sampling_params.py

"""Sampling parameters for text generation."""
import copy
from dataclasses import dataclass
from enum import Enum, IntEnum
from functools import cached_property
from typing import Any, Callable, Dict, List, Optional, Set, Union

import msgspec
import torch
from pydantic import BaseModel
from typing_extensions import Annotated

import vllm.envs as envs
from vllm.logger import init_logger

logger = init_logger(__name__)

_SAMPLING_EPS = 1e-5
_MAX_TEMP = 1e-2


class SamplingType(IntEnum):
    GREEDY = 0
    RANDOM = 1
    RANDOM_SEED = 2
    BEAM = 3


LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor],
                        Callable[[List[int], List[int], torch.Tensor],
                                 torch.Tensor]]
"""LogitsProcessor is a function that takes a list
of previously generated tokens, the logits tensor
for the next token and, optionally, prompt tokens as a
first argument, and returns a modified tensor of logits
to sample from."""


# maybe make msgspec?
@dataclass
class GuidedDecodingParams:
    """One of these fields will be used to build a logit processor."""
    json: Optional[Union[str, Dict]] = None
    regex: Optional[str] = None
    choice: Optional[List[str]] = None
    grammar: Optional[str] = None
    json_object: Optional[bool] = None
    """These are other options that can be set"""
    backend: Optional[str] = None
    whitespace_pattern: Optional[str] = None

    @staticmethod
    def from_optional(
        json: Optional[Union[Dict, BaseModel, str]],
        regex: Optional[str] = None,
        choice: Optional[List[str]] = None,
        grammar: Optional[str] = None,
        json_object: Optional[bool] = None,
        backend: Optional[str] = None,
        whitespace_pattern: Optional[str] = None,
    ) -> "GuidedDecodingParams":
        # Extract json schemas from pydantic models
        if isinstance(json, (BaseModel, type(BaseModel))):
            json = json.model_json_schema()
        return GuidedDecodingParams(
            json=json,
            regex=regex,
            choice=choice,
            grammar=grammar,
            json_object=json_object,
            backend=backend,
            whitespace_pattern=whitespace_pattern,
        )

    def __post_init__(self):
        """Validate that some fields are mutually exclusive."""
        guide_count = sum([
            self.json is not None, self.regex is not None, self.choice
            is not None, self.grammar is not None, self.json_object is not None
        ])
        if guide_count > 1:
            raise ValueError(
                "You can only use one kind of guided decoding but multiple are "
                f"specified: {self.__dict__}")


class RequestOutputKind(Enum):
    # Return entire output so far in every RequestOutput
    CUMULATIVE = 0
    # Return only deltas in each RequestOutput
    DELTA = 1
    # Do not return intermediate RequestOuputs
    FINAL_ONLY = 2


class SamplingParams(
        msgspec.Struct,
        omit_defaults=True,  # type: ignore[call-arg]
        # required for @cached_property.
        dict=True):  # type: ignore[call-arg]
    """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.
        repetition_penalty: Float that penalizes new tokens based on whether
            they appear in the prompt and the generated text so far. Values > 1
            encourage the model to use new tokens, while values < 1 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.
        min_p: Float that represents the minimum probability for a token to be
            considered, relative to the probability of the most likely token.
            Must be in [0, 1]. Set to 0 to disable this.
        seed: Random seed to use for the generation.
        use_beam_search: Whether to use beam search instead of sampling.
        length_penalty: Float that penalizes sequences based on their length.
            Used in beam search.
        early_stopping: Controls the stopping condition for beam search. It
            accepts the following values: `True`, where the generation stops as
            soon as there are `best_of` complete candidates; `False`, where an
            heuristic is applied and the generation stops when is it very
            unlikely to find better candidates; `"never"`, where the beam search
            procedure only stops when there cannot be better candidates
            (canonical beam search algorithm).
        stop: List of strings that stop the generation when they are generated.
            The returned output will not contain the stop strings.
        stop_token_ids: List of tokens that stop the generation when they are
            generated. The returned output will contain the stop tokens unless
            the stop tokens are special tokens.
        include_stop_str_in_output: Whether to include the stop strings in
            output text. Defaults to False.
        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.
        min_tokens: Minimum number of tokens to generate per output sequence
            before EOS or stop_token_ids can be generated
        logprobs: Number of log probabilities to return per output token.
            When set to None, no probability is returned. If set to a non-None
            value, the result includes the log probabilities of the specified
            number of most likely tokens, as well as the chosen tokens.
            Note that the implementation follows the OpenAI API: The API will
            always return the log probability of the sampled token, so there
            may be up to `logprobs+1` elements in the response.
        prompt_logprobs: Number of log probabilities to return per prompt token.
        detokenize: Whether to detokenize the output. Defaults to True.
        skip_special_tokens: Whether to skip special tokens in the output.
        spaces_between_special_tokens: Whether to add spaces between special
            tokens in the output.  Defaults to True.
        logits_processors: List of functions that modify logits based on
            previously generated tokens, and optionally prompt tokens as
            a first argument.
        truncate_prompt_tokens: If set to an integer k, will use only the last k
            tokens from the prompt (i.e., left truncation). Defaults to None
            (i.e., no truncation).
        guided_decoding: If provided, the engine will construct a guided
            decoding logits processor from these parameters. Defaults to None.
        logit_bias: If provided, the engine will construct a logits processor
            that applies these logit biases. Defaults to None.
        allowed_token_ids: If provided, the engine will construct a logits
            processor which only retains scores for the given token ids.
            Defaults to None.
    """

    n: int = 1
    best_of: Optional[int] = None
    presence_penalty: float = 0.0
    frequency_penalty: float = 0.0
    repetition_penalty: float = 1.0
    temperature: float = 1.0
    top_p: float = 1.0
    top_k: int = -1
    min_p: float = 0.0
    seed: Optional[int] = None
    use_beam_search: bool = False
    length_penalty: float = 1.0
    early_stopping: Union[bool, str] = False
    stop: Optional[Union[str, List[str]]] = None
    stop_token_ids: Optional[List[int]] = None
    ignore_eos: bool = False
    max_tokens: Optional[int] = 16
    min_tokens: int = 0
    logprobs: Optional[int] = None
    prompt_logprobs: Optional[int] = None
    # NOTE: This parameter is only exposed at the engine level for now.
    # It is not exposed in the OpenAI API server, as the OpenAI API does
    # not support returning only a list of token IDs.
    detokenize: bool = True
    skip_special_tokens: bool = True
    spaces_between_special_tokens: bool = True
    # Optional[List[LogitsProcessor]] type. We use Any here because
    # Optional[List[LogitsProcessor]] type is not supported by msgspec.
    logits_processors: Optional[Any] = None
    include_stop_str_in_output: bool = False
    truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=1)]] = None
    output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE

    # The below fields are not supposed to be used as an input.
    # They are set in post_init.
    output_text_buffer_length: int = 0
    _all_stop_token_ids: Set[int] = msgspec.field(default_factory=set)

    # Fields used to construct logits processors
    guided_decoding: Optional[GuidedDecodingParams] = None
    logit_bias: Optional[Dict[int, float]] = None
    allowed_token_ids: Optional[List[int]] = None

    @staticmethod
    def from_optional(
        n: Optional[int] = 1,
        best_of: Optional[int] = None,
        presence_penalty: Optional[float] = 0.0,
        frequency_penalty: Optional[float] = 0.0,
        repetition_penalty: Optional[float] = 1.0,
        temperature: Optional[float] = 1.0,
        top_p: Optional[float] = 1.0,
        top_k: int = -1,
        min_p: float = 0.0,
        seed: Optional[int] = None,
        use_beam_search: bool = False,
        length_penalty: float = 1.0,
        early_stopping: Union[bool, str] = False,
        stop: Optional[Union[str, List[str]]] = None,
        stop_token_ids: Optional[List[int]] = None,
        include_stop_str_in_output: bool = False,
        ignore_eos: bool = False,
        max_tokens: Optional[int] = 16,
        min_tokens: int = 0,
        logprobs: Optional[int] = None,
        prompt_logprobs: Optional[int] = None,
        detokenize: bool = True,
        skip_special_tokens: bool = True,
        spaces_between_special_tokens: bool = True,
        logits_processors: Optional[List[LogitsProcessor]] = None,
        truncate_prompt_tokens: Optional[Annotated[int,
                                                   msgspec.Meta(ge=1)]] = None,
        output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE,
        guided_decoding: Optional[GuidedDecodingParams] = None,
        logit_bias: Optional[Union[Dict[int, float], Dict[str, float]]] = None,
        allowed_token_ids: Optional[List[int]] = None,
    ) -> "SamplingParams":
        if logit_bias is not None:
            logit_bias = {
                int(token): bias
                for token, bias in logit_bias.items()
            }

        return SamplingParams(
            n=1 if n is None else n,
            best_of=best_of,
            presence_penalty=0.0
            if presence_penalty is None else presence_penalty,
            frequency_penalty=0.0
            if frequency_penalty is None else frequency_penalty,
            repetition_penalty=1.0
            if repetition_penalty is None else repetition_penalty,
            temperature=1.0 if temperature is None else temperature,
            top_p=1.0 if top_p is None else top_p,
            top_k=top_k,
            min_p=min_p,
            seed=seed,
            use_beam_search=use_beam_search,
            length_penalty=length_penalty,
            early_stopping=early_stopping,
            stop=stop,
            stop_token_ids=stop_token_ids,
            include_stop_str_in_output=include_stop_str_in_output,
            ignore_eos=ignore_eos,
            max_tokens=max_tokens,
            min_tokens=min_tokens,
            logprobs=logprobs,
            prompt_logprobs=prompt_logprobs,
            detokenize=detokenize,
            skip_special_tokens=skip_special_tokens,
            spaces_between_special_tokens=spaces_between_special_tokens,
            logits_processors=logits_processors,
            truncate_prompt_tokens=truncate_prompt_tokens,
            output_kind=output_kind,
            guided_decoding=guided_decoding,
            logit_bias=logit_bias,
            allowed_token_ids=allowed_token_ids,
        )

    def __post_init__(self) -> None:
        self.best_of = self.best_of or self.n
        if 0 < self.temperature < _MAX_TEMP:
            logger.warning(
                "temperature %s is less than %s, which may cause numerical "
                "errors nan or inf in tensors. We have maxed it out to %s.",
                self.temperature, _MAX_TEMP, _MAX_TEMP)
            self.temperature = max(self.temperature, _MAX_TEMP)
        if self.seed == -1:
            self.seed = None
        else:
            self.seed = self.seed
        if self.stop is None:
            self.stop = []
        elif isinstance(self.stop, str):
            self.stop = [self.stop]
        else:
            self.stop = list(self.stop)
        if self.stop_token_ids is None:
            self.stop_token_ids = []
        else:
            self.stop_token_ids = list(self.stop_token_ids)
        self.logprobs = 1 if self.logprobs is True else self.logprobs
        self.prompt_logprobs = (1 if self.prompt_logprobs is True else
                                self.prompt_logprobs)

        # Number of characters to hold back for stop string evaluation
        # until sequence is finished.
        if self.stop and not self.include_stop_str_in_output:
            self.output_text_buffer_length = max(len(s) for s in self.stop) - 1

        self._verify_args()
        if self.use_beam_search:
            if not envs.VLLM_ALLOW_DEPRECATED_BEAM_SEARCH:
                raise ValueError(
                    "Using beam search as a sampling parameter is deprecated, and will be removed in the future release. Please use the `vllm.LLM.use_beam_search` method for dedicated beam search instead, or set the environment variable `VLLM_ALLOW_DEPRECATED_BEAM_SEARCH=1` to suppress this error. For more details, see https://github.com/vllm-project/vllm/issues/8306 ."  # noqa
                )
            self._verify_beam_search()
        else:
            self._verify_non_beam_search()
            if self.temperature < _SAMPLING_EPS:
                # Zero temperature means greedy sampling.
                self.top_p = 1.0
                self.top_k = -1
                self.min_p = 0.0
                self._verify_greedy_sampling()
        # eos_token_id is added to this by the engine
        self._all_stop_token_ids = set(self.stop_token_ids)

    def _verify_args(self) -> None:
        if not isinstance(self.n, int):
            raise ValueError(f"n must be an int, but is of "
                             f"type {type(self.n)}")
        if self.n < 1:
            raise ValueError(f"n must be at least 1, got {self.n}.")
        if not isinstance(self.best_of, int):
            raise ValueError(f'best_of must be an int, but is of '
                             f'type {type(self.best_of)}')
        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 not 0.0 < self.repetition_penalty <= 2.0:
            raise ValueError("repetition_penalty must be in (0, 2], got "
                             f"{self.repetition_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 not isinstance(self.top_k, int):
            raise TypeError(
                f"top_k must be an integer, got {type(self.top_k).__name__}")
        if not 0.0 <= self.min_p <= 1.0:
            raise ValueError("min_p must be in [0, 1], got "
                             f"{self.min_p}.")
        if self.max_tokens is not None and self.max_tokens < 1:
            raise ValueError(
                f"max_tokens must be at least 1, got {self.max_tokens}.")
        if self.min_tokens < 0:
            raise ValueError(f"min_tokens must be greater than or equal to 0, "
                             f"got {self.min_tokens}.")
        if self.max_tokens is not None and self.min_tokens > self.max_tokens:
            raise ValueError(
                f"min_tokens must be less than or equal to "
                f"max_tokens={self.max_tokens}, got {self.min_tokens}.")
        if self.logprobs is not None and self.logprobs < 0:
            raise ValueError(
                f"logprobs must be non-negative, got {self.logprobs}.")
        if self.prompt_logprobs is not None and self.prompt_logprobs < 0:
            raise ValueError(f"prompt_logprobs must be non-negative, got "
                             f"{self.prompt_logprobs}.")
        if (self.truncate_prompt_tokens is not None
                and self.truncate_prompt_tokens < 1):
            raise ValueError(f"truncate_prompt_tokens must be >= 1, "
                             f"got {self.truncate_prompt_tokens}")
        assert isinstance(self.stop, list)
        if any(not stop_str for stop_str in self.stop):
            raise ValueError("stop cannot contain an empty string.")
        if self.stop and not self.detokenize:
            raise ValueError(
                "stop strings are only supported when detokenize is True. "
                "Set detokenize=True to use stop.")
        if self.best_of != self.n and self.output_kind == (
                RequestOutputKind.DELTA):
            raise ValueError("best_of must equal n to use output_kind=DELTA")

    def _verify_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 > _SAMPLING_EPS:
            raise ValueError("temperature must be 0 when using beam search.")
        if self.top_p < 1.0 - _SAMPLING_EPS:
            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.")
        if self.early_stopping not in [True, False, "never"]:
            raise ValueError(
                f"early_stopping must be True, False, or 'never', "
                f"got {self.early_stopping}.")

    def _verify_non_beam_search(self) -> None:
        if self.early_stopping is not False:
            raise ValueError("early_stopping is not effective and must be "
                             "False when not using beam search.")
        if (self.length_penalty < 1.0 - _SAMPLING_EPS
                or self.length_penalty > 1.0 + _SAMPLING_EPS):
            raise ValueError(
                "length_penalty is not effective and must be the "
                "default value of 1.0 when not using beam search.")

    def _verify_greedy_sampling(self) -> None:
        assert isinstance(self.best_of, int)
        if self.best_of > 1:
            raise ValueError("best_of must be 1 when using greedy sampling."
                             f"Got {self.best_of}.")

    def update_from_generation_config(
            self,
            generation_config: Dict[str, Any],
            model_eos_token_id: Optional[int] = None) -> None:
        """Update if there are non-default values from generation_config"""

        if model_eos_token_id is not None:
            # Add the eos token id into the sampling_params to support
            # min_tokens processing.
            self._all_stop_token_ids.add(model_eos_token_id)

        # Update eos_token_id for generation
        if (eos_ids := generation_config.get("eos_token_id")) is not None:
            # it can be either int or list of int
            eos_ids = {eos_ids} if isinstance(eos_ids, int) else set(eos_ids)
            if model_eos_token_id is not None:
                # We don't need to include the primary eos_token_id in
                # stop_token_ids since it's handled separately for stopping
                # purposes.
                eos_ids.discard(model_eos_token_id)
            if eos_ids:
                self._all_stop_token_ids.update(eos_ids)
                if not self.ignore_eos:
                    eos_ids.update(self.stop_token_ids)
                    self.stop_token_ids = list(eos_ids)

    @cached_property
    def sampling_type(self) -> SamplingType:
        if self.use_beam_search:
            return SamplingType.BEAM
        if self.temperature < _SAMPLING_EPS:
            return SamplingType.GREEDY
        if self.seed is not None:
            return SamplingType.RANDOM_SEED
        return SamplingType.RANDOM

    @property
    def all_stop_token_ids(self) -> Set[int]:
        return self._all_stop_token_ids

    def clone(self) -> "SamplingParams":
        """Deep copy excluding LogitsProcessor objects.

        LogitsProcessor objects are excluded because they may contain an
        arbitrary, nontrivial amount of data.
        See https://github.com/vllm-project/vllm/issues/3087
        """

        logit_processor_refs = None if self.logits_processors is None else {
            id(lp): lp
            for lp in self.logits_processors
        }
        return copy.deepcopy(self, memo=logit_processor_refs)

    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"repetition_penalty={self.repetition_penalty}, "
            f"temperature={self.temperature}, "
            f"top_p={self.top_p}, "
            f"top_k={self.top_k}, "
            f"min_p={self.min_p}, "
            f"seed={self.seed}, "
            f"use_beam_search={self.use_beam_search}, "
            f"length_penalty={self.length_penalty}, "
            f"early_stopping={self.early_stopping}, "
            f"stop={self.stop}, "
            f"stop_token_ids={self.stop_token_ids}, "
            f"include_stop_str_in_output={self.include_stop_str_in_output}, "
            f"ignore_eos={self.ignore_eos}, "
            f"max_tokens={self.max_tokens}, "
            f"min_tokens={self.min_tokens}, "
            f"logprobs={self.logprobs}, "
            f"prompt_logprobs={self.prompt_logprobs}, "
            f"skip_special_tokens={self.skip_special_tokens}, "
            "spaces_between_special_tokens="
            f"{self.spaces_between_special_tokens}, "
            f"truncate_prompt_tokens={self.truncate_prompt_tokens}), "
            f"guided_decoding={self.guided_decoding}")