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vllm.beam_search

BeamSearchInstance

Source code in vllm/beam_search.py 
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class BeamSearchInstance:
    def __init__(
        self,
        prompt_tokens: list[int],
        lora_request: LoRARequest | None = None,
        logprobs: list[dict[int, Logprob]] | None = None,
        **kwargs,
    ):
        self.beams: list[BeamSearchSequence] = [
            BeamSearchSequence(
                tokens=prompt_tokens,
                logprobs=[] if logprobs is None else list(logprobs),
                lora_request=lora_request,
                **kwargs,
            )
        ]
        self.completed: list[BeamSearchSequence] = []

beams instance-attribute 

beams: list[BeamSearchSequence] = [
    BeamSearchSequence(
        tokens=prompt_tokens,
        logprobs=[] if logprobs is None else list(logprobs),
        lora_request=lora_request,
        **kwargs,
    )
]

completed instance-attribute 

completed: list[BeamSearchSequence] = []

__init__ 

__init__(
    prompt_tokens: list[int],
    lora_request: LoRARequest | None = None,
    logprobs: list[dict[int, Logprob]] | None = None,
    **kwargs,
)
Source code in vllm/beam_search.py 
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def __init__(
    self,
    prompt_tokens: list[int],
    lora_request: LoRARequest | None = None,
    logprobs: list[dict[int, Logprob]] | None = None,
    **kwargs,
):
    self.beams: list[BeamSearchSequence] = [
        BeamSearchSequence(
            tokens=prompt_tokens,
            logprobs=[] if logprobs is None else list(logprobs),
            lora_request=lora_request,
            **kwargs,
        )
    ]
    self.completed: list[BeamSearchSequence] = []

BeamSearchOutput dataclass

The output of beam search. It contains the list of the best beam search sequences. The length of the list is equal to the beam width.

Source code in vllm/beam_search.py 
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@dataclass
class BeamSearchOutput:
    """The output of beam search.
    It contains the list of the best beam search sequences.
    The length of the list is equal to the beam width.
    """

    sequences: list[BeamSearchSequence]

sequences instance-attribute 

sequences: list[BeamSearchSequence]

__init__ 

__init__(sequences: list[BeamSearchSequence]) -> None

BeamSearchSequence dataclass

A sequence for beam search. It keeps track of the tokens and the log probability of the sequence. The text field is optional and will only be filled when the sequence is about to be returned to the user.

Source code in vllm/beam_search.py 
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@dataclass
class BeamSearchSequence:
    """A sequence for beam search.
    It keeps track of the tokens and the log probability of the sequence.
    The text field is optional and will only be filled when the sequence is
    about to be returned to the user.
    """

    # The tokens include the prompt.
    tokens: list[int]
    logprobs: list[dict[int, Logprob]]
    lora_request: LoRARequest | None = None
    cum_logprob: float = 0.0
    text: str | None = None
    finish_reason: str | None = None
    stop_reason: int | str | None = None
    multi_modal_data: Optional["MultiModalDataDict"] = None
    mm_processor_kwargs: dict[str, Any] | None = None

cum_logprob class-attribute instance-attribute 

cum_logprob: float = 0.0

finish_reason class-attribute instance-attribute 

finish_reason: str | None = None

logprobs instance-attribute 

logprobs: list[dict[int, Logprob]]

lora_request class-attribute instance-attribute 

lora_request: LoRARequest | None = None

mm_processor_kwargs class-attribute instance-attribute 

mm_processor_kwargs: dict[str, Any] | None = None

multi_modal_data class-attribute instance-attribute 

multi_modal_data: Optional[MultiModalDataDict] = None

stop_reason class-attribute instance-attribute 

stop_reason: int | str | None = None

text class-attribute instance-attribute 

text: str | None = None

tokens instance-attribute 

tokens: list[int]

__init__ 

__init__(
    tokens: list[int],
    logprobs: list[dict[int, Logprob]],
    lora_request: LoRARequest | None = None,
    cum_logprob: float = 0.0,
    text: str | None = None,
    finish_reason: str | None = None,
    stop_reason: int | str | None = None,
    multi_modal_data: Optional[MultiModalDataDict] = None,
    mm_processor_kwargs: dict[str, Any] | None = None,
) -> None

create_sort_beams_key_function 

create_sort_beams_key_function(
    eos_token_id: int, length_penalty: float
)
Source code in vllm/beam_search.py 
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def create_sort_beams_key_function(eos_token_id: int, length_penalty: float):
    def sort_beams_key(x: BeamSearchSequence) -> float:
        return get_beam_search_score(
            x.tokens, x.cum_logprob, eos_token_id, length_penalty
        )

    return sort_beams_key

get_beam_search_score 

get_beam_search_score(
    tokens: list[int],
    cumulative_logprob: float,
    eos_token_id: int,
    length_penalty: float = 1.0,
) -> float

Calculate the beam search score with length penalty.

Adapted from

https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938

Source code in vllm/beam_search.py 
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def get_beam_search_score(
    tokens: list[int],
    cumulative_logprob: float,
    eos_token_id: int,
    length_penalty: float = 1.0,
) -> float:
    """Calculate the beam search score with length penalty.

    Adapted from

    https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938
    """
    seq_len = len(tokens)
    if tokens[-1] == eos_token_id:
        seq_len -= 1

    return cumulative_logprob / (seq_len**length_penalty)