315 lines
15 KiB
Python
315 lines
15 KiB
Python
"""Sampling parameters for text generation."""
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import copy
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from enum import IntEnum
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from functools import cached_property
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from typing import Callable, List, Optional, Union
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import torch
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from pydantic import conint
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_SAMPLING_EPS = 1e-5
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class SamplingType(IntEnum):
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GREEDY = 0
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RANDOM = 1
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RANDOM_SEED = 2
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BEAM = 3
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LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor]
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"""LogitsProcessor is a function that takes a list of previously generated
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tokens and a tensor of the logits for the next token, and returns a modified
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tensor of logits to sample from."""
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class SamplingParams:
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"""Sampling parameters for text generation.
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Overall, we follow the sampling parameters from the OpenAI text completion
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API (https://platform.openai.com/docs/api-reference/completions/create).
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In addition, we support beam search, which is not supported by OpenAI.
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Args:
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n: Number of output sequences to return for the given prompt.
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best_of: Number of output sequences that are generated from the prompt.
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From these `best_of` sequences, the top `n` sequences are returned.
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`best_of` must be greater than or equal to `n`. This is treated as
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the beam width when `use_beam_search` is True. By default, `best_of`
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is set to `n`.
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presence_penalty: Float that penalizes new tokens based on whether they
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appear in the generated text so far. Values > 0 encourage the model
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to use new tokens, while values < 0 encourage the model to repeat
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tokens.
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frequency_penalty: Float that penalizes new tokens based on their
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frequency in the generated text so far. Values > 0 encourage the
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model to use new tokens, while values < 0 encourage the model to
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repeat tokens.
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repetition_penalty: Float that penalizes new tokens based on whether
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they appear in the prompt and the generated text so far. Values > 1
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encourage the model to use new tokens, while values < 1 encourage
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the model to repeat tokens.
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temperature: Float that controls the randomness of the sampling. Lower
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values make the model more deterministic, while higher values make
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the model more random. Zero means greedy sampling.
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top_p: Float that controls the cumulative probability of the top tokens
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to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
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top_k: Integer that controls the number of top tokens to consider. Set
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to -1 to consider all tokens.
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min_p: Float that represents the minimum probability for a token to be
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considered, relative to the probability of the most likely token.
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Must be in [0, 1]. Set to 0 to disable this.
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seed: Random seed to use for the generation.
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use_beam_search: Whether to use beam search instead of sampling.
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length_penalty: Float that penalizes sequences based on their length.
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Used in beam search.
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early_stopping: Controls the stopping condition for beam search. It
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accepts the following values: `True`, where the generation stops as
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soon as there are `best_of` complete candidates; `False`, where an
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heuristic is applied and the generation stops when is it very
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unlikely to find better candidates; `"never"`, where the beam search
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procedure only stops when there cannot be better candidates
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(canonical beam search algorithm).
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stop: List of strings that stop the generation when they are generated.
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The returned output will not contain the stop strings.
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stop_token_ids: List of tokens that stop the generation when they are
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generated. The returned output will contain the stop tokens unless
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the stop tokens are special tokens.
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include_stop_str_in_output: Whether to include the stop strings in
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output text. Defaults to False.
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ignore_eos: Whether to ignore the EOS token and continue generating
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tokens after the EOS token is generated.
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max_tokens: Maximum number of tokens to generate per output sequence.
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min_tokens: Minimum number of tokens to generate per output sequence
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before EOS or stop_token_ids can be generated
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logprobs: Number of log probabilities to return per output token.
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Note that the implementation follows the OpenAI API: The return
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result includes the log probabilities on the `logprobs` most likely
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tokens, as well the chosen tokens. The API will always return the
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log probability of the sampled token, so there may be up to
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`logprobs+1` elements in the response.
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prompt_logprobs: Number of log probabilities to return per prompt token.
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detokenize: Whether to detokenize the output. Defaults to True.
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skip_special_tokens: Whether to skip special tokens in the output.
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spaces_between_special_tokens: Whether to add spaces between special
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tokens in the output. Defaults to True.
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logits_processors: List of functions that modify logits based on
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previously generated tokens.
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truncate_prompt_tokens: If set to an integer k, will use only the last k
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tokens from the prompt (i.e., left truncation). Defaults to None
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(i.e., no truncation).
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"""
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def __init__(
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self,
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n: int = 1,
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best_of: Optional[int] = None,
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presence_penalty: float = 0.0,
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frequency_penalty: float = 0.0,
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repetition_penalty: float = 1.0,
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temperature: float = 1.0,
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top_p: float = 1.0,
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top_k: int = -1,
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min_p: float = 0.0,
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seed: Optional[int] = None,
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use_beam_search: bool = False,
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length_penalty: float = 1.0,
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early_stopping: Union[bool, str] = False,
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stop: Optional[Union[str, List[str]]] = None,
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stop_token_ids: Optional[List[int]] = None,
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include_stop_str_in_output: bool = False,
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ignore_eos: bool = False,
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max_tokens: Optional[int] = 16,
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min_tokens: int = 0,
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logprobs: Optional[int] = None,
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prompt_logprobs: Optional[int] = None,
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detokenize: bool = True,
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skip_special_tokens: bool = True,
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spaces_between_special_tokens: bool = True,
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logits_processors: Optional[List[LogitsProcessor]] = None,
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truncate_prompt_tokens: Optional[conint(ge=1)] = None,
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) -> None:
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self.n = n
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self.best_of = best_of if best_of is not None else n
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self.presence_penalty = presence_penalty
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self.frequency_penalty = frequency_penalty
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self.repetition_penalty = repetition_penalty
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self.temperature = temperature
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self.top_p = top_p
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self.top_k = top_k
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self.min_p = min_p
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self.seed = seed
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self.use_beam_search = use_beam_search
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self.length_penalty = length_penalty
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self.early_stopping = early_stopping
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if stop is None:
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self.stop = []
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elif isinstance(stop, str):
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self.stop = [stop]
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else:
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self.stop = list(stop)
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if stop_token_ids is None:
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self.stop_token_ids = []
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else:
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self.stop_token_ids = list(stop_token_ids)
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self.ignore_eos = ignore_eos
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self.max_tokens = max_tokens
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self.min_tokens = min_tokens
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self.logprobs = logprobs
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self.prompt_logprobs = prompt_logprobs
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# NOTE: This parameter is only exposed at the engine level for now.
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# It is not exposed in the OpenAI API server, as the OpenAI API does
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# not support returning only a list of token IDs.
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self.detokenize = detokenize
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self.skip_special_tokens = skip_special_tokens
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self.spaces_between_special_tokens = spaces_between_special_tokens
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self.logits_processors = logits_processors
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self.include_stop_str_in_output = include_stop_str_in_output
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self.truncate_prompt_tokens = truncate_prompt_tokens
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self._verify_args()
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if self.use_beam_search:
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self._verify_beam_search()
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else:
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self._verify_non_beam_search()
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if self.temperature < _SAMPLING_EPS:
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# Zero temperature means greedy sampling.
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self.top_p = 1.0
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self.top_k = -1
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self.min_p = 0.0
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self._verify_greedy_sampling()
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# injected by the engine
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self.eos_token_id = None
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def _verify_args(self) -> None:
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if self.n < 1:
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raise ValueError(f"n must be at least 1, got {self.n}.")
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if self.best_of < self.n:
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raise ValueError(f"best_of must be greater than or equal to n, "
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f"got n={self.n} and best_of={self.best_of}.")
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if not -2.0 <= self.presence_penalty <= 2.0:
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raise ValueError("presence_penalty must be in [-2, 2], got "
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f"{self.presence_penalty}.")
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if not -2.0 <= self.frequency_penalty <= 2.0:
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raise ValueError("frequency_penalty must be in [-2, 2], got "
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f"{self.frequency_penalty}.")
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if not 0.0 < self.repetition_penalty <= 2.0:
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raise ValueError("repetition_penalty must be in (0, 2], got "
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f"{self.repetition_penalty}.")
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if self.temperature < 0.0:
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raise ValueError(
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f"temperature must be non-negative, got {self.temperature}.")
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if not 0.0 < self.top_p <= 1.0:
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raise ValueError(f"top_p must be in (0, 1], got {self.top_p}.")
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if self.top_k < -1 or self.top_k == 0:
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raise ValueError(f"top_k must be -1 (disable), or at least 1, "
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f"got {self.top_k}.")
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if not 0.0 <= self.min_p <= 1.0:
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raise ValueError("min_p must be in [0, 1], got "
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f"{self.min_p}.")
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if self.max_tokens is not None and self.max_tokens < 1:
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raise ValueError(
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f"max_tokens must be at least 1, got {self.max_tokens}.")
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if self.min_tokens < 0:
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raise ValueError(f"min_tokens must be greater than or equal to 0, "
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f"got {self.min_tokens}.")
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if self.max_tokens is not None and self.min_tokens > self.max_tokens:
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raise ValueError(
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f"min_tokens must be less than or equal to "
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f"max_tokens={self.max_tokens}, got {self.min_tokens}.")
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if self.logprobs is not None and self.logprobs < 0:
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raise ValueError(
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f"logprobs must be non-negative, got {self.logprobs}.")
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if self.prompt_logprobs is not None and self.prompt_logprobs < 0:
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raise ValueError(f"prompt_logprobs must be non-negative, got "
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f"{self.prompt_logprobs}.")
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if (self.truncate_prompt_tokens is not None
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and self.truncate_prompt_tokens < 1):
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raise ValueError(f"truncate_prompt_tokens must be >= 1, "
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f"got {self.truncate_prompt_tokens}")
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if self.stop and not self.detokenize:
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raise ValueError(
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"stop strings are only supported when detokenize is True. "
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"Set detokenize=True to use stop.")
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def _verify_beam_search(self) -> None:
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if self.best_of == 1:
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raise ValueError("best_of must be greater than 1 when using beam "
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f"search. Got {self.best_of}.")
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if self.temperature > _SAMPLING_EPS:
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raise ValueError("temperature must be 0 when using beam search.")
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if self.top_p < 1.0 - _SAMPLING_EPS:
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raise ValueError("top_p must be 1 when using beam search.")
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if self.top_k != -1:
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raise ValueError("top_k must be -1 when using beam search.")
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if self.early_stopping not in [True, False, "never"]:
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raise ValueError(
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f"early_stopping must be True, False, or 'never', "
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f"got {self.early_stopping}.")
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def _verify_non_beam_search(self) -> None:
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if self.early_stopping is not False:
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raise ValueError("early_stopping is not effective and must be "
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"False when not using beam search.")
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if (self.length_penalty < 1.0 - _SAMPLING_EPS
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or self.length_penalty > 1.0 + _SAMPLING_EPS):
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raise ValueError(
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"length_penalty is not effective and must be the "
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"default value of 1.0 when not using beam search.")
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def _verify_greedy_sampling(self) -> None:
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if self.best_of > 1:
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raise ValueError("best_of must be 1 when using greedy sampling."
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f"Got {self.best_of}.")
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@cached_property
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def sampling_type(self) -> SamplingType:
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if self.use_beam_search:
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return SamplingType.BEAM
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if self.temperature < _SAMPLING_EPS:
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return SamplingType.GREEDY
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if self.seed is not None:
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return SamplingType.RANDOM_SEED
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return SamplingType.RANDOM
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def clone(self) -> "SamplingParams":
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"""Deep copy excluding LogitsProcessor objects.
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LogitsProcessor objects are excluded because they may contain an
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arbitrary, nontrivial amount of data.
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See https://github.com/vllm-project/vllm/issues/3087
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"""
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logit_processor_refs = None if self.logits_processors is None else {
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id(lp): lp
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for lp in self.logits_processors
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}
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return copy.deepcopy(self, memo=logit_processor_refs)
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def __repr__(self) -> str:
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return (
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f"SamplingParams(n={self.n}, "
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f"best_of={self.best_of}, "
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f"presence_penalty={self.presence_penalty}, "
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f"frequency_penalty={self.frequency_penalty}, "
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f"repetition_penalty={self.repetition_penalty}, "
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f"temperature={self.temperature}, "
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f"top_p={self.top_p}, "
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f"top_k={self.top_k}, "
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f"min_p={self.min_p}, "
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f"seed={self.seed}, "
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f"use_beam_search={self.use_beam_search}, "
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f"length_penalty={self.length_penalty}, "
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f"early_stopping={self.early_stopping}, "
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f"stop={self.stop}, "
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f"stop_token_ids={self.stop_token_ids}, "
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f"include_stop_str_in_output={self.include_stop_str_in_output}, "
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f"ignore_eos={self.ignore_eos}, "
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f"max_tokens={self.max_tokens}, "
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f"min_tokens={self.min_tokens}, "
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f"logprobs={self.logprobs}, "
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f"prompt_logprobs={self.prompt_logprobs}, "
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f"skip_special_tokens={self.skip_special_tokens}, "
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"spaces_between_special_tokens="
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f"{self.spaces_between_special_tokens}, "
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f"truncate_prompt_tokens={self.truncate_prompt_tokens})")
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