mirror of https://github.com/hpcaitech/ColossalAI
84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
# Adapted from https://github.com/ModelTC/lightllm
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"""Sampling parameters for text generation."""
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from typing import List, Optional, Union
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_SAMPLING_EPS = 1e-5
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class SamplingParams:
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def __init__(
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self,
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do_sample: bool = False,
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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, # -1 is for all
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ignore_eos: bool = False,
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max_new_tokens: int = 256,
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stop_sequences: Optional[Union[str, List[str]]] = None, # conditions to stop generation
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) -> None:
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self.do_sample = do_sample
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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.ignore_eos = ignore_eos
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self.max_new_tokens = max_new_tokens
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self.stop_sequences = stop_sequences
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if self.do_sample == False:
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self.temperature = 1.0
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self.top_p = 1.0
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self.top_k = 1
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if (
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self.temperature >= 0.0 and self.temperature < _SAMPLING_EPS
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): # temperature is too slow, change to greedy search
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self.temperature = 1.0
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self.top_k = 1
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return
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def verify(self):
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if self.presence_penalty < 0.0:
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raise ValueError(f"presence_penalty must >= 0.0, got {self.presence_penalty}")
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if self.frequency_penalty < 0.0:
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raise ValueError(f"frequency_penalty must >= 0.0, got {self.frequency_penalty}")
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if self.temperature <= 0.0:
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raise ValueError(f"temperature must > 0.0, got {self.temperature}")
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if self.top_p <= 0.0 or self.top_p > 1.0:
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raise ValueError(f"top_p must in (0.0, 1.0], 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, got {self.top_k}.")
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if self.max_new_tokens < 1:
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raise ValueError(f"max_new_tokens must be at least 1 , got {self.max_new_tokens}.")
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return
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def stop_sentences_to_token_ids(self, tokenizer):
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if self.stop_sequences is None:
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self.stop_sequences = []
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else:
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if isinstance(self.stop_sequences, str):
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self.stop_sequences = [self.stop_sequences]
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new_stop_sequences = []
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for stop_str in self.stop_sequences:
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stop_str_ids = tokenizer.encode(stop_str)
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if stop_str_ids is not None and len(stop_str_ids) >= 1: # remove bos_token_id
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stop_str_ids = stop_str_ids[1:]
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if len(stop_str_ids) > 0:
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new_stop_sequences.append(stop_str_ids)
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self.stop_sequences = new_stop_sequences
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return
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def to_dict(self):
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ret = {}
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ret["do_sample"] = self.do_sample
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ret["presence_penalty"] = self.presence_penalty
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ret["frequency_penalty"] = self.frequency_penalty
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ret["temperature"] = self.temperature
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ret["top_p"] = self.top_p
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ret["top_k"] = self.top_k
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# if self.ignore_eos is not None:
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# ret["ignore_eos"] = self.ignore_eos
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return ret
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