138 lines
6.3 KiB
Python
138 lines
6.3 KiB
Python
"""Sampling parameters for text generation."""
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from typing import List, Optional, Union
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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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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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use_beam_search: Whether to use beam search instead of sampling.
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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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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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logprobs: Number of log probabilities to return per output token.
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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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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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use_beam_search: bool = False,
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stop: Union[str, List[str]] = [],
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ignore_eos: bool = False,
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max_tokens: int = 16,
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logprobs: int = 0,
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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.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.use_beam_search = use_beam_search
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self.stop = [stop] if isinstance(stop, str) else list(stop)
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self.ignore_eos = ignore_eos
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self.max_tokens = max_tokens
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self.logprobs = logprobs
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self._verify_args()
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if self.use_beam_search:
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self._verity_beam_search()
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elif self.temperature == 0.0:
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# Zero temperature means greedy sampling.
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self._verify_greedy_sampling()
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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 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 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.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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def _verity_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 > 0.0:
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raise ValueError("temperature must be 0 when using beam search.")
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if self.top_p < 1.0:
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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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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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if self.top_p < 1.0:
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raise ValueError("top_p must be 1 when using greedy sampling.")
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if self.top_k != -1:
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raise ValueError("top_k must be -1 when using greedy sampling.")
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def __repr__(self) -> str:
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return (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"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"use_beam_search={self.use_beam_search}, "
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f"stop={self.stop}, "
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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"logprobs={self.logprobs})")
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