89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
from typing import Dict, Set
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class SamplingParams:
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def __init__(
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self,
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n: int = 1,
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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_token_ids: Set[int] = set(),
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max_tokens: int = 16,
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logprobs: int = 0,
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) -> None:
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if n < 1:
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raise ValueError(f"n must be at least 1, got {n}.")
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if not -2.0 <= presence_penalty <= 2.0:
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raise ValueError(
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f"presence_penalty must be in [-2, 2], got {presence_penalty}.")
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if not -2.0 <= frequency_penalty <= 2.0:
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raise ValueError(
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f"frequency_penalty must be in [-2, 2], got {frequency_penalty}.")
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if temperature < 0.0:
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raise ValueError(
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f"temperature must be non-negative, got {temperature}.")
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if not 0.0 < top_p <= 1.0:
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raise ValueError(f"top_p must be in (0, 1], got {top_p}.")
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if top_k < -1 or 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 {top_k}.")
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if max_tokens < 1:
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raise ValueError(
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f"max_tokens must be at least 1, got {max_tokens}.")
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if logprobs < 0:
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raise ValueError(
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f"logprobs must be non-negative, got {logprobs}.")
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if use_beam_search:
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if n == 1:
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raise ValueError(
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"n must be greater than 1 when using beam search.")
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if temperature > 0.0:
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raise ValueError(
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"temperature must be 0 when using beam search.")
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if top_p < 1.0:
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raise ValueError(
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"top_p must be 1 when using beam search.")
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if top_k != -1:
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raise ValueError(
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"top_k must be -1 when using beam search.")
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elif temperature == 0.0:
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# Zero temperature means greedy sampling.
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if n > 1:
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raise ValueError(
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"n must be 1 when using greedy sampling.")
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if top_p < 1.0:
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raise ValueError(
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"top_p must be 1 when using greedy sampling.")
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if top_k != -1:
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raise ValueError(
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"top_k must be -1 when using greedy sampling.")
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self.n = 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_token_ids = stop_token_ids
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self.max_tokens = max_tokens
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self.logprobs = logprobs
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def __repr__(self) -> str:
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return (f"SamplingParams(n={self.n}, "
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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_token_ids={self.stop_token_ids}, "
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f"max_tokens={self.max_tokens}, "
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f"logprobs={self.logprobs}")
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