vllm/cacheflow/sampling_params.py

"""Sampling parameters for text generation."""
from typing import List, Optional, Union


class SamplingParams:
    """Sampling parameters for text generation.

    Overall, we follow the sampling parameters from the OpenAI text completion
    API (https://platform.openai.com/docs/api-reference/completions/create).
    In addition, we support beam search, which is not supported by OpenAI.

    Args:
        n: Number of output sequences to return for the given prompt.
        best_of: Number of output sequences that are generated from the prompt.
            From these `best_of` sequences, the top `n` sequences are returned.
            `best_of` must be greater than or equal to `n`. This is treated as
            the beam width when `use_beam_search` is True. By default, `best_of`
            is set to `n`.
        presence_penalty: Float that penalizes new tokens based on whether they
            appear in the generated text so far. Values > 0 encourage the model
            to use new tokens, while values < 0 encourage the model to repeat
            tokens.
        frequency_penalty: Float that penalizes new tokens based on their
            frequency in the generated text so far. Values > 0 encourage the
            model to use new tokens, while values < 0 encourage the model to
            repeat tokens.
        temperature: Float that controls the randomness of the sampling. Lower
            values make the model more deterministic, while higher values make
            the model more random. Zero means greedy sampling.
        top_p: Float that controls the cumulative probability of the top tokens
            to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
        top_k: Integer that controls the number of top tokens to consider. Set
            to -1 to consider all tokens.
        use_beam_search: Whether to use beam search instead of sampling.
        stop: List of strings that stop the generation when they are generated.
            The returned output will not contain the stop strings.
        ignore_eos: Whether to ignore the EOS token and continue generating
            tokens after the EOS token is generated.
        max_tokens: Maximum number of tokens to generate per output sequence.
        logprobs: Number of log probabilities to return per output token.
    """

    def __init__(
        self,
        n: int = 1,
        best_of: Optional[int] = None,
        presence_penalty: float = 0.0,
        frequency_penalty: float = 0.0,
        temperature: float = 1.0,
        top_p: float = 1.0,
        top_k: int = -1,
        use_beam_search: bool = False,
        stop: Union[str, List[str]] = [],
        ignore_eos: bool = False,
        max_tokens: int = 16,
        logprobs: Optional[int] = None,
    ) -> None:
        self.n = n
        self.best_of = best_of if best_of is not None else n
        self.presence_penalty = presence_penalty
        self.frequency_penalty = frequency_penalty
        self.temperature = temperature
        self.top_p = top_p
        self.top_k = top_k
        self.use_beam_search = use_beam_search
        self.stop = [stop] if isinstance(stop, str) else list(stop)
        self.ignore_eos = ignore_eos
        self.max_tokens = max_tokens
        self.logprobs = logprobs

        self._verify_args()
        if self.use_beam_search:
            self._verity_beam_search()
        elif self.temperature == 0.0:
            # Zero temperature means greedy sampling.
            self._verify_greedy_sampling()

    def _verify_args(self) -> None:
        if self.n < 1:
            raise ValueError(f"n must be at least 1, got {self.n}.")
        if self.best_of < self.n:
            raise ValueError(f"best_of must be greater than or equal to n, "
                             f"got n={self.n} and best_of={self.best_of}.")
        if not -2.0 <= self.presence_penalty <= 2.0:
            raise ValueError("presence_penalty must be in [-2, 2], got "
                             f"{self.presence_penalty}.")
        if not -2.0 <= self.frequency_penalty <= 2.0:
            raise ValueError("frequency_penalty must be in [-2, 2], got "
                             f"{self.frequency_penalty}.")
        if self.temperature < 0.0:
            raise ValueError(
                f"temperature must be non-negative, got {self.temperature}.")
        if not 0.0 < self.top_p <= 1.0:
            raise ValueError(f"top_p must be in (0, 1], got {self.top_p}.")
        if self.top_k < -1 or self.top_k == 0:
            raise ValueError(f"top_k must be -1 (disable), or at least 1, "
                             f"got {self.top_k}.")
        if self.max_tokens < 1:
            raise ValueError(
                f"max_tokens must be at least 1, got {self.max_tokens}.")
        if self.logprobs is not None and self.logprobs < 0:
            raise ValueError(
                f"logprobs must be non-negative, got {self.logprobs}.")

    def _verity_beam_search(self) -> None:
        if self.best_of == 1:
            raise ValueError("best_of must be greater than 1 when using beam "
                             f"search. Got {self.best_of}.")
        if self.temperature > 0.0:
            raise ValueError("temperature must be 0 when using beam search.")
        if self.top_p < 1.0:
            raise ValueError("top_p must be 1 when using beam search.")
        if self.top_k != -1:
            raise ValueError("top_k must be -1 when using beam search.")

    def _verify_greedy_sampling(self) -> None:
        if self.best_of > 1:
            raise ValueError("best_of must be 1 when using greedy sampling."
                             f"Got {self.best_of}.")
        if self.top_p < 1.0:
            raise ValueError("top_p must be 1 when using greedy sampling.")
        if self.top_k != -1:
            raise ValueError("top_k must be -1 when using greedy sampling.")

    def __repr__(self) -> str:
        return (f"SamplingParams(n={self.n}, "
                f"best_of={self.best_of}, "
                f"presence_penalty={self.presence_penalty}, "
                f"frequency_penalty={self.frequency_penalty}, "
                f"temperature={self.temperature}, "
                f"top_p={self.top_p}, "
                f"top_k={self.top_k},"
                f"use_beam_search={self.use_beam_search}, "
                f"stop={self.stop}, "
                f"ignore_eos={self.ignore_eos}, "
                f"max_tokens={self.max_tokens}, "
                f"logprobs={self.logprobs})")