Files
vllm/vllm/v1/request.py
2025-08-29 18:36:57 +08:00

234 lines
8.6 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import enum
import time
from functools import partial
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
from vllm.multimodal.inputs import MultiModalFeatureSpec
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams
from vllm.v1.engine import (EngineCoreEvent, EngineCoreEventType,
EngineCoreRequest, FinishReason)
from vllm.v1.structured_output.request import StructuredOutputRequest
from vllm.v1.utils import ConstantList
if TYPE_CHECKING:
from vllm.lora.request import LoRARequest
from vllm.v1.core.kv_cache_utils import BlockHash
class Request:
def __init__(
self,
request_id: str,
prompt_token_ids: list[int],
sampling_params: Optional[SamplingParams],
pooling_params: Optional[PoolingParams],
eos_token_id: Optional[int],
client_index: int = 0,
arrival_time: Optional[float] = None,
mm_features: Optional[list[MultiModalFeatureSpec]] = None,
lora_request: Optional["LoRARequest"] = None,
structured_output_request: Optional["StructuredOutputRequest"] = None,
cache_salt: Optional[str] = None,
priority: int = 0,
block_hasher: Optional[Callable[["Request"],
list["BlockHash"]]] = None,
) -> None:
self.request_id = request_id
self.client_index = client_index
self.priority = priority
self.sampling_params = sampling_params
self.pooling_params = pooling_params
# Because of LoRA, the eos token id can be different for each request.
self.eos_token_id = eos_token_id
self.lora_request = lora_request
self.structured_output_request = structured_output_request
self.arrival_time = arrival_time if arrival_time is not None else \
time.time()
self.status = RequestStatus.WAITING
self.use_structured_output = False
self.events: list[EngineCoreEvent] = []
self.stop_reason: Union[int, str, None] = None
# P/D: Connector-specific KV transfer parameters.
self.kv_transfer_params: Optional[dict[str, Any]] = None
if pooling_params is not None:
# Pooling models.
self.max_tokens = 1
elif sampling_params is not None:
# Generative models.
assert sampling_params.max_tokens is not None
self.max_tokens = sampling_params.max_tokens
if sampling_params.guided_decoding is not None:
self.status = RequestStatus.WAITING_FOR_FSM
self.use_structured_output = True
if sampling_params.extra_args is not None:
self.kv_transfer_params = \
sampling_params.extra_args.get("kv_transfer_params")
else:
raise ValueError(
"sampling_params and pooling_params can't both be unset")
self.prompt_token_ids = prompt_token_ids
self.num_prompt_tokens = len(self.prompt_token_ids)
self._output_token_ids: list[int] = []
self._all_token_ids: list[int] = self.prompt_token_ids.copy()
self.num_output_placeholders = 0 # Used in async scheduling.
self.spec_token_ids: list[int] = []
self.num_computed_tokens = 0
self.cache_salt: Optional[str] = cache_salt
# Multi-modal related
self.mm_features = mm_features or []
self.num_encoder_inputs = len(self.mm_features)
self.has_encoder_inputs = self.num_encoder_inputs > 0
# TODO(sfeng33): Remove these legacy fields after clearing out all
# references in scheduler and model runner
self.mm_positions = [f.mm_position for f in self.mm_features]
self.mm_kwargs = [f.data for f in self.mm_features]
self.mm_hashes = [f.identifier for f in self.mm_features]
# Read-only views
# Prevent directly appending to these lists since
# they should also be updated simultaneously.
self.output_token_ids = ConstantList(self._output_token_ids)
self.all_token_ids = ConstantList(self._all_token_ids)
# State
# The number of tokens with prefix cache hits.
self.num_cached_tokens = -1
# The number of NaNs in logits. A value greater than 0
# indicates that the output is corrupted
self.num_nans_in_logits = 0
self.block_hashes: list[BlockHash] = []
self.get_hash_new_full_blocks: Optional[Callable[
[], list[BlockHash]]] = None
if block_hasher is not None:
self.get_hash_new_full_blocks = partial(block_hasher, self)
self.block_hashes = self.get_hash_new_full_blocks()
@classmethod
def from_engine_core_request(
cls, request: EngineCoreRequest,
block_hasher: Optional[Callable[["Request"], list["BlockHash"]]]
) -> "Request":
return cls(
request_id=request.request_id,
client_index=request.client_index,
prompt_token_ids=request.prompt_token_ids,
mm_features=request.mm_features,
sampling_params=request.sampling_params,
pooling_params=request.pooling_params,
eos_token_id=request.eos_token_id,
arrival_time=request.arrival_time,
lora_request=request.lora_request,
structured_output_request=StructuredOutputRequest(
sampling_params=request.sampling_params) \
if request.sampling_params else None,
cache_salt=request.cache_salt,
priority=request.priority,
block_hasher=block_hasher,
)
def append_output_token_ids(
self,
token_ids: Union[int, list[int]],
) -> None:
if isinstance(token_ids, int):
self._output_token_ids.append(token_ids)
self._all_token_ids.append(token_ids)
else:
self._output_token_ids.extend(token_ids)
self._all_token_ids.extend(token_ids)
if self.get_hash_new_full_blocks is not None:
self.block_hashes.extend(self.get_hash_new_full_blocks())
@property
def is_output_corrupted(self) -> bool:
return self.num_nans_in_logits > 0
@property
def num_tokens(self) -> int:
return len(self._all_token_ids)
@property
def num_tokens_with_spec(self) -> int:
return len(self._all_token_ids) + len(self.spec_token_ids)
@property
def num_output_tokens(self) -> int:
return len(self._output_token_ids)
def is_finished(self) -> bool:
return RequestStatus.is_finished(self.status)
def get_finished_reason(self) -> Union[FinishReason, None]:
return RequestStatus.get_finished_reason(self.status)
def get_num_encoder_tokens(self, input_id: int) -> int:
assert input_id < len(self.mm_positions)
num_tokens = self.mm_positions[input_id].length
return num_tokens
def record_event(
self,
event_type: EngineCoreEventType,
timestamp: Optional[float] = None,
) -> None:
self.events.append(EngineCoreEvent.new_event(event_type, timestamp))
def take_events(self) -> Optional[list[EngineCoreEvent]]:
if not self.events:
return None
events, self.events = self.events, []
return events
class RequestStatus(enum.IntEnum):
"""Status of a request."""
WAITING = enum.auto()
WAITING_FOR_FSM = enum.auto()
WAITING_FOR_REMOTE_KVS = enum.auto()
RUNNING = enum.auto()
PREEMPTED = enum.auto()
# Note: anything after PREEMPTED will be considered
# as a finished status.
FINISHED_STOPPED = enum.auto()
FINISHED_LENGTH_CAPPED = enum.auto()
FINISHED_ABORTED = enum.auto()
FINISHED_IGNORED = enum.auto()
def __str__(self):
return self.name
@staticmethod
def is_finished(status: "RequestStatus") -> bool:
return status > RequestStatus.PREEMPTED
@staticmethod
def get_finished_reason(
status: "RequestStatus") -> Union[FinishReason, None]:
return _FINISHED_REASON_MAP.get(status)
# Mapping of finished statuses to their finish reasons.
# NOTE: The ignored requests are the requests whose prompt lengths
# are longer than the model's length cap. Therefore, the stop
# reason should also be "length" as in OpenAI API.
_FINISHED_REASON_MAP = {
RequestStatus.FINISHED_STOPPED: FinishReason.STOP,
RequestStatus.FINISHED_LENGTH_CAPPED: FinishReason.LENGTH,
RequestStatus.FINISHED_ABORTED: FinishReason.ABORT,
RequestStatus.FINISHED_IGNORED: FinishReason.LENGTH,
}