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tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/async_scheduler.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from vllm.logger import init_logger
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.request import Request, RequestStatus
+
+logger = init_logger(__name__)
+
+
+class AsyncScheduler(Scheduler):
+
+    def _update_after_schedule(
+        self,
+        scheduler_output: SchedulerOutput,
+    ) -> None:
+        super()._update_after_schedule(scheduler_output)
+        for req_id in scheduler_output.num_scheduled_tokens:
+            request = self.requests[req_id]
+            if (request.num_computed_tokens == request.num_tokens +
+                    request.num_output_placeholders):
+                # The request will generate a new token in this scheduling step.
+                # TODO(woosuk): Support speculative decoding.
+                request.num_output_placeholders += 1
+
+    def _update_request_with_output(
+        self,
+        request: Request,
+        new_token_ids: list[int],
+    ) -> tuple[list[int], bool]:
+        status_before_update = request.status
+        new_token_ids, stopped = super()._update_request_with_output(
+            request, new_token_ids)
+
+        # Update the number of output placeholders.
+        request.num_output_placeholders -= len(new_token_ids)
+        assert request.num_output_placeholders >= 0
+
+        # Cache the new tokens. Preempted requests should be skipped.
+        if status_before_update == RequestStatus.RUNNING:
+            self.kv_cache_manager.cache_blocks(
+                request,
+                request.num_computed_tokens - request.num_output_placeholders)
+        return new_token_ids, stopped

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/interface.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Optional, Union
+
+if TYPE_CHECKING:
+    from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.engine import EngineCoreOutputs
+    from vllm.v1.metrics.stats import SchedulerStats
+    from vllm.v1.outputs import ModelRunnerOutput
+    from vllm.v1.request import Request, RequestStatus
+
+
+class SchedulerInterface(ABC):
+
+    @abstractmethod
+    def schedule(self) -> "SchedulerOutput":
+        """Schedule the requests to process in this scheduling step.
+
+        The scheduling decision is made at the iteration level. Each scheduling
+        step corresponds to a single forward pass of the model. Therefore, this
+        method is called repeatedly by a busy loop in the engine.
+
+        Essentially, the scheduler produces a dictionary of {req_id: num_tokens}
+        that specifies how many tokens to process for each request in this
+        scheduling step. For example, num_tokens can be as large as the number
+        of prompt tokens for new requests, or it can be 1 for the requests that
+        are auto-regressively generating new tokens one by one. Otherwise, it
+        can be somewhere in between in case of chunked prefills, prefix caching,
+        speculative decoding, etc.
+
+        Additionally, the scheduler also returns useful data about each request
+        or the batch as a whole. The model runner will use this information in
+        preparing inputs to the model.
+
+        Returns:
+            A SchedulerOutput object containing information about the scheduled
+            requests.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def update_from_output(
+        self,
+        scheduler_output: "SchedulerOutput",
+        model_runner_output: "ModelRunnerOutput",
+    ) -> dict[int, "EngineCoreOutputs"]:
+        """Update the scheduler state based on the model runner output.
+
+        This method is called after the model runner has processed the scheduled
+        requests. The model runner output includes generated token ids, draft
+        token ids for next step, etc. The scheduler uses this information to
+        update its states, checks the finished requests, and returns the output
+        for each request.
+
+        Returns:
+            A dict of client index to EngineCoreOutputs object containing the
+            outputs for each request originating from that client.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def add_request(self, request: "Request") -> None:
+        """Add a new request to the scheduler's internal queue.
+        
+        Args:
+            request: The new request being added.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def finish_requests(
+        self,
+        request_ids: Union[str, Iterable[str]],
+        finished_status: "RequestStatus",
+    ) -> None:
+        """Finish the requests in the scheduler's internal queue. If the request
+        is not in the queue, this method will do nothing.
+
+        This method is called in two cases:
+        1. When the request is aborted by the client.
+        2. When the frontend process detects a stop string of the request after
+           de-tokenizing its generated tokens.
+           
+        Args:
+            request_ids: A single or a list of request IDs.
+            finished_status: The finished status of the given requests.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_num_unfinished_requests(self) -> int:
+        """Number of unfinished requests in the scheduler's internal queue."""
+        raise NotImplementedError
+
+    def has_unfinished_requests(self) -> bool:
+        """Returns True if there are unfinished requests in the scheduler's
+        internal queue."""
+        return self.get_num_unfinished_requests() > 0
+
+    @abstractmethod
+    def has_finished_requests(self) -> bool:
+        """Returns True if there are finished requests that need to be cleared.
+        NOTE: This is different from `not self.has_unfinished_requests()`.
+
+        The scheduler maintains an internal list of the requests finished in the
+        previous step. This list is returned from the next call to schedule(),
+        to be sent to the model runner in the next step to clear cached states
+        for these finished requests.
+
+        This method checks if this internal list of finished requests is
+        non-empty. This information is useful for DP attention.
+        """
+        raise NotImplementedError
+
+    def has_requests(self) -> bool:
+        """Returns True if there are unfinished requests, or finished requests
+        not yet returned in SchedulerOutputs."""
+        return self.has_unfinished_requests() or self.has_finished_requests()
+
+    @abstractmethod
+    def reset_prefix_cache(self) -> bool:
+        """Reset the prefix cache for KV cache.
+
+        This is particularly required when the model weights are live-updated.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_request_counts(self) -> tuple[int, int]:
+        """Returns (num_running_reqs, num_waiting_reqs)."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def make_stats(self) -> Optional["SchedulerStats"]:
+        """Make a SchedulerStats object for logging.
+
+        The SchedulerStats object is created for every scheduling step.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def shutdown(self) -> None:
+        """Shutdown the scheduler."""
+        raise NotImplementedError
+
+    def get_kv_connector(self) -> Optional["KVConnectorBase_V1"]:
+        return None

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/output.py

@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    import numpy as np
+    import numpy.typing as npt
+
+    from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+        KVConnectorMetadata)
+    from vllm.lora.request import LoRARequest
+    from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
+    from vllm.pooling_params import PoolingParams
+    from vllm.sampling_params import SamplingParams
+    from vllm.v1.request import Request
+
+
+@dataclass
+class NewRequestData:
+
+    req_id: str
+    prompt_token_ids: list[int]
+    mm_kwargs: list[MultiModalKwargsItem]
+    mm_hashes: list[str]
+    mm_positions: list[PlaceholderRange]
+    sampling_params: Optional[SamplingParams]
+    pooling_params: Optional[PoolingParams]
+    block_ids: tuple[list[int], ...]
+    num_computed_tokens: int
+    lora_request: Optional[LoRARequest]
+
+    @classmethod
+    def from_request(
+        cls,
+        request: Request,
+        block_ids: tuple[list[int], ...],
+    ) -> NewRequestData:
+        return cls(
+            req_id=request.request_id,
+            prompt_token_ids=request.prompt_token_ids,
+            mm_kwargs=request.mm_kwargs,
+            mm_hashes=request.mm_hashes,
+            mm_positions=request.mm_positions,
+            sampling_params=request.sampling_params,
+            pooling_params=request.pooling_params,
+            block_ids=block_ids,
+            num_computed_tokens=request.num_computed_tokens,
+            lora_request=request.lora_request,
+        )
+
+    def __repr__(self):
+        return (f"NewRequestData("
+                f"req_id={self.req_id},"
+                f"prompt_token_ids={self.prompt_token_ids},"
+                f"mm_kwargs={self.mm_kwargs},"
+                f"mm_hashes={self.mm_hashes},"
+                f"mm_positions={self.mm_positions},"
+                f"sampling_params={self.sampling_params},"
+                f"block_ids={self.block_ids},"
+                f"num_computed_tokens={self.num_computed_tokens},"
+                f"lora_request={self.lora_request}"
+                ")")
+
+    # Version of __repr__ with the prompt data obfuscated
+    def anon_repr(self):
+        return (f"NewRequestData("
+                f"req_id={self.req_id},"
+                f"prompt_token_ids_len={len(self.prompt_token_ids)},"
+                f"mm_kwargs={self.mm_kwargs},"
+                f"mm_hashes={self.mm_hashes},"
+                f"mm_positions={self.mm_positions},"
+                f"sampling_params={self.sampling_params},"
+                f"block_ids={self.block_ids},"
+                f"num_computed_tokens={self.num_computed_tokens},"
+                f"lora_request={self.lora_request}"
+                ")")
+
+
+@dataclass
+class CachedRequestData:
+
+    req_ids: list[str]
+    # If resumed_from_preemption is False, new_block_ids will be appended to
+    # the request's block IDs. If True, new_block_ids will be used as the
+    # request's block IDs instead of appending to the existing block IDs.
+    resumed_from_preemption: list[bool]
+    # NOTE(woosuk): new_token_ids is only used for pipeline parallelism.
+    # When PP is not used, new_token_ids will be empty.
+    new_token_ids: list[list[int]]
+    new_block_ids: list[tuple[list[int], ...]]
+    num_computed_tokens: list[int]
+
+    @property
+    def num_reqs(self) -> int:
+        return len(self.req_ids)
+
+    @classmethod
+    def make_empty(cls) -> CachedRequestData:
+        return cls(
+            req_ids=[],
+            resumed_from_preemption=[],
+            new_token_ids=[],
+            new_block_ids=[],
+            num_computed_tokens=[],
+        )
+
+
+@dataclass
+class SchedulerOutput:
+
+    # list of the requests that are scheduled for the first time.
+    # We cache the request's data in each worker process, so that we don't
+    # need to re-send it every scheduling step.
+    scheduled_new_reqs: list[NewRequestData]
+    # list of the requests that have been scheduled before.
+    # Since the request's data is already cached in the worker processes,
+    # we only send the diff to minimize the communication cost.
+    scheduled_cached_reqs: CachedRequestData
+
+    # req_id -> num_scheduled_tokens
+    # Number of tokens scheduled for each request.
+    num_scheduled_tokens: dict[str, int]
+    # Total number of tokens scheduled for all requests.
+    # Equal to sum(num_scheduled_tokens.values())
+    total_num_scheduled_tokens: int
+    # req_id -> spec_token_ids
+    # If a request does not have any spec decode tokens, it will not be
+    # included in the dictionary.
+    scheduled_spec_decode_tokens: dict[str, list[int]]
+    # req_id -> encoder input indices that need processing.
+    # E.g., if a request has [0, 1], it could mean the vision encoder needs
+    # to process that the request's 0-th and 1-th images in the current step.
+    scheduled_encoder_inputs: dict[str, list[int]]
+    # Number of common prefix blocks for all requests in each KV cache group.
+    # This can be used for cascade attention.
+    num_common_prefix_blocks: list[int]
+
+    # 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.
+    finished_req_ids: set[str]
+    # list of (req_id, encoder_input_index) tuples.
+    # Used to free the encoder cache.
+    free_encoder_input_ids: list[tuple[str, int]]
+
+    # Dict of request ids to their index within the batch
+    # for filling the next token bitmask
+    structured_output_request_ids: dict[str, int]
+    # the bitmask for the whole batch
+    grammar_bitmask: Optional[npt.NDArray[np.int32]]
+
+    # KV Cache Connector metadata.
+    kv_connector_metadata: Optional[KVConnectorMetadata] = None

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/request_queue.py

@@ -0,0 +1,224 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import heapq
+from abc import ABC, abstractmethod
+from collections import deque
+from collections.abc import Iterable, Iterator
+from enum import Enum
+
+from vllm.v1.request import Request
+
+
+class SchedulingPolicy(Enum):
+    """Enum for scheduling policies."""
+    FCFS = "fcfs"
+    PRIORITY = "priority"
+
+
+class RequestQueue(ABC):
+    """Abstract base class for request queues."""
+
+    @abstractmethod
+    def add_request(self, request: Request) -> None:
+        """Add a request to the queue according to the policy."""
+        pass
+
+    @abstractmethod
+    def pop_request(self) -> Request:
+        """Pop a request from the queue according to the policy."""
+        pass
+
+    @abstractmethod
+    def peek_request(self) -> Request:
+        """Peek at the request at the front of the queue without removing it."""
+        pass
+
+    @abstractmethod
+    def prepend_request(self, request: Request) -> None:
+        """Prepend a request to the front of the queue."""
+        pass
+
+    @abstractmethod
+    def prepend_requests(self, requests: RequestQueue) -> None:
+        """Prepend all requests from another queue to the front of this
+        queue."""
+        pass
+
+    @abstractmethod
+    def remove_request(self, request: Request) -> None:
+        """Remove a specific request from the queue."""
+        pass
+
+    @abstractmethod
+    def remove_requests(self, requests: Iterable[Request]) -> None:
+        """Remove multiple specific requests from the queue."""
+        pass
+
+    @abstractmethod
+    def __bool__(self) -> bool:
+        """Check if queue has any requests."""
+        pass
+
+    @abstractmethod
+    def __len__(self) -> int:
+        """Get number of requests in queue."""
+        pass
+
+    @abstractmethod
+    def __iter__(self) -> Iterator[Request]:
+        """Iterate over the queue according to the policy."""
+        pass
+
+    @abstractmethod
+    def __reversed__(self) -> Iterator[Request]:
+        """Iterate over the queue in reverse order."""
+        pass
+
+
+class FCFSRequestQueue(deque[Request], RequestQueue):
+    """A first-come-first-served queue that supports deque operations."""
+
+    def add_request(self, request: Request) -> None:
+        """Add a request to the queue according to FCFS policy."""
+        self.append(request)
+
+    def pop_request(self) -> Request:
+        """Pop a request from the queue according to FCFS policy."""
+        return self.popleft()
+
+    def peek_request(self) -> Request:
+        """Peek at the next request in the queue without removing it."""
+        if not self:
+            raise IndexError("peek from an empty queue")
+        return self[0]
+
+    def prepend_request(self, request: Request) -> None:
+        """Prepend a request to the front of the queue."""
+        self.appendleft(request)
+
+    def prepend_requests(self, requests: RequestQueue) -> None:
+        """Prepend all requests from another queue to the front of this
+        queue."""
+        self.extendleft(reversed(requests))
+
+    def remove_request(self, request: Request) -> None:
+        """Remove a specific request from the queue."""
+        self.remove(request)
+
+    def remove_requests(self, requests: Iterable[Request]) -> None:
+        """Remove multiple specific requests from the queue."""
+        requests_to_remove = set(requests)
+        filtered_requests = [
+            req for req in self if req not in requests_to_remove
+        ]
+        # deque does not support in-place filtering, so we need to clear
+        # and extend
+        self.clear()
+        self.extend(filtered_requests)
+
+    def __bool__(self) -> bool:
+        """Check if queue has any requests."""
+        return len(self) > 0
+
+    def __len__(self) -> int:
+        """Get number of requests in queue."""
+        return super().__len__()
+
+    def __iter__(self) -> Iterator[Request]:
+        """Iterate over the queue according to FCFS policy."""
+        return super().__iter__()
+
+    def __reversed__(self) -> Iterator[Request]:
+        """Iterate over the queue in reverse order."""
+        return super().__reversed__()
+
+
+class PriorityRequestQueue(RequestQueue):
+    """
+    A priority queue that supports heap operations.
+
+    Requests with a smaller value of `priority` are processed first.
+    If multiple requests have the same priority, the one with the earlier
+    `arrival_time` is processed first.
+    """
+
+    def __init__(self) -> None:
+        self._heap: list[tuple[int, float, Request]] = []
+
+    def add_request(self, request: Request) -> None:
+        """Add a request to the queue according to priority policy."""
+        heapq.heappush(self._heap,
+                       (request.priority, request.arrival_time, request))
+
+    def pop_request(self) -> Request:
+        """Pop a request from the queue according to priority policy."""
+        if not self._heap:
+            raise IndexError("pop from empty heap")
+        _, _, request = heapq.heappop(self._heap)
+        return request
+
+    def peek_request(self) -> Request:
+        """Peek at the next request in the queue without removing it."""
+        if not self._heap:
+            raise IndexError("peek from empty heap")
+        _, _, request = self._heap[0]
+        return request
+
+    def prepend_request(self, request: Request) -> None:
+        """Add a request to the queue according to priority policy.
+        
+        Note: In a priority queue, there is no concept of prepending to the 
+        front. Requests are ordered by (priority, arrival_time)."""
+        self.add_request(request)
+
+    def prepend_requests(self, requests: RequestQueue) -> None:
+        """Add all requests from another queue according to priority policy.
+        
+        Note: In a priority queue, there is no concept of prepending to the 
+        front. Requests are ordered by (priority, arrival_time)."""
+        for request in requests:
+            self.add_request(request)
+
+    def remove_request(self, request: Request) -> None:
+        """Remove a specific request from the queue."""
+        self._heap = [(p, t, r) for p, t, r in self._heap if r != request]
+        heapq.heapify(self._heap)
+
+    def remove_requests(self, requests: Iterable[Request]) -> None:
+        """Remove multiple specific requests from the queue."""
+        requests_to_remove = set(requests)
+        self._heap = [(p, t, r) for p, t, r in self._heap
+                      if r not in requests_to_remove]
+        heapq.heapify(self._heap)
+
+    def __bool__(self) -> bool:
+        """Check if queue has any requests."""
+        return bool(self._heap)
+
+    def __len__(self) -> int:
+        """Get number of requests in queue."""
+        return len(self._heap)
+
+    def __iter__(self) -> Iterator[Request]:
+        """Iterate over the queue according to priority policy."""
+        heap_copy = self._heap[:]
+        while heap_copy:
+            _, _, request = heapq.heappop(heap_copy)
+            yield request
+
+    def __reversed__(self) -> Iterator[Request]:
+        """Iterate over the queue in reverse priority order."""
+        return reversed(list(self))
+
+
+def create_request_queue(policy: SchedulingPolicy) -> RequestQueue:
+    """Create request queue based on scheduling policy."""
+    if policy == SchedulingPolicy.PRIORITY:
+        return PriorityRequestQueue()
+    elif policy == SchedulingPolicy.FCFS:
+        return FCFSRequestQueue()
+    else:
+        raise ValueError(f"Unknown scheduling policy: {policy}")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/scheduler.py

@@ -0,0 +1,1161 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+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.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.request_queue import (SchedulingPolicy,
+                                              create_request_queue)
+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 KVConnectorOutput, 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,
+        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
+
+        # 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
+        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")
+            self.connector = KVConnectorFactory.create_connector(
+                config=self.vllm_config, role=KVConnectorRole.SCHEDULER)
+
+        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
+
+        # 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). 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)
+
+        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,
+        )
+        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] = []
+
+        # 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, tuple[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]
+
+            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_budget = encoder_budget
+            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 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)
+                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)
+                    else:
+                        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.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)
+            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] = (
+                new_blocks.get_block_ids())
+            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 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.
+        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()
+
+                # 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))
+
+                    # 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_budget = encoder_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_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
+
+                # 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)
+
+                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,
+                )
+
+                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
+
+                if request.use_structured_output:
+                    structured_output_request_ids[request.request_id] = (
+                        req_index)
+                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_block_ids[request.request_id] = (
+                    self.kv_cache_manager.get_block_ids(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_budget = new_encoder_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)))
+
+        grammar_bitmask = self.structured_output_manager.grammar_bitmask(
+            self.requests,
+            structured_output_request_ids,
+            scheduled_spec_decode_tokens,
+        )
+        # 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
+        ]
+        cached_reqs_data = self._make_cached_request_data(
+            scheduled_running_reqs,
+            scheduled_resumed_reqs,
+            num_scheduled_tokens,
+            scheduled_spec_decode_tokens,
+            req_to_new_block_ids,
+        )
+        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,
+            # 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,
+        )
+
+        # 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
+
+        events = self.kv_cache_manager.take_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 _update_after_schedule(
+        self,
+        scheduler_output: SchedulerOutput,
+    ) -> None:
+        # 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.
+        num_scheduled_tokens = scheduler_output.num_scheduled_tokens
+        for req_id, num_scheduled_token in num_scheduled_tokens.items():
+            request = self.requests[req_id]
+            request.num_computed_tokens += num_scheduled_token
+
+            # NOTE: _free_encoder_inputs relies on num_computed_tokens, which
+            # may be updated again in _update_from_output for speculative
+            # decoding. However, it is safe to call the method here because
+            # encoder inputs are always part of the prompt, not the output,
+            # and thus are unaffected by speculative decoding.
+            if request.has_encoder_inputs:
+                self._free_encoder_inputs(request)
+
+        # Clear the finished request IDs.
+        # NOTE: We shouldn't do self.finished_req_ids.clear() here because
+        # it will also affect the scheduler output.
+        self.finished_req_ids = set()
+
+    def _make_cached_request_data(
+        self,
+        running_reqs: list[Request],
+        resumed_reqs: list[Request],
+        num_scheduled_tokens: dict[str, int],
+        spec_decode_tokens: dict[str, list[int]],
+        req_to_new_block_ids: dict[str, tuple[list[int], ...]],
+    ) -> CachedRequestData:
+        req_ids: list[str] = []
+        new_token_ids: list[list[int]] = []
+        new_block_ids: list[tuple[list[int], ...]] = []
+        num_computed_tokens: list[int] = []
+
+        use_connector = self.connector is not None
+        for req in itertools.chain(running_reqs, resumed_reqs):
+            req_id = req.request_id
+            req_ids.append(req_id)
+            num_tokens = (num_scheduled_tokens[req_id] -
+                          len(spec_decode_tokens.get(req_id, ())))
+            if self.use_pp:
+                # When using PP, the scheduler sends the sampled tokens back,
+                # because there's no direct communication between the first-
+                # stage worker and the last-stage worker. Otherwise, we don't
+                # need to send the sampled tokens back because the model runner
+                # will cache them.
+                token_ids = req.all_token_ids[req.num_computed_tokens:req.
+                                              num_computed_tokens + num_tokens]
+                new_token_ids.append(token_ids)
+            elif use_connector:
+                # When using a KVConnector, we add a placeholder to avoid index
+                # out of bounds errors. TODO: Remove this once the KVConnector
+                # is updated to handle token IDs properly.
+                new_token_ids.append([])
+            new_block_ids.append(req_to_new_block_ids[req_id])
+            num_computed_tokens.append(req.num_computed_tokens)
+        # Because resumed_reqs is usually empty, it is more efficient to do
+        # in-place appending so that we don't need to allocate a new list.
+        resumed_from_preemption = [False] * len(running_reqs)
+        resumed_from_preemption += [True] * len(resumed_reqs)
+
+        return CachedRequestData(
+            req_ids=req_ids,
+            resumed_from_preemption=resumed_from_preemption,
+            new_token_ids=new_token_ids,
+            new_block_ids=new_block_ids,
+            num_computed_tokens=num_computed_tokens,
+        )
+
+    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.
+
+        Note that num_computed_tokens includes both locally cached
+        blocks and externally cached blocks (via KVConnector).
+        """
+        if num_new_tokens == 0 or not request.has_encoder_inputs:
+            return [], num_new_tokens, encoder_budget
+        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 no encoder input chunking is allowed, we do not want to
+            # partially schedule a multimodal item. If the scheduled range would
+            # only cover part of the mm input, roll back to before the mm item.
+            if (self.scheduler_config.disable_chunked_mm_input
+                    and num_computed_tokens < start_pos
+                    and (num_computed_tokens + num_new_tokens)
+                    < (start_pos + num_encoder_tokens)):
+                num_new_tokens = start_pos - num_computed_tokens
+                break
+
+            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,
+    ) -> dict[int, 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
+        pooler_outputs = model_runner_output.pooler_output
+        num_nans_in_logits = model_runner_output.num_nans_in_logits
+
+        outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
+        spec_decoding_stats: Optional[SpecDecodingStats] = None
+
+        # NOTE(woosuk): As len(num_scheduled_tokens) 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.
+        stopped_running_reqs: set[Request] = set()
+        stopped_preempted_reqs: set[Request] = set()
+        for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
+            assert num_tokens_scheduled > 0
+            request = self.requests.get(req_id)
+            if request is None:
+                # The request is already finished. This can happen if the
+                # request is aborted while the model is executing it (e.g.,
+                # in pipeline parallelism).
+                continue
+
+            req_index = model_runner_output.req_id_to_index[req_id]
+            generated_token_ids = sampled_token_ids[
+                req_index] if sampled_token_ids else []
+
+            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)
+
+            stopped = False
+            new_logprobs = None
+            new_token_ids = generated_token_ids
+            kv_transfer_params = None
+            status_before_stop = request.status
+
+            # Check for stop and update request status.
+            if new_token_ids:
+                new_token_ids, stopped = self._update_request_with_output(
+                    request, new_token_ids)
+
+            # Stop checking for pooler models.
+            pooler_output = None
+            if pooler_outputs:
+                pooler_output = pooler_outputs[req_index]
+                stopped = check_stop(request, self.max_model_len,
+                                     pooler_output)
+
+            if stopped:
+                kv_transfer_params = self._free_request(request)
+                if status_before_stop == RequestStatus.RUNNING:
+                    stopped_running_reqs.add(request)
+                else:
+                    stopped_preempted_reqs.add(request)
+
+            # Extract sample logprobs if needed.
+            if request.sampling_params is not None \
+                and 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 self.structured_output_manager.should_advance(
+                    request):
+                # 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)
+
+            # spec_token_ids comes from the model runner output
+            if num_nans_in_logits is not None and req_id in num_nans_in_logits:
+                request.num_nans_in_logits = num_nans_in_logits[req_id]
+
+            # Add newly generated spec token ids to the request.
+            if spec_token_ids is not None:
+                if self.structured_output_manager.should_advance(request):
+                    metadata = request.structured_output_request
+                    # Needs to happen after new_token_ids are accepted.
+                    request.spec_token_ids = metadata.grammar.validate_tokens(  # type: ignore[union-attr]
+                        spec_token_ids[req_index])
+                else:
+                    request.spec_token_ids = spec_token_ids[req_index]
+
+            # Get prompt logprobs for this request.
+            prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
+            if new_token_ids or pooler_output is not None \
+                or kv_transfer_params:
+
+                # Add EngineCoreOutput for this Request.
+                outputs[request.client_index].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,
+                        pooling_output=pooler_output,
+                        stop_reason=request.stop_reason,
+                        events=request.take_events(),
+                        kv_transfer_params=kv_transfer_params,
+                        num_cached_tokens=request.num_cached_tokens,
+                    ))
+
+            else:
+                # Invariant: EngineCore returns no partial prefill outputs.
+                assert not prompt_logprobs_tensors
+
+        # Remove the stopped requests from the running and waiting queues.
+        if stopped_running_reqs:
+            self.running = [
+                req for req in self.running if req not in stopped_running_reqs
+            ]
+        if stopped_preempted_reqs:
+            # This is a rare case and unlikely to impact performance.
+            self.waiting.remove_requests(stopped_preempted_reqs)
+
+        # KV Connector: update state for finished KV Transfers.
+        if model_runner_output.kv_connector_output:
+            self._update_from_kv_xfer_finished(
+                model_runner_output.kv_connector_output)
+
+        # Create EngineCoreOutputs for all clients that have requests with
+        # outputs in this step.
+        engine_core_outputs = {
+            client_index: EngineCoreOutputs(outputs=outs)
+            for client_index, outs in outputs.items()
+        }
+
+        finished_req_ids = self.finished_req_ids_dict
+        if finished_req_ids:
+            # Include ids of requests that finished since last outputs
+            # were sent.
+            for client_index, finished_set in finished_req_ids.items():
+                # Set finished request set in EngineCoreOutputs for this client.
+                if (eco := engine_core_outputs.get(client_index)) is not None:
+                    eco.finished_requests = finished_set
+                else:
+                    engine_core_outputs[client_index] = EngineCoreOutputs(
+                        finished_requests=finished_set)
+            finished_req_ids.clear()
+
+        if engine_core_outputs:
+            # Return stats to only one of the front-ends.
+            next(iter(engine_core_outputs.values())).scheduler_stats = (
+                self.make_stats(spec_decoding_stats))
+
+        return engine_core_outputs
+
+    def _update_request_with_output(
+        self,
+        request: Request,
+        new_token_ids: list[int],
+    ) -> tuple[list[int], bool]:
+        # 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.
+        stopped = False
+        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:
+                del new_token_ids[num_new:]  # Trim new tokens if needed.
+                break
+        return new_token_ids, stopped
+
+    def _free_encoder_inputs(self, request: Request) -> None:
+        cached_encoder_input_ids = (
+            self.encoder_cache_manager.get_cached_input_ids(request))
+        # OPTIMIZATION: Avoid list(set) if the set is empty.
+        if not cached_encoder_input_ids:
+            return
+
+        # Here, we use list(set) to avoid modifying the set while iterating
+        # over it.
+        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)
+
+    def get_request_counts(self) -> tuple[int, int]:
+        """Returns (num_running_reqs, num_waiting_reqs)."""
+        return len(self.running), len(self.waiting)
+
+    def add_request(self, request: Request) -> None:
+        self.waiting.add_request(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)
+
+        running_requests_to_remove = []
+        waiting_requests_to_remove = []
+        valid_requests = []
+
+        # First pass: collect requests to remove from queues
+        for req_id in request_ids:
+            request = self.requests.get(req_id)
+            if request is None:
+                # Invalid request ID.
+                continue
+
+            valid_requests.append(request)
+            if request.status == RequestStatus.RUNNING:
+                running_requests_to_remove.append(request)
+            else:
+                waiting_requests_to_remove.append(request)
+
+        # Remove all requests from queues at once for better efficiency
+        for request in running_requests_to_remove:
+            self.running.remove(request)
+        if waiting_requests_to_remove:
+            self.waiting.remove_requests(waiting_requests_to_remove)
+
+        # Second pass: set status and free requests
+        for request in valid_requests:
+            request.status = finished_status
+            self._free_request(request)
+
+    def _free_request(self, request: Request) -> Optional[dict[str, Any]]:
+        assert request.is_finished()
+
+        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
+
+    def _free_blocks(self, request: Request):
+        assert request.is_finished()
+        self.kv_cache_manager.free(request)
+        del self.requests[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 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
+        prefix_cache_stats = self.kv_cache_manager.make_prefix_cache_stats()
+        assert prefix_cache_stats is not None
+        return SchedulerStats(
+            num_running_reqs=len(self.running),
+            num_waiting_reqs=len(self.waiting),
+            kv_cache_usage=self.kv_cache_manager.usage,
+            prefix_cache_stats=prefix_cache_stats,
+            spec_decoding_stats=spec_decoding_stats,
+            num_corrupted_reqs=sum(req.is_output_corrupted
+                                   for req in self.running),
+        )
+
+    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.new(self.num_spec_tokens)
+        spec_decoding_stats.observe_draft(
+            num_draft_tokens=num_draft_tokens,
+            num_accepted_tokens=num_accepted_tokens)
+        return spec_decoding_stats
+
+    def shutdown(self) -> None:
+        if self.kv_event_publisher:
+            self.kv_event_publisher.shutdown()
+
+    ########################################################################
+    # KV Connector Related Methods
+    ########################################################################
+
+    def get_kv_connector(self) -> Optional[KVConnectorBase_V1]:
+        return self.connector
+
+    def _connector_finished(
+            self, request: Request) -> tuple[bool, Optional[dict[str, Any]]]:
+        """
+        Invoke the KV connector request_finished() method if applicable.
+
+        Returns optional kv transfer parameters to be included with the
+        request outputs.
+        """
+        if self.connector is None:
+            return False, None
+
+        (block_ids, ) = self.kv_cache_manager.get_block_ids(request.request_id)
+        return self.connector.request_finished(request, block_ids)
+
+    def _update_waiting_for_remote_kv(self, request: Request) -> bool:
+        """
+        KV Connector: check if the request_id is finished_recving.
+
+        The finished_recving_kv_req_ids list is populated
+        on the previous steps()'s update_from_output based
+        on the worker side connector.
+
+        When the kv transfer is ready, we cache the blocks
+        and the request state will be moved back to WAITING from
+        WAITING_FOR_REMOTE_KV.
+        """
+        assert self.connector is not None
+        if request.request_id not in self.finished_recving_kv_req_ids:
+            return False
+
+        # Now that the blocks are ready, actually cache them.
+        (block_ids, ) = self.kv_cache_manager.get_block_ids(request.request_id)
+        num_computed_tokens = len(block_ids) * self.block_size
+        # Handle the case where num request tokens less then one block.
+        num_computed_tokens = min(num_computed_tokens, request.num_tokens)
+        if num_computed_tokens == request.num_tokens:
+            num_computed_tokens -= 1
+        # This will cache the blocks iff caching is enabled.
+        self.kv_cache_manager.cache_blocks(request, num_computed_tokens)
+
+        # Update the request state for scheduling.
+        request.num_computed_tokens = num_computed_tokens
+
+        # Return that we are ready.
+        self.finished_recving_kv_req_ids.remove(request.request_id)
+        return True
+
+    def _update_from_kv_xfer_finished(self,
+                                      kv_connector_output: KVConnectorOutput):
+        """
+        KV Connector: update the scheduler state based on the output.
+
+        The Worker side connectors add finished_recving and
+        finished_sending reqs to the output.
+        * if finished_sending: free the blocks
+        # if finished_recving: add to state so we can
+            scheduler the request during the next step.
+        """
+
+        if self.connector is not None:
+            self.connector.update_connector_output(kv_connector_output)
+
+        # KV Connector:: update recv and send status from last step.
+        for req_id in (kv_connector_output.finished_recving or ()):
+            logger.debug("Finished recving KV transfer for request %s", req_id)
+            self.finished_recving_kv_req_ids.add(req_id)
+        for req_id in (kv_connector_output.finished_sending or ()):
+            logger.debug("Finished sending KV transfer for request %s", req_id)
+            self._free_blocks(self.requests[req_id])

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/core/sched/utils.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+from vllm.v1.request import Request, RequestStatus
+
+
+def check_stop(request: Request,
+               max_model_len: int,
+               pooler_output: Optional[torch.Tensor] = None) -> bool:
+    if (request.num_tokens >= max_model_len
+            or request.num_output_tokens >= request.max_tokens):
+        request.status = RequestStatus.FINISHED_LENGTH_CAPPED
+        return True
+
+    if request.pooling_params:
+        if pooler_output is not None:
+            request.status = RequestStatus.FINISHED_STOPPED
+            return True
+        return False
+
+    sampling_params = request.sampling_params
+    assert sampling_params is not None
+    last_token_id = request.output_token_ids[-1]
+    if (not sampling_params.ignore_eos
+            and last_token_id == request.eos_token_id):
+        request.status = RequestStatus.FINISHED_STOPPED
+        return True
+
+    if last_token_id in (sampling_params.stop_token_ids or ()):
+        request.status = RequestStatus.FINISHED_STOPPED
+        request.stop_reason = last_token_id
+        return True
+    return False