"""Sampling parameters for text generation.""" from enum import IntEnum from functools import cached_property from typing import Callable, List, Optional, Union import torch _SAMPLING_EPS = 1e-5 class SamplingType(IntEnum): GREEDY = 0 RANDOM = 1 BEAM = 2 LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor] """LogitsProcessor is a function that takes a list of previously generated tokens and a tensor of the logits for the next token, and returns a modified tensor of logits to sample from.""" 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. 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. 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. logprobs: Number of log probabilities to return per output token. Note that the implementation follows the OpenAI API: The return result includes the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. 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. 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. """ def __init__( self, 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, 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: int = 16, logprobs: Optional[int] = None, prompt_logprobs: Optional[int] = None, skip_special_tokens: bool = True, spaces_between_special_tokens: bool = True, logits_processors: Optional[List[LogitsProcessor]] = ( [ lambda logits_token, logits: logits, ], [ lambda logits_token, logits: logits, ], ), min_new_token: int = 0, max_new_token: int = 8192, infer_text: bool = False, eos_token: int = 0, spk_emb: str = None, start_idx: int = 0, ) -> 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.repetition_penalty = repetition_penalty self.temperature = temperature self.top_p = top_p self.top_k = top_k self.min_p = min_p self.use_beam_search = use_beam_search self.length_penalty = length_penalty self.early_stopping = early_stopping self.min_new_token = min_new_token self.max_new_token = max_new_token self.infer_text = infer_text self.eos_token = eos_token self.spk_emb = spk_emb self.start_idx = start_idx if stop is None: self.stop = [] elif isinstance(stop, str): self.stop = [stop] else: self.stop = list(stop) if stop_token_ids is None: self.stop_token_ids = [] else: self.stop_token_ids = list(stop_token_ids) self.ignore_eos = ignore_eos self.max_tokens = max_tokens self.logprobs = logprobs self.prompt_logprobs = prompt_logprobs self.skip_special_tokens = skip_special_tokens self.spaces_between_special_tokens = spaces_between_special_tokens self.logits_processors = logits_processors self.include_stop_str_in_output = include_stop_str_in_output self._verify_args() if self.use_beam_search: 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() 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 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 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 < 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}.") 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}." ) 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: if self.best_of > 1: raise ValueError( "best_of must be 1 when using greedy sampling." f"Got {self.best_of}." ) @cached_property def sampling_type(self) -> SamplingType: if self.use_beam_search: return SamplingType.BEAM # if self.temperature < _SAMPLING_EPS: # return SamplingType.GREEDY return SamplingType.RANDOM 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"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"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"max_new_token={self.max_new_token}), " f"min_new_token={self.min_new_token}), " f"infer_text={self.infer_text})" )