mirror of
https://github.com/deepseek-ai/DeepSeek-Prover-V1.5
synced 2024-11-22 03:17:43 +00:00
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import os
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import numpy as np
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from transformers import AutoTokenizer
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from prover.utils import get_datetime, load_jsonl_objects, MODEL_FORMAT
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class SamplingAlgorithmBase(object):
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def __init__(self, scheduler, tokenizer_path, process_print, cfg, **kwargs):
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os.environ['TOKENIZERS_PARALLELISM'] = 'false'
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self.scheduler = scheduler
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self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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self.process_print = process_print
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self.cfg = cfg
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self.max_tokens = cfg.max_tokens
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self.few_shot_dataset = cfg.get('few_shot_dataset', None)
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if self.few_shot_dataset is not None:
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self.few_shot_dataset = load_jsonl_objects(self.few_shot_dataset)
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self.few_shot_num = cfg.get('few_shot_num', 3)
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self.few_shot_func = MODEL_FORMAT[cfg.mode]['few_shot']
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self.log_interval = cfg.get('log_interval', 32)
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@property
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def algorithm_name(self):
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return self.__class__.__name__
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def _post_sample_info(self, **kwargs):
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return dict(
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algorithm=self.algorithm_name,
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datetime=get_datetime(),
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**kwargs,
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)
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def _encode_length(self, code):
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return len(self.tokenizer.encode(code))
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def _preprocess_data(self, input_data):
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if self.few_shot_dataset is None or self.few_shot_num == 0:
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return input_data
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return {
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**input_data,
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'_extra_header': ''.join([
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self.few_shot_func(self.few_shot_dataset[idx])
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for idx in np.random.choice([
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_idx for _idx, _data in enumerate(self.few_shot_dataset)
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if _data['name'] != input_data['name']
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], size=self.few_shot_num, replace=False)
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] + [input_data.get('_extra_header', str())]),
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}
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def sample(self, **kwargs):
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raise NotImplementedError |