107 lines
4.0 KiB
Python
107 lines
4.0 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,
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presence_penalty: float,
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frequency_penalty: float,
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temperature: float,
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top_p: float,
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top_k: int,
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use_beam_search: bool,
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stop_token_ids: Set[int],
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max_num_steps: int,
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num_logprobs: int,
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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_num_steps < 1:
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raise ValueError(
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f"max_num_steps must be at least 1, got {max_num_steps}.")
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if num_logprobs < 0:
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raise ValueError(
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f"num_logprobs must be non-negative, got {num_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_num_steps = max_num_steps
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self.num_logprobs = num_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_num_steps={self.max_num_steps}, "
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f"num_logprobs={self.num_logprobs}")
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@classmethod
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def from_dict(cls, d: Dict) -> "SamplingParams":
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sampling_params = cls(
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n=d.pop("n", 1),
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presence_penalty=d.pop("presence_penalty", 0.0),
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frequency_penalty=d.pop("frequency_penalty", 0.0),
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temperature=d.pop("temperature", 1.0),
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top_p=d.pop("top_p", 1.0),
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top_k=d.pop("top_k", -1),
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use_beam_search=d.pop("use_beam_search", False),
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stop_token_ids=set(d.pop("stop_token_ids", set())),
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max_num_steps=d.pop("max_num_steps", 16),
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num_logprobs=d.pop("num_logprobs", 0),
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)
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if d:
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raise ValueError(f"Unrecognized keys in dict: {d.keys()}")
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return sampling_params
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