__init__.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import enum
import time
from collections.abc import Mapping
from typing import Any, Literal

import msgspec
import numpy as np
import torch

from vllm.lora.request import LoRARequest
from vllm.multimodal.inputs import MultiModalFeatureSpec
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams
from vllm.v1.metrics.stats import SchedulerStats
from vllm.v1.outputs import LogprobsLists, LogprobsTensors
from vllm.v1.serial_utils import UtilityResult

# Type for pause_generation mode parameter.
# - "abort": Abort all in-flight requests immediately (default).
# - "wait": Wait for in-flight requests to complete before pausing.
# - "keep": Freeze requests in queue; they resume on resume_generation().
PauseMode = Literal["abort", "wait", "keep"]

# These are possible values of RequestOutput.finish_reason,
# so form part of the external API.
FINISH_REASON_STRINGS = ("stop", "length", "abort", "error", "repetition")

EEP_NOTIFICATION_CALL_ID = -1


class EEPNotificationType(enum.Enum):
    NEW_CORE_ENGINES_INIT_READY = "NEW_CORE_ENGINES_INIT_READY"
    NEW_CORE_ENGINES_WEIGHTS_INIT_READY = "NEW_CORE_ENGINES_WEIGHTS_INIT_READY"
    RECONFIGURE_FINISHED = "RECONFIGURE_FINISHED"
    SHUTDOWN_COMPLETE = "SHUTDOWN_COMPLETE"


class FinishReason(enum.IntEnum):
    """
    Reason a request finished - stop, length, abort, error, or repetition.

    Int rather than Str for more compact serialization.

    stop - a stop string was emitted
    length - max_tokens was consumed, or max_model_len was reached
    abort - aborted by client
    error - retryable request-level internal error (e.g., KV load failure).
            Invariant: always converted to 500 Internal Server Error.
    repetition - repetitive token pattern detected (hallucination)

    """

    STOP = 0
    LENGTH = 1
    ABORT = 2
    ERROR = 3
    REPETITION = 4

    def __str__(self):
        return FINISH_REASON_STRINGS[self.value]


class EngineCoreRequest(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    request_id: str
    prompt_token_ids: list[int] | None
    mm_features: list[MultiModalFeatureSpec] | None
    sampling_params: SamplingParams | None
    pooling_params: PoolingParams | None
    arrival_time: float
    lora_request: LoRARequest | None
    cache_salt: str | None
    data_parallel_rank: int | None
    prompt_embeds: torch.Tensor | None = None

    # Index of the client, used to ensure outputs are sent back to the same
    # client for this request when scaling out the front-end.
    client_index: int = 0

    # Used in DP case to indicate which wave of requests this is expected to
    # belong to, to cover a race condition where the request is sent before
    # a wave finished notification is received.
    current_wave: int = 0
    priority: int = 0

    trace_headers: Mapping[str, str] | None = None
    resumable: bool = False

    # The user-provided request ID. This field is set internally,
    # copied from the provided request_id that's originally assigned
    # to the request_id field, see InputProcessor.assign_request_id().
    # Used in outputs and to support abort(req_id, internal=False).
    external_req_id: str | None = None

    reasoning_ended: bool | None = None

    @property
    def params(self) -> SamplingParams | PoolingParams:
        """Return the processed params (sampling or pooling)."""
        if self.sampling_params is not None:
            return self.sampling_params
        assert self.pooling_params is not None
        return self.pooling_params


class EngineCoreEventType(enum.IntEnum):
    """The type of engine core request event."""

    QUEUED = 1
    SCHEDULED = 2
    PREEMPTED = 3


class EngineCoreEvent(msgspec.Struct):
    """A timestamped engine core event associated with a request.

    The timestamp is a monotonic timestamps and is used for by the engine
    frontend to calculate intervals between engine core events. These
    timestamps should not be compared with timestamps from other processes.
    """

    type: EngineCoreEventType
    timestamp: float

    @classmethod
    def new_event(
        cls, event_type: EngineCoreEventType, timestamp: float | None = None
    ) -> "EngineCoreEvent":
        timestamp = time.monotonic() if timestamp is None else timestamp
        return cls(event_type, timestamp)


class EngineCoreOutput(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    request_id: str
    new_token_ids: list[int]

    new_logprobs: LogprobsLists | None = None
    new_prompt_logprobs_tensors: LogprobsTensors | None = None

    pooling_output: torch.Tensor | None = None

    finish_reason: FinishReason | None = None
    stop_reason: int | str | None = None
    events: list[EngineCoreEvent] | None = None
    kv_transfer_params: dict[str, Any] | None = None

    trace_headers: Mapping[str, str] | None = None
    # The number of tokens with prefix cache hits (local + external).
    num_cached_tokens: int = 0
    # The number of tokens computed remotely (original count from connector).
    num_external_computed_tokens: int = 0
    routed_experts: np.ndarray | None = None
    # The number of NaNs in logits.
    # A value greater than 0 indicates that the output is corrupted.
    num_nans_in_logits: int = 0

    @property
    def finished(self) -> bool:
        return self.finish_reason is not None


class UtilityOutput(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    call_id: int

    # Non-None implies the call failed, result should be None.
    failure_message: str | None = None
    result: UtilityResult | None = None


class EngineCoreOutputs(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    # NOTE(Nick): We could consider ways to make this more compact,
    # e.g. columnwise layout

    engine_index: int = 0

    # [num_reqs]
    outputs: list[EngineCoreOutput] = []
    scheduler_stats: SchedulerStats | None = None
    timestamp: float = 0.0

    utility_output: UtilityOutput | None = None
    finished_requests: set[str] | None = None

    # In DP case, used to signal that the current wave of requests
    # has finished and the engines are paused.
    wave_complete: int | None = None
    # In DP case, used to signal that a request was received for an
    # "old" wave, so the next wave needs to be started in other engines.
    start_wave: int | None = None

    def __post_init__(self):
        if self.timestamp == 0.0:
            self.timestamp = time.monotonic()


class EngineCoreRequestType(enum.Enum):
    """
    Request types defined as hex byte strings, so it can be sent over sockets
    without separate encoding step.
    """

    ADD = b"\x00"
    ABORT = b"\x01"
    START_DP_WAVE = b"\x02"
    UTILITY = b"\x03"
    # Sentinel used within EngineCoreProc.
    EXECUTOR_FAILED = b"\x04"
    # Sentinel to wake up input_queue.get() during shutdown.
    WAKEUP = b"\x05"


class ReconfigureDistributedRequest(msgspec.Struct):
    new_data_parallel_size: int
    new_data_parallel_rank: int
    new_data_parallel_rank_local: int
    new_data_parallel_master_ip: str
    new_data_parallel_master_port: int
    new_data_parallel_master_port_list: list[int]
    new_stateless_world_group_port_list: list[list[int]]
    new_stateless_dp_group_port_list: list[list[int]]
    new_stateless_ep_group_port_list: list[list[int]]
    new_stateless_eplb_group_port_list: list[list[int]]


class ReconfigureRankType(enum.IntEnum):
    """
    Rank type for reconfiguring distributed request.
    """

    KEEP_CURRENT_RANK = -1
    SHUTDOWN_CURRENT_RANK = -2