"""Sampling parameters for text generation.""" import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from loguru import logger from typing_extensions import Annotated import aphrodite.common.envs as envs _SAMPLING_EPS = 1e-5 _MAX_TEMP = 1e-2 APHRODITE_NO_DEPRECATION_WARNING = envs.APHRODITE_NO_DEPRECATION_WARNING class SamplingType(IntEnum): GREEDY = 0 RANDOM = 1 RANDOM_SEED = 2 BEAM = 3 class SamplerID(IntEnum): # Mirror these in aphrodite/modeling/layers/sampler.py # Values out of order to keep backwards compatibility # with Koboldcpp values DRY = 7 PENALTIES = 6 NO_REPEAT_NGRAM = 8 TEMPERATURE = 5 TOP_NSIGMA = 9 TOP_P_TOP_K = 0 TOP_A = 1 MIN_P = 2 TFS = 3 ETA_CUTOFF = 10 EPSILON_CUTOFF = 11 TYPICAL_P = 4 QUADRATIC = 12 XTC = 13 @classmethod def from_str(cls, value: Union[str, int]) -> "SamplerID": """Convert string or int to SamplerID enum. Args: value: String name (case-insensitive) or integer value Returns: SamplerID enum value Raises: ValueError: If value cannot be converted to SamplerID """ if isinstance(value, int): return cls(value) try: return cls[value.upper()] except KeyError as e: valid_names = [x.name for x in cls] raise ValueError( f"Invalid sampler name '{value}'. Must be one of: {valid_names}" ) from e LogitsProcessorFunc = 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.""" class SamplingParams( msgspec.Struct, omit_defaults=True, dict=True): """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 multiple additional samplers which are 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 their frequency in the generated text so far. freq_pen is applied additively while rep_pen is applied multiplicatively. Must be in [1, inf). Set to 1 to disable the effect. no_repeat_ngram_size: Size of the n-grams to prevent repeating. 1 would mean no token can appear twice. 2 would mean no pair of consecutive tokens can appear twice. 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. top_a: Float that controls the cutoff for Top-A sampling. Exact cutoff is top_a*max_prob**2. Must be in [0,inf], 0 to disable. min_p: Float that controls the cutoff for min-p sampling. Exact cutoff is min_p*max_prob. Must be in [0,1], 0 to disable. tfs: Float that controls the cumulative approximate curvature of the distribution to retain for Tail Free Sampling. Must be in (0, 1]. Set to 1 to disable eta_cutoff: Float that controls the cutoff threshold for Eta sampling (a form of entropy adaptive truncation sampling) threshold is computed as min(eta, sqrt(eta)*entropy(probs)). Specified in units of 1e-4. Set to 0 to disable epsilon_cutoff: Float that controls the cutoff threshold for Epsilon sampling (simple probability threshold truncation). Specified in units of 1e-4. Set to 0 to disable. typical_p: Float that controls the cumulative probability of tokens closest in surprise to the expected surprise to consider. Must be in (0, 1]. Set to 1 to disable. mirostat_mode: Can either be 0 (disabled) or 2 (Mirostat v2). mirostat_tau: Target "surprisal" that mirostat works towards. Range [0, inf). mirostat_eta: Rate at which mirostat updates its internal surprisal value. Range [0, inf). dynatemp_min: Minimum temperature for dynatemp sampling. Range [0, inf). dynatemp_max: Maximum temperature for dynatemp sampling. Range [0, inf). dynatemp_exponent: Exponent for dynatemp sampling. Range [0, inf). smoothing_factor: Smoothing factor for Quadratic Sampling. smoothing_curve: Smoothing curve for Quadratic (Cubic) Sampling. 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 tokens are 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. custom_token_bans: List of token IDs to ban from generating skip_special_tokens: Whether to skip special tokens in the output. defaults to true. 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). xtc_threshold: In XTC sampling, if 2 or more tokens have probability above this threshold, consider removing all but the last one. xtc_probability: Probability that the removal will actually happen. 0 disables the sampler, 1 makes it always happen. nsigma: Number of standard deviations from the maximum logit to use as a cutoff threshold. Tokens with logits below (max_logit - nsgima * std_dev) are filtered out. Higher values (e.g. 3.0) keep more tokens, lower values (e.g. 1.0) are more selective. Must be positive. 0 to disable. dry_multiplier: Float that controls the magnitude of the DRY sampling penalty. Higher values create stronger penalties against repetition. The penalty is multiplied by this value before being applied. Must be non-negative. 0 disables the sampler. dry_base: Base for the exponential growth of the DRY sampling penalty. Controls how quickly the penalty increases with longer repeated sequences. Must be greater than 1. Higher values (e.g. 2.0) create more aggressive penalties for longer repetitions. Defaults to 1.75. dry_allowed_length: Maximum number of tokens that can be repeated without incurring a DRY sampling penalty. Sequences longer than this will be penalized exponentially. Must be at least 1. Defaults to 2. dry_sequence_breaker_ids: List of token IDs that stop the matching of repeated content. These tokens will break up the input into sections where repetition is evaluated separately. Common examples are newlines, quotes, and other structural tokens. Defaults to None. dry_range: The range of tokens (input + output) to apply the DRY sampler. skew: Bias the token selection towards higher or lower probability tokens. Defaults to 0 (disabled). sampler_priority: A list of integers to control the order in which samplers are applied. """ n: int = 1 best_of: Optional[int] = None presence_penalty: float = 0.0 frequency_penalty: float = 0.0 repetition_penalty: float = 1.0 no_repeat_ngram_size: int = 0 temperature: float = 1.0 dynatemp_min: float = 0.0 dynatemp_max: float = 0.0 dynatemp_exponent: float = 1.0 temperature_last: bool = False top_p: float = 1.0 top_k: int = -1 top_a: float = 0.0 min_p: float = 0.0 tfs: float = 1.0 eta_cutoff: float = 0.0 epsilon_cutoff: float = 0.0 typical_p: float = 1.0 smoothing_factor: float = 0.0 smoothing_curve: float = 1.0 seed: Optional[int] = None use_beam_search: bool = False length_penalty: float = 1.0 early_stopping: Union[bool, str] = False stop: Union[None, 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 custom_token_bans: Optional[List[int]] = None skip_special_tokens: bool = True spaces_between_special_tokens: bool = True # Optional[List[LogitsProcessorFunc]] type. # We use Any here because the type above # is not supported by msgspec. logits_processors: Optional[Any] = None truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=1)]] = None xtc_threshold: float = 0.1 xtc_probability: float = 0 nsigma: float = 0.0 dry_multiplier: float = 0.0 dry_base: float = 1.75 dry_allowed_length: int = 2 dry_sequence_breaker_ids: List[int] = [] dry_range: int = 0 skew: float = 0.0 sampler_priority: Optional[List[int]] = [] # 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) default_values = { "n": 1, "best_of": 1, "presence_penalty": 0.0, "frequency_penalty": 0.0, "repetition_penalty": 1.0, "no_repeat_ngram_size": 0, "temperature": 1.0, "dynatemp_min": 0.0, "dynatemp_max": 0.0, "dynatemp_exponent": 1.0, "temperature_last": False, "top_p": 1.0, "top_k": -1, "top_a": 0.0, "min_p": 0.0, "tfs": 1.0, "eta_cutoff": 0.0, "epsilon_cutoff": 0.0, "typical_p": 1.0, "smoothing_factor": 0.0, "smoothing_curve": 1.0, "seed": None, "use_beam_search": False, "length_penalty": 1.0, "early_stopping": False, "stop": [], "stop_token_ids": [], "ignore_eos": False, "max_tokens": 16, "min_tokens": 0, "logprobs": None, "prompt_logprobs": None, "detokenize": True, "custom_token_bans": None, "skip_special_tokens": True, "spaces_between_special_tokens": True, "include_stop_str_in_output": False, "truncate_prompt_tokens": None, "xtc_threshold": 0.1, "xtc_probability": 0, "nsigma": 0.0, "dry_multiplier": 0.0, "dry_base": 1.75, "dry_allowed_length": 2, "dry_sequence_breaker_ids": [], "dry_range": 0, "skew": 0.0, "sampler_priority": [], } def __post_init__(self) -> None: self.best_of = self.best_of or self.n if 0 < self.temperature < _MAX_TEMP: logger.warning( f"temperature {self.temperature} is less than {_MAX_TEMP}, " "which may cause numerical errors NaN or inf in tensors. We " f"have maxed it out to {_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 APHRODITE_NO_DEPRECATION_WARNING: logger.warning( "[IMPORTANT] We plan to discontinue the support for beam " "search in the next major release. Set " "APHRODITE_NO_DEPRECATION_WARNING=1 to " "suppress this warning.") 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.top_a = 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 self.n < 1: raise ValueError(f"n must be at least 1, got {self.n}.") assert isinstance(self.best_of, int) 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.repetition_penalty < 1.0: raise ValueError("repetition_penalty must be in [1, inf), 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 self.top_a < 0: raise ValueError(f"top_a must be non negative, got {self.top_a}.") if not 0.0 <= self.min_p <= 1.0: raise ValueError(f"min_p must be in [0, 1], got {self.min_p}.") if not 0.0 < self.tfs <= 1.0: raise ValueError(f"tfs must be in (0, 1], got {self.tfs}.") if self.epsilon_cutoff < 0.0 or self.epsilon_cutoff > 1000.0: raise ValueError("epsilon_cutoff must be in [0, 1000], got " f"{self.epsilon_cutoff}.") # pylint: disable=unneeded-not if not self.eta_cutoff >= 0: raise ValueError( f"eta_cutoff must be non negative, got {self.eta_cutoff}.") if not 0.0 <= self.typical_p <= 1.0: raise ValueError( f"typical_p must be in (0, 1], got {self.typical_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("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.xtc_threshold < 0.0: raise ValueError( "xtc_threshold must be non-negative, got " f"{self.xtc_threshold}.") if not 0.0 <= self.xtc_probability <= 1.0: raise ValueError( "xtc_probability must be in [0, 1], got " f"{self.xtc_probability}.") if self.nsigma < 0.0: raise ValueError( "nsigma must be non-negative, got " f"{self.nsigma}.") if self.dry_multiplier < 0.0: raise ValueError( "dry_multiplier must be non-negative, got " f"{self.dry_multiplier}.") if self.dry_base <= 1.0: raise ValueError( "dry_base must be greater than 1, got " f"{self.dry_base}.") if self.dry_allowed_length < 0: raise ValueError( "dry_allowed_length must be non-negative, got " f"{self.dry_allowed_length}.") if self.dry_range < 0: raise ValueError( "dry_range must be non-negative, got " f"{self.dry_range}.") if self.skew < 0.0: raise ValueError( "skew must be non-negative, got " f"{self.skew}.") if self.sampler_priority is not None: if not self.sampler_priority: self.sampler_priority = None return if not isinstance(self.sampler_priority, list): raise ValueError( "sampler_priority must be a list of integers or strings") try: self.sampler_priority = [ SamplerID.from_str(x) for x in self.sampler_priority ] provided_samplers = set(self.sampler_priority) except ValueError as e: raise ValueError( f"Invalid sampler ID in priority list: {e}" ) from e required_samplers = set(SamplerID) if not required_samplers.issubset(provided_samplers): missing = required_samplers - provided_samplers missing_names = [s.name for s in missing] raise ValueError( "Missing required samplers in priority list: " f"{missing_names}" ) 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}.") if self.top_p < 1.0 - _SAMPLING_EPS: 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 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: assert isinstance(self.stop_token_ids, list) 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. """ 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: repr_str = "SamplingParams(" for param, default_value in self.default_values.items(): current_value = getattr(self, param) if current_value != default_value: repr_str += f"{param}={current_value}, " repr_str = repr_str.rstrip(', ') + ")" return repr_str