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8142f399a8
* add needlebench * simplify needlebench 32k, 128k, 200k for eval * update act prompt * fix bug in needlebench summarizer * add needlebench intro, fix summarizer * lint summarizer * fix linting error * move readme.md * update readme for needlebench * update docs of needlebench * simplify needlebench summarizers
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
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from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
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from opencompass.datasets.needlebench.origin import needlebench_postprocess
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from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
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import math
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def logistic(x, L=100, x0=50, k=0.1):
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return round(L / (1 + math.exp(-k * (x - x0))), 3)
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def generate_linear_space(start, end, num):
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if num == 1:
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return [start]
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elif num < 1:
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raise ValueError("num must be at least 1.")
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step = (end - start) / (num - 1)
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return [start + step * i for i in range(num)]
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def generate_depth_percents(intervals, interval_type):
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if interval_type == 'linear':
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return generate_linear_space(0, 100, intervals)
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elif interval_type == 'sigmoid':
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linear_space = generate_linear_space(0, 100, intervals)
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return [logistic(x) for x in linear_space]
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else:
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raise ValueError('Unsupported interval type')
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needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
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needlebench_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(role='HUMAN', prompt='{prompt}'),
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dict(role='BOT', prompt='{answer}\n'),
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]
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)
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer))
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needlebench_eval_cfg = dict(
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evaluator=dict(type=NeedleBenchParallelEvaluator),
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pred_postprocessor=dict(type=needlebench_postprocess),
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dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
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pred_role='BOT')
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context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000])
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document_depth_percent_intervals = 20
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document_depth_percent_interval_type = "linear"
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base_path = './data/needlebench'
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file_list = ['PaulGrahamEssays.jsonl']
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needlebench_datasets_en = []
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needle_file_name = 'needles.jsonl'
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depths = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
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for original_context_length in context_lengths:
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dataset_dict = {
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'abbr': f'Length{original_context_length}'
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f'_parallel_en_128k',
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'type': NeedleBenchParallelDataset,
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'path': base_path,
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'needle_file_name': needle_file_name,
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'length': original_context_length,
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'depths': depths,
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'tokenizer_model': 'gpt-4',
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'file_list': file_list,
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'num_repeats_per_file': 25,
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'length_buffer': 3000,
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'guide': True,
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'language': 'English',
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'reader_cfg': needlebench_reader_cfg,
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'infer_cfg': needlebench_infer_cfg,
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'eval_cfg': needlebench_eval_cfg
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}
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needlebench_datasets_en.append(dataset_dict)
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file_list = ['zh_finance.jsonl']
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needlebench_datasets_zh = []
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for original_context_length in context_lengths:
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dataset_dict = {
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'abbr': f'Length{original_context_length}'
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f'_parallel_zh_128k',
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'type': NeedleBenchParallelDataset,
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'path': base_path,
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'needle_file_name': needle_file_name,
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'length': original_context_length,
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'depths': depths,
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'tokenizer_model': 'gpt-4',
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'file_list': file_list,
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'num_repeats_per_file': 25,
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'length_buffer': 200,
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'guide': True,
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'language': 'Chinese',
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'reader_cfg': needlebench_reader_cfg,
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'infer_cfg': needlebench_infer_cfg,
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'eval_cfg': needlebench_eval_cfg
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}
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needlebench_datasets_zh.append(dataset_dict)
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