoder_inputs_to_schedule = None
                    new_encoder_budget = encoder_budget

                new_blocks = self.kv_cache_manager.allocate_slots(
                    request, num_new_tokens, computed_blocks)
                if new_blocks is None:
                    # The request cannot be scheduled.
                    break

                self.waiting.popleft()
                if request.use_structured_output:
                    structured_output_request_ids[
                        request.request_id] = req_index
                req_index += 1
                self.running.append(request)
                self.scheduled_req_ids.add(request.request_id)
                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_block_ids[request.request_id] = [
                    b.block_id for b in computed_blocks + new_blocks
                ]
                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

                # 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_budget = new_encoder_budget

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

        grammar_bitmask = self.structured_output_manager.grammar_bitmask(
            self.requests,
            structured_output_request_ids,
            len(self.running),
        )
        # Construct the scheduler output.
        new_reqs_data = [
            NewRequestData.from_request(req,
                                        req_to_new_block_ids[req.request_id])
            for req in scheduled_new_reqs
        ]
        resumed_reqs_data = [
            self._make_cached_request_data(
                req,
                num_scheduled_tokens[req.request_id],
                len(scheduled_spec_decode_tokens.get(req.request_id, ())),
                req_to_new_block_ids[req.request_id],
                resumed_from_preemption=True,
            ) for req in scheduled_resumed_reqs
        ]
        running_reqs_data = [
            self._make_cached_request_data(
                req,
                num_scheduled_tokens[req.request_id],
                len(scheduled_spec_decode_tokens.get(req.request_id, ())),
                req_to_new_block_ids[req.request_id],
                resumed_from_preemption=False,
            ) for req in scheduled_running_reqs
        ]
        scheduler_output = SchedulerOutput(
            scheduled_new_reqs=new_reqs_data,
            scheduled_cached_reqs=resumed_reqs_data + running_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,
            # 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_input_ids=self.encoder_cache_manager.get_freed_ids(),
            structured_output_request_ids=structured_output_request_ids,
            grammar_bitmask=grammar_bitmask,
        )

        # Advance the number of computed tokens for the request AFTER
        # the request is scheduled.
        # 1. The scheduler_output of the current step has to include the
        #    original number of scheduled tokens to determine input IDs.
        # 2. Advance the number of computed tokens here allowing us to
        #    schedule the prefill request again immediately in the next
        #    scheduling step.
        # 3. If some tokens (e.g. spec tokens) are rejected later, the number of
        #    computed tokens will be adjusted in update_from_output.
        for req_id, num_scheduled_token in num_scheduled_tokens.items():
            self.requests[req_id].num_computed_tokens += num_scheduled_token

        self.finished_req_ids = set()
        return scheduler_output

    def _make_cached_request_data(
        self,
        request: Request,
        num_scheduled_tokens: int,
        num_scheduled_spec_tokens: int,
        new_block_ids: list[int],
        resumed_from_preemption: bool,
    ) -> CachedRequestData:
        # OPTIMIZATION: Cache the CachedRequestData objects to avoid creating
        # them at each scheduling step.
        num_computed_tokens = request.num_computed_tokens
        num_regular_tokens = num_scheduled_tokens - num_scheduled_spec_tokens
        new_token_ids = request.all_token_ids[
            num_computed_tokens:num_computed_tokens + num_regular_tokens]
        req_data = self._cached_reqs_data.get(request.request_id)
        if req_data is not None:
            req_data.resumed_from_preemption = resumed_from_preemption
            req_data.new_token_ids = new_token_ids
            req_data.new_block_ids = new_block_ids
            req_data.num_computed_tokens = num_computed_tokens
        else:
            req_data = CachedRequestData.from_request(request,
                                                      resumed_from_preemption,
                                                      new_token_ids,
                                                      new_block_ids)
            self._cached_reqs_data[request.request_id] = req_data
        return req_data

    def _try_schedule_encoder_inputs(
        self,
        request: Request,
        num_computed_tokens: int,
        num_new_tokens: int,
        encoder_budget: int,
    ) -> tuple[list[int], int, int]:
        """
        Determine which encoder inputs need to be scheduled in the current step,
        and update `num_new_tokens` and encoder token budget accordingly.

        An encoder input will be scheduled if:
        - Its output tokens overlap with the range of tokens being computed
        in this step, i.e.,
        [num_computed_tokens, num_computed_tokens + num_new_tokens).
        - It is not already computed and stored in the encoder cache.
        - There is sufficient encoder token budget to process it.
        - The encoder cache has space to store it.

        If an encoder input cannot be scheduled due to cache or budget
        limitations, the method adjusts `num_new_tokens` to schedule only the
        decoder tokens up to just before the unschedulable encoder input.
        """
        encoder_inputs_to_schedule: list[int] = []
        mm_positions = request.mm_positions
        assert mm_positions is not None
        assert len(mm_positions) > 0
        for i, pos_info in enumerate(mm_positions):
            start_pos = pos_info["offset"]
            num_encoder_tokens = pos_info["length"]

            # The encoder output is needed if the two ranges overlap:
            # [num_computed_tokens, num_computed_tokens + num_new_tokens) and
            # [start_pos, start_pos + num_encoder_tokens)
            if start_pos >= num_computed_tokens + num_new_tokens:
                # The encoder input is not needed in this step.
                break
            if start_pos + num_encoder_tokens <= num_computed_tokens:
                # The encoder input is already computed and stored
                # in the decoder's KV cache.
                continue

            if self.encoder_cache_manager.has_cache(request, i):
                # The encoder input is already computed and cached.
                continue
            if (not self.encoder_cache_manager.can_allocate(request, i)
                    or num_encoder_tokens > encoder_budget):
                # The encoder cache is full or the encoder budget is exhausted.
                # NOTE(woosuk): We assume that the encoder input tokens should
                # be processed altogether, as the encoder usually uses
                # bidirectional attention.
                if num_computed_tokens < start_pos:
                    # We only schedule the decoder tokens just before the
                    # encoder input.
                    num_new_tokens = start_pos - num_computed_tokens
                else:
                    # Because of prefix caching, num_computed_tokens is greater
                    # than start_pos even though its encoder input is not
                    # available. In this case, we can't schedule any token for
                    # the request in this step.
                    num_new_tokens = 0
                break

            encoder_budget -= num_encoder_tokens
            encoder_inputs_to_schedule.append(i)
        return encoder_inputs_to_schedule, num_new_tokens, encoder_budget

    def update_from_output(
        self,
        scheduler_output: SchedulerOutput,
        model_runner_output: ModelRunnerOutput,
    ) -> EngineCoreOutputs:
        sampled_token_ids = model_runner_output.sampled_token_ids
        spec_token_ids = model_runner_output.spec_token_ids
        logprobs = model_runner_output.logprobs
        prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict
        num_scheduled_tokens = scheduler_output.num_scheduled_tokens

        new_running: list[Request] = []
        outputs: list[EngineCoreOutput] = []
        spec_decoding_stats: Optional[SpecDecodingStats] = None

        # NOTE(woosuk): As len(self.running) can be up to 1K or more, the below
        # loop can be a performance bottleneck. We should do our best to avoid
        # expensive operations inside the loop.
        for request in self.running:
            req_id = request.request_id
            num_tokens_scheduled = num_scheduled_tokens.get(req_id, 0)
            if num_tokens_scheduled == 0:
                # The request was not scheduled in this step.
                new_running.append(request)
                continue

            req_index = model_runner_output.req_id_to_index[req_id]
            generated_token_ids = sampled_token_ids[req_index]

            scheduled_spec_token_ids = (
                scheduler_output.scheduled_spec_decode_tokens.get(req_id))
            if scheduled_spec_token_ids:
                # num_computed_tokens represents the number of tokens
                # processed in the current step, considering scheduled
                # tokens and rejections. If some tokens are rejected,
                # num_computed_tokens is decreased by the number of rejected
                # tokens, where is given by:
                # len(scheduled_spec_token_ids) + 1 - len(generated_token_ids).
                num_tokens_rejected = (len(scheduled_spec_token_ids) + 1 -
                                       len(generated_token_ids))
                request.num_computed_tokens -= num_tokens_rejected
                spec_decoding_stats = self.make_spec_decoding_stats(
                    spec_decoding_stats,
                    num_draft_tokens=len(scheduled_spec_token_ids),
                    num_accepted_tokens=len(generated_token_ids) - 1)

            cached_encoder_input_ids = (
                self.encoder_cache_manager.get_cached_input_ids(request))
            # OPTIMIZATION: Avoid list(set) if the set is empty.
            if cached_encoder_input_ids:
                for input_id in list(cached_encoder_input_ids):
                    mm_positions = request.mm_positions[input_id]
                    start_pos = mm_positions["offset"]
                    num_tokens = mm_positions["length"]
                    if start_pos + num_tokens <= request.num_computed_tokens:
                        # The encoder output is already processed and stored
                        # in the decoder's KV cache.
                        self.encoder_cache_manager.free_encoder_input(
                            request, input_id)

            # Add newly generated spec token ids to the request.
            if spec_token_ids is not None:
                request.spec_token_ids = spec_token_ids[req_index]

            stopped = False
            new_logprobs = None
            new_token_ids = generated_token_ids

            # Append generated tokens and check for stop. Note that if
            # a request is still being prefilled, we expect the model runner
            # to return empty token ids for the request.
            for num_new, output_token_id in enumerate(new_token_ids, 1):
                request.append_output_token_ids(output_token_id)

                # Check for stop and update request state.
                # This must be called before we make the EngineCoreOutput.
                stopped = check_stop(request, self.max_model_len)
                if stopped:
                    self._free_request(request)
                    del new_token_ids[num_new:]  # Trim new tokens if needed.
                    break

            # Extract sample logprobs if needed.
            if request.sampling_params.logprobs is not None and logprobs:
                # NOTE: once we support N tokens per step (spec decode),
                # the outer lists can be of length > 1.
                new_logprobs = logprobs.slice(req_index, req_index + 1)

            if new_token_ids and request.use_structured_output:
                # NOTE: structured_output_request
                # should not be None if use_structured_output, we have
                # check above, so safe to ignore type warning
                request.structured_output_request.grammar.accept_tokens(  # type: ignore[union-attr]
                    req_id, new_token_ids)

            # Get prompt logprobs for this request.
            prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
            if new_token_ids:
                # Add EngineCoreOutput for this Request.
                outputs.append(
                    EngineCoreOutput(
                        request_id=req_id,
                        new_token_ids=new_token_ids,
                        finish_reason=request.get_finished_reason(),
                        new_logprobs=new_logprobs,
                        new_prompt_logprobs_tensors=prompt_logprobs_tensors,
                        stop_reason=request.stop_reason,
                        events=request.take_events()))
            else:
                # Invariant: EngineCore returns no partial prefill outputs.
                assert not prompt_logprobs_tensors

            self.scheduled_req_ids.remove(req_id)
            if not stopped:
                new_running.append(request)

        self.running = new_running
        engine_core_outputs = EngineCoreOutputs(
            outputs=outputs,
            scheduler_stats=self.make_stats(spec_decoding_stats),
        )
        if self.include_finished_set:
            #TODO currently sending duplicates here, improve this
            engine_core_outputs.finished_requests = (
                scheduler_output.finished_req_ids | self.finished_req_ids)

        return engine_core_outputs

    def add_request(self, request: Request) -> None:
        self.waiting.append(request)
        self.requests[request.request_id] = request
        if self.log_stats:
            request.record_event(EngineCoreEventType.QUEUED)

    def finish_requests(
        self,
        request_ids: Union[str, Iterable[str]],
        finished_status: RequestStatus,
    ) -> None:
        """Handles the finish signal from outside the scheduler.

        For example, the API server can abort a request when the client
        disconnects.
        """
        assert RequestStatus.is_finished(finished_status)
        if isinstance(request_ids, str):
            request_ids = (request_ids, )
        else:
            request_ids = set(request_ids)

        for req_id in request_ids:
            request = self.requests.get(req_id)
            if request is None:
                # Invalid request ID.
                continue

            if request.status == RequestStatus.RUNNING:
                self.running.remove(request)
                self.scheduled_req_ids.discard(request.request_id)
            else:
                self.waiting.remove(request)
            request.status = finished_status
            self._free_request(request)

    def _free_request(self, request: Request) -> None:
        assert request.is_finished()
        self.kv_cache_manager.free(request)
        self.kv_cache_manager.free_block_hashes(request)
        self.encoder_cache_manager.free(request)
        self._cached_reqs_data.pop(request.request_id, None)
        del self.requests[request.request_id]
        self.finished_req_ids.add(request.request_id)

    def get_num_unfinished_requests(self) -> int:
        return len(self.waiting) + len(self.running)

    def has_finished_requests(self) -> bool:
        return len(self.finished_req_ids) > 0

    def get_num_unscheduled_requests(self) -> int:
        """Number of requests that are not being processed by the executor."""
        return self.get_num_unfinished_requests() - len(self.scheduled_req_ids)

    def reset_prefix_cache(self) -> bool:
        return self.kv_cache_manager.reset_prefix_cache()

    def make_stats(
        self,
        spec_decoding_stats: Optional[SpecDecodingStats] = None,
    ) -> Optional[SchedulerStats]:
        if not self.log_stats:
            return None
        return SchedulerStats(
            num_running_reqs=len(self.running),
            num_waiting_reqs=len(self.waiting),
            gpu_cache_usage=self.kv_cache_manager.usage,
            prefix_cache_stats=self.kv_cache_manager.make_prefix_cache_stats(),
            spec_decoding_stats=spec_decoding_stats,
        )

    def make_spec_decoding_stats(
        self,
        spec_decoding_stats: Optional[SpecDecodingStats],
        num_draft_tokens: int,
        num_accepted_tokens: int,
    ) -> Optional[SpecDecodingStats]:
        if not self.log_stats:
            return None
        if spec_decoding_stats is None:
            spec_decoding_stats = SpecDecodingStats()
        spec_decoding_stats.observe(num_draft_tokens=num_draft_tokens,
                                    num_accepted_tokens=num_accepted_tokens)
        return spec_decoding_stats
