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Unverified Commit 8142f399 authored by Mo Li's avatar Mo Li Committed by GitHub
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[Feature] Upgrade the needle-in-a-haystack experiment to Needlebench (#913) 

* 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
parent 120bf8b3
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configs/datasets/needlebench/atc/atc.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.atc import NeedleBenchATCOrderedDataset
from opencompass.datasets.needlebench.atc import NeedleBenchATCDataset
from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchOriginEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
needle_num_list = list(range(2, 100, 3))
document_depth_percent_intervals = 20
repeats = 30
names_path = './data/needlebench/names.json'
needlebench_atc_datasets_zh = []
needlebench_atc_datasets_en = []
needlebench_atc_datasets_zh_ordered = []
needlebench_atc_datasets_en_ordered = []
for num_needles in needle_num_list:
# ordered English version
dataset_dict = {
'abbr': f'needlebench_atc_challenge'
f'needle_{num_needles}_en_ordered',
'type': NeedleBenchATCOrderedDataset,
'path': names_path,
'num_needles': num_needles,
'language': 'English',
'repeats': repeats,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_atc_datasets_en_ordered.append(dataset_dict)
for num_needles in needle_num_list:
# ordered Chinese version
dataset_dict = {
'abbr': f'needlebench_atc_challenge'
f'needle_{num_needles}_zh_ordered',
'type': NeedleBenchATCOrderedDataset,
'path': names_path,
'num_needles': num_needles,
'language': 'Chinese',
'repeats': repeats,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_atc_datasets_zh_ordered.append(dataset_dict)
for num_needles in needle_num_list:
# standard English version
dataset_dict = {
'abbr': f'needlebench_atc_challenge'
f'needle_{num_needles}_en',
'type': NeedleBenchATCDataset,
'path': names_path,
'num_needles': num_needles,
'language': 'English',
'repeats': repeats,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_atc_datasets_en.append(dataset_dict)
for num_needles in needle_num_list:
# standard Chinese version
dataset_dict = {
'abbr': f'needlebench_atc_challenge'
f'needle_{num_needles}_zh',
'type': NeedleBenchATCDataset,
'path': names_path,
'num_needles': num_needles,
'language': 'Chinese',
'repeats': repeats,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_atc_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_4k.needlebench import needlebench_datasets as needlebench_datasets_4k
from .needlebench_8k.needlebench import needlebench_datasets as needlebench_datasets_8k
from .needlebench_32k.needlebench import needlebench_datasets as needlebench_datasets_32k
from .needlebench_128k.needlebench import needlebench_datasets as needlebench_datasets_128k
from .needlebench_200k.needlebench import needlebench_datasets as needlebench_datasets_200k
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_128k/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_multi_reasoning import needlebench_datasets_2needle_en as needlebench_multi_2needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_en as needlebench_multi_3needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_en as needlebench_multi_4needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_en as needlebench_multi_5needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_2needle_zh as needlebench_multi_2needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_zh as needlebench_multi_3needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_zh as needlebench_multi_4needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_zh as needlebench_multi_5needle_zh_datasets
from .needlebench_single import needlebench_datasets_en as needlebench_origin_en_datasets
from .needlebench_single import needlebench_datasets_zh as needlebench_origin_zh_datasets
from .needlebench_multi_retrieval import needlebench_datasets_en as needlebench_parallel_en_datasets
from .needlebench_multi_retrieval import needlebench_datasets_zh as needlebench_parallel_zh_datasets
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_128k/needlebench_multi_reasoning.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.multi import NeedleBenchMultiDataset
from opencompass.datasets.needlebench.multi import NeedleBenchMultiEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchMultiEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000])
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
# ----------English Version----------
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needle_file_name = 'multi_needle_reasoning_en.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_en = []
language = 'English'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_en.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_en.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_en.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_en.append(dataset_dict)
# ----------Chinese Version----------
base_path = './data/needlebench'
file_list = ['zh_finance.jsonl']
needle_file_name = 'multi_needle_reasoning_zh.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_zh = []
language = 'Chinese'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_zh.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_zh.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_zh.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_128k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_128k/needlebench_multi_retrieval.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchParallelEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000])
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
depths = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_en_128k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 3000,
'guide': True,
'language': 'English',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl']
needlebench_datasets_zh = []
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_zh_128k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 200,
'guide': True,
'language': 'Chinese',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_zh.append(dataset_dict)
configs/datasets/cdme/cdme8k.py → configs/datasets/needlebench/needlebench_128k/needlebench_single.py
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.cdme.cdme import CDMEDataset from opencompass.datasets.needlebench.origin import NeedleBenchOriginDataset
from opencompass.datasets.cdme.cdme import CDMEEvaluator from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
from opencompass.datasets.cdme.cdme import cdme_postprocess from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.cdme.cdme import cdme_dataset_postprocess from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math import math
...@@ -31,37 +31,69 @@ def generate_depth_percents(intervals, interval_type): ...@@ -31,37 +31,69 @@ def generate_depth_percents(intervals, interval_type):
raise ValueError('Unsupported interval type') raise ValueError('Unsupported interval type')
cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer') needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
cdme_infer_cfg = dict( needlebench_infer_cfg = dict(
prompt_template=dict( prompt_template=dict(
type=PromptTemplate, type=PromptTemplate,
template='''{prompt}'''), template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever), retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512)) inferencer=dict(type=GenInferencer))
cdme_eval_cfg = dict( needlebench_eval_cfg = dict(
evaluator=dict(type=CDMEEvaluator), evaluator=dict(type=NeedleBenchOriginEvaluator),
pred_postprocessor=dict(type=cdme_postprocess), pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=cdme_dataset_postprocess), dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT') pred_role='BOT')
context_lengths = list(range(1000, 9000, 1000)) context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000])
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
document_depth_percent_intervals = 20 document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear" document_depth_percent_interval_type = "linear"
base_path = './data/CDME' base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_origin_en_128k',
'type': NeedleBenchOriginDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': 'English',
'needle_file_name': needle_file_name,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl'] file_list = ['zh_finance.jsonl']
cdme_datasets = [] needlebench_datasets_zh = []
needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths: for original_context_length in context_lengths:
for depth_percent in generate_depth_percents( for depth_percent in depths_list:
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = { dataset_dict = {
'abbr': f'CDME_Length{original_context_length}' 'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}', f'Depth{int(depth_percent)}_origin_zh_128k',
'type': CDMEDataset, 'type': NeedleBenchOriginDataset,
'path': base_path, 'path': base_path,
'length': original_context_length, 'length': original_context_length,
'depth': int(depth_percent), 'depth': int(depth_percent),
...@@ -71,11 +103,9 @@ for original_context_length in context_lengths: ...@@ -71,11 +103,9 @@ for original_context_length in context_lengths:
'length_buffer': 200, 'length_buffer': 200,
'guide': True, 'guide': True,
'language': 'Chinese', 'language': 'Chinese',
'needle': '\n小明最喜欢的实习的地点就是上海人工智能实验室。\n', 'needle_file_name': needle_file_name,
'retrieval_question': '小明最喜欢的实习地点是哪里？请按照' 'reader_cfg': needlebench_reader_cfg,
'“小明最喜欢的实习地点就是________。”的格式回答。', 'infer_cfg': needlebench_infer_cfg,
'reader_cfg': cdme_reader_cfg, 'eval_cfg': needlebench_eval_cfg
'infer_cfg': cdme_infer_cfg,
'eval_cfg': cdme_eval_cfg
} }
cdme_datasets.append(dataset_dict) needlebench_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_200k/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_multi_reasoning import needlebench_datasets_2needle_en as needlebench_multi_2needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_en as needlebench_multi_3needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_en as needlebench_multi_4needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_en as needlebench_multi_5needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_2needle_zh as needlebench_multi_2needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_zh as needlebench_multi_3needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_zh as needlebench_multi_4needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_zh as needlebench_multi_5needle_zh_datasets
from .needlebench_single import needlebench_datasets_en as needlebench_origin_en_datasets
from .needlebench_single import needlebench_datasets_zh as needlebench_origin_zh_datasets
from .needlebench_multi_retrieval import needlebench_datasets_en as needlebench_parallel_en_datasets
from .needlebench_multi_retrieval import needlebench_datasets_zh as needlebench_parallel_zh_datasets
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_200k/needlebench_multi_reasoning.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.multi import NeedleBenchMultiDataset
from opencompass.datasets.needlebench.multi import NeedleBenchMultiEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchMultiEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
# context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000, 144000, 160000, 176000, 192000, 200000])
context_lengths = [16000, 48000, 80000, 112000, 128000, 144000, 176000, 200000]
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
# ----------English Version----------
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needle_file_name = 'multi_needle_reasoning_en.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_en = []
language = 'English'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_en.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_en.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_en.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_en.append(dataset_dict)
# ----------Chinese Version----------
base_path = './data/needlebench'
file_list = ['zh_finance.jsonl']
needle_file_name = 'multi_needle_reasoning_zh.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_zh = []
language = 'Chinese'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_zh.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_zh.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_zh.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_200k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_200k/needlebench_multi_retrieval.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchParallelEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
# context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000, 144000, 160000, 176000, 192000, 200000])
context_lengths = list([16000, 48000, 80000, 112000, 128000, 144000, 176000, 200000])
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
depths = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_en_200k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 3000,
'guide': True,
'language': 'English',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl']
needlebench_datasets_zh = []
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_zh_200k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 200,
'guide': True,
'language': 'Chinese',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_200k/needlebench_single.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.origin import NeedleBenchOriginDataset
from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchOriginEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
# context_lengths = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000, 144000, 160000, 176000, 192000, 200000])
context_lengths = [16000, 48000, 80000, 112000, 128000, 144000, 176000, 200000]
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_origin_en_200k',
'type': NeedleBenchOriginDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': 'English',
'needle_file_name': needle_file_name,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl']
needlebench_datasets_zh = []
needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_origin_zh_200k',
'type': NeedleBenchOriginDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': 'Chinese',
'needle_file_name': needle_file_name,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_32k/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_multi_reasoning import needlebench_datasets_2needle_en as needlebench_multi_2needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_en as needlebench_multi_3needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_en as needlebench_multi_4needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_en as needlebench_multi_5needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_2needle_zh as needlebench_multi_2needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_zh as needlebench_multi_3needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_zh as needlebench_multi_4needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_zh as needlebench_multi_5needle_zh_datasets
from .needlebench_single import needlebench_datasets_en as needlebench_origin_en_datasets
from .needlebench_single import needlebench_datasets_zh as needlebench_origin_zh_datasets
from .needlebench_multi_retrieval import needlebench_datasets_en as needlebench_parallel_en_datasets
from .needlebench_multi_retrieval import needlebench_datasets_zh as needlebench_parallel_zh_datasets
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_32k/needlebench_multi_reasoning.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.multi import NeedleBenchMultiDataset
from opencompass.datasets.needlebench.multi import NeedleBenchMultiEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchMultiEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list([9000, 13000, 17000, 21000, 25000, 29000, 31000, 32000])
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
# ----------English Version----------
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needle_file_name = 'multi_needle_reasoning_en.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_en = []
language = 'English'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 3000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_en.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 3000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_en.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 3000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_en.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_en = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 3000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_en.append(dataset_dict)
# ----------Chinese Version----------
base_path = './data/needlebench'
file_list = ['zh_finance.jsonl']
needle_file_name = 'multi_needle_reasoning_zh.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_zh = []
language = 'Chinese'
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_zh.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_zh.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_zh.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_zh = []
for original_context_length in context_lengths:
for depth_percent in depths_list:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_32k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_32k/needlebench_multi_retrieval.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchParallelEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list([9000, 13000, 17000, 21000, 25000, 29000, 31000, 32000])
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
depths = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_en_32k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 3000,
'guide': True,
'language': 'English',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl']
needlebench_datasets_zh = []
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_zh_32k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 200,
'guide': True,
'language': 'Chinese',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_zh.append(dataset_dict)
configs/datasets/cdme/multi_needle/cdme8k_cot2_italy.py → configs/datasets/needlebench/needlebench_32k/needlebench_single.py
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.cdme.cdme_multi import CDMEDataset from opencompass.datasets.needlebench.origin import NeedleBenchOriginDataset
from opencompass.datasets.cdme.cdme_multi import CDMEEvaluator from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
from opencompass.datasets.cdme.cdme_multi import cdme_postprocess from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.cdme.cdme_multi import cdme_dataset_postprocess from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math import math
...@@ -31,46 +31,69 @@ def generate_depth_percents(intervals, interval_type): ...@@ -31,46 +31,69 @@ def generate_depth_percents(intervals, interval_type):
raise ValueError('Unsupported interval type') raise ValueError('Unsupported interval type')
cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer') needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
cdme_infer_cfg = dict( needlebench_infer_cfg = dict(
prompt_template=dict( prompt_template=dict(
type=PromptTemplate, type=PromptTemplate,
template='''{prompt}'''), template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever), retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512)) inferencer=dict(type=GenInferencer))
cdme_eval_cfg = dict( needlebench_eval_cfg = dict(
evaluator=dict(type=CDMEEvaluator), evaluator=dict(type=NeedleBenchOriginEvaluator),
pred_postprocessor=dict(type=cdme_postprocess), pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=cdme_dataset_postprocess), dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT') pred_role='BOT')
context_lengths = list(range(1000, 9000, 1000)) context_lengths = list([9000, 13000, 17000, 21000, 25000, 29000, 31000, 32000])
depths_list = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
document_depth_percent_intervals = 20 document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear" document_depth_percent_interval_type = "linear"
base_path = './data/CDME' base_path = './data/needlebench'
file_list = ['zh_finance.jsonl'] file_list = ['PaulGrahamEssays.jsonl']
cdme_datasets = [] needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
needles = ['\n意大利的佛罗伦萨有一家名为"La Giostra"的餐馆，是整个佛罗伦萨中排行第一的餐馆。\n', for original_context_length in context_lengths:
'"La Giostra"餐馆的特色菜肴是松露奶酪通心粉。'] for depth_percent in depths_list:
retrieval_question = ("佛罗伦萨中排行第一的餐馆的特色菜肴是？" dataset_dict = {
"请按照'佛罗伦萨中排行第一的餐馆的特色菜肴是______。'的格式回答。") 'abbr': f'Length{original_context_length}'
answer = "佛罗伦萨中排行第一的餐馆的特色菜肴是松露奶酪通心粉。" f'Depth{int(depth_percent)}_origin_en_32k',
'type': NeedleBenchOriginDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 3000,
'guide': True,
'language': 'English',
'needle_file_name': needle_file_name,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
keyword = "松露奶酪通心粉" file_list = ['zh_finance.jsonl']
diff = 25 needlebench_datasets_zh = []
needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths: for original_context_length in context_lengths:
for depth_percent in generate_depth_percents( for depth_percent in depths_list:
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = { dataset_dict = {
'abbr': f'CDME_Length{original_context_length}' 'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}', f'Depth{int(depth_percent)}_origin_zh_32k',
'type': CDMEDataset, 'type': NeedleBenchOriginDataset,
'path': base_path, 'path': base_path,
'length': original_context_length, 'length': original_context_length,
'depth': int(depth_percent), 'depth': int(depth_percent),
...@@ -80,13 +103,9 @@ for original_context_length in context_lengths: ...@@ -80,13 +103,9 @@ for original_context_length in context_lengths:
'length_buffer': 200, 'length_buffer': 200,
'guide': True, 'guide': True,
'language': 'Chinese', 'language': 'Chinese',
'needles': needles, 'needle_file_name': needle_file_name,
'diff': diff, 'reader_cfg': needlebench_reader_cfg,
'retrieval_question': retrieval_question, 'infer_cfg': needlebench_infer_cfg,
'answer': answer, 'eval_cfg': needlebench_eval_cfg
'keyword': keyword,
'reader_cfg': cdme_reader_cfg,
'infer_cfg': cdme_infer_cfg,
'eval_cfg': cdme_eval_cfg
} }
cdme_datasets.append(dataset_dict) needlebench_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_4k/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_multi_reasoning import needlebench_datasets_2needle_en as needlebench_multi_2needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_en as needlebench_multi_3needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_en as needlebench_multi_4needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_en as needlebench_multi_5needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_2needle_zh as needlebench_multi_2needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_zh as needlebench_multi_3needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_zh as needlebench_multi_4needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_zh as needlebench_multi_5needle_zh_datasets
from .needlebench_single import needlebench_datasets_en as needlebench_origin_en_datasets
from .needlebench_single import needlebench_datasets_zh as needlebench_origin_zh_datasets
from .needlebench_multi_retrieval import needlebench_datasets_en as needlebench_parallel_en_datasets
from .needlebench_multi_retrieval import needlebench_datasets_zh as needlebench_parallel_zh_datasets
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_4k/needlebench_multi_reasoning.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.multi import NeedleBenchMultiDataset
from opencompass.datasets.needlebench.multi import NeedleBenchMultiEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchMultiEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list(range(1000, 5000, 1000))
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
# ----------English Version----------
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needle_file_name = 'multi_needle_reasoning_en.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_en = []
language = 'English'
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_en.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_en.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_en.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 600,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_en.append(dataset_dict)
# ----------Chinese Version----------
base_path = './data/needlebench'
file_list = ['zh_finance.jsonl']
needle_file_name = 'multi_needle_reasoning_zh.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_zh = []
language = 'Chinese'
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_zh.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_zh.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_zh.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_4k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_zh.append(dataset_dict)
configs/datasets/cdme/cdme32k.py → configs/datasets/needlebench/needlebench_4k/needlebench_multi_retrieval.py
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.cdme.cdme import CDMEDataset from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
from opencompass.datasets.cdme.cdme import CDMEEvaluator from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
from opencompass.datasets.cdme.cdme import cdme_postprocess from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.cdme.cdme import cdme_dataset_postprocess from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math import math
...@@ -31,51 +31,81 @@ def generate_depth_percents(intervals, interval_type): ...@@ -31,51 +31,81 @@ def generate_depth_percents(intervals, interval_type):
raise ValueError('Unsupported interval type') raise ValueError('Unsupported interval type')
cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer') needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
cdme_infer_cfg = dict( needlebench_infer_cfg = dict(
prompt_template=dict( prompt_template=dict(
type=PromptTemplate, type=PromptTemplate,
template='''{prompt}'''), template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever), retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512)) inferencer=dict(type=GenInferencer))
cdme_eval_cfg = dict( needlebench_eval_cfg = dict(
evaluator=dict(type=CDMEEvaluator), evaluator=dict(type=NeedleBenchParallelEvaluator),
pred_postprocessor=dict(type=cdme_postprocess), pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=cdme_dataset_postprocess), dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT') pred_role='BOT')
context_lengths = list(range(1000, 33000, 1000)) context_lengths = list(range(1000, 5000, 1000))
document_depth_percent_intervals = 20 document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear" document_depth_percent_interval_type = "linear"
base_path = './data/CDME' base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needlebench_datasets_en = []
needle_file_name = 'needles.jsonl'
depths_float = generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type)
depths = [int(depth) for depth in depths_float]
for original_context_length in context_lengths:
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'_parallel_en_4k',
'type': NeedleBenchParallelDataset,
'path': base_path,
'needle_file_name': needle_file_name,
'length': original_context_length,
'depths': depths,
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 25,
'length_buffer': 1000,
'guide': True,
'language': 'English',
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl'] file_list = ['zh_finance.jsonl']
cdme_datasets = [] needlebench_datasets_zh = []
for original_context_length in context_lengths: for original_context_length in context_lengths:
for depth_percent in generate_depth_percents( dataset_dict = {
document_depth_percent_intervals, 'abbr': f'Length{original_context_length}'
document_depth_percent_interval_type): f'_parallel_zh_4k',
dataset_dict = { 'type': NeedleBenchParallelDataset,
'abbr': f'CDME_Length{original_context_length}' 'path': base_path,
f'Depth{int(depth_percent)}', 'needle_file_name': needle_file_name,
'type': CDMEDataset, 'length': original_context_length,
'path': base_path, 'depths': depths,
'length': original_context_length, 'tokenizer_model': 'gpt-4',
'depth': int(depth_percent), 'file_list': file_list,
'tokenizer_model': 'gpt-4', 'num_repeats_per_file': 25,
'file_list': file_list, 'length_buffer': 200,
'num_repeats_per_file': 10, 'guide': True,
'length_buffer': 200, 'language': 'Chinese',
'guide': True, 'reader_cfg': needlebench_reader_cfg,
'language': 'Chinese', 'infer_cfg': needlebench_infer_cfg,
'needle': '\n小明最喜欢的实习的地点就是上海人工智能实验室。\n', 'eval_cfg': needlebench_eval_cfg
'retrieval_question': '小明最喜欢的实习地点是哪里？请按照' }
'“小明最喜欢的实习地点就是________。”的格式回答。', needlebench_datasets_zh.append(dataset_dict)
'reader_cfg': cdme_reader_cfg,
'infer_cfg': cdme_infer_cfg,
'eval_cfg': cdme_eval_cfg
}
cdme_datasets.append(dataset_dict)
configs/datasets/cdme/cdme200k.py → configs/datasets/needlebench/needlebench_4k/needlebench_single.py
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.cdme.cdme import CDMEDataset from opencompass.datasets.needlebench.origin import NeedleBenchOriginDataset
from opencompass.datasets.cdme.cdme import CDMEEvaluator from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
from opencompass.datasets.cdme.cdme import cdme_postprocess from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.cdme.cdme import cdme_dataset_postprocess from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math import math
...@@ -31,67 +31,72 @@ def generate_depth_percents(intervals, interval_type): ...@@ -31,67 +31,72 @@ def generate_depth_percents(intervals, interval_type):
raise ValueError('Unsupported interval type') raise ValueError('Unsupported interval type')
cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer') needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
cdme_infer_cfg = dict( needlebench_infer_cfg = dict(
prompt_template=dict( prompt_template=dict(
type=PromptTemplate, type=PromptTemplate,
template='''{prompt}'''), template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever), retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512)) inferencer=dict(type=GenInferencer))
cdme_eval_cfg = dict( needlebench_eval_cfg = dict(
evaluator=dict(type=CDMEEvaluator), evaluator=dict(type=NeedleBenchOriginEvaluator),
pred_postprocessor=dict(type=cdme_postprocess), pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=cdme_dataset_postprocess), dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT') pred_role='BOT')
cdme_trim_eval_cfg = dict( context_lengths = list(range(1000, 5000, 1000))
evaluator=dict(type=CDMEEvaluator, use_trim=True),
pred_postprocessor=dict(type=cdme_postprocess),
dataset_postprocessor=dict(type=cdme_dataset_postprocess),
pred_role='BOT')
#context_lengths = list(range(1000, 201000, 1000))
context_lengths = [16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000, 144000, 160000, 176000, 192000, 200000]
document_depth_percent_intervals = 20 document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear" document_depth_percent_interval_type = "linear"
base_path = './data/CDME' base_path = './data/needlebench'
file_list = ['zh_finance.jsonl'] file_list = ['PaulGrahamEssays.jsonl']
cdme_datasets = [] needlebench_datasets_en = []
cdme_trim_datasets = [] needle_file_name = 'needles.jsonl'
for original_context_length in context_lengths: for original_context_length in context_lengths:
for depth_percent in generate_depth_percents( for depth_percent in generate_depth_percents(
document_depth_percent_intervals, document_depth_percent_intervals,
document_depth_percent_interval_type): document_depth_percent_interval_type):
dataset_dict = { dataset_dict = {
'abbr': f'CDME_Length{original_context_length}' 'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}', f'Depth{int(depth_percent)}_origin_en_4k',
'type': CDMEDataset, 'type': NeedleBenchOriginDataset,
'path': base_path, 'path': base_path,
'length': original_context_length, 'length': original_context_length,
'depth': int(depth_percent), 'depth': int(depth_percent),
'tokenizer_model': 'gpt-4', 'tokenizer_model': 'gpt-4',
'file_list': file_list, 'file_list': file_list,
'num_repeats_per_file': 10, 'num_repeats_per_file': 10,
'length_buffer': 200, 'length_buffer': 600,
'guide': True, 'guide': True,
'language': 'Chinese', 'language': 'English',
'needle': '\n小明最喜欢的实习的地点就是上海人工智能实验室。\n', 'needle_file_name': needle_file_name,
'retrieval_question': '小明最喜欢的实习地点是哪里？请按照' 'reader_cfg': needlebench_reader_cfg,
'“小明最喜欢的实习地点就是________。”的格式回答。\n', 'infer_cfg': needlebench_infer_cfg,
'reader_cfg': cdme_reader_cfg, 'eval_cfg': needlebench_eval_cfg
'infer_cfg': cdme_infer_cfg,
'eval_cfg': cdme_eval_cfg
} }
cdme_datasets.append(dataset_dict) needlebench_datasets_en.append(dataset_dict)
file_list = ['zh_finance.jsonl']
needlebench_datasets_zh = []
needle_file_name = 'needles.jsonl'
trim_dataset_dict = { for original_context_length in context_lengths:
'abbr': f'CDME_Length{original_context_length}' for depth_percent in generate_depth_percents(
f'Depth{int(depth_percent)}', document_depth_percent_intervals,
'type': CDMEDataset, document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_origin_zh_4k',
'type': NeedleBenchOriginDataset,
'path': base_path, 'path': base_path,
'length': original_context_length, 'length': original_context_length,
'depth': int(depth_percent), 'depth': int(depth_percent),
...@@ -101,11 +106,9 @@ for original_context_length in context_lengths: ...@@ -101,11 +106,9 @@ for original_context_length in context_lengths:
'length_buffer': 200, 'length_buffer': 200,
'guide': True, 'guide': True,
'language': 'Chinese', 'language': 'Chinese',
'needle': '\n小明最喜欢的实习的地点就是上海人工智能实验室。\n', 'needle_file_name': needle_file_name,
'retrieval_question': '小明最喜欢的实习地点是哪里？请按照' 'reader_cfg': needlebench_reader_cfg,
'“小明最喜欢的实习地点就是________。”的格式回答。\n', 'infer_cfg': needlebench_infer_cfg,
'reader_cfg': cdme_reader_cfg, 'eval_cfg': needlebench_eval_cfg
'infer_cfg': cdme_infer_cfg,
'eval_cfg': cdme_trim_eval_cfg
} }
cdme_trim_datasets.append(trim_dataset_dict) needlebench_datasets_zh.append(dataset_dict)
configs/datasets/needlebench/needlebench_8k/needlebench.py 0 → 100644
View file @ 8142f399
from mmengine.config import read_base
with read_base():
from .needlebench_multi_reasoning import needlebench_datasets_2needle_en as needlebench_multi_2needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_en as needlebench_multi_3needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_en as needlebench_multi_4needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_en as needlebench_multi_5needle_en_datasets
from .needlebench_multi_reasoning import needlebench_datasets_2needle_zh as needlebench_multi_2needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_3needle_zh as needlebench_multi_3needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_4needle_zh as needlebench_multi_4needle_zh_datasets
from .needlebench_multi_reasoning import needlebench_datasets_5needle_zh as needlebench_multi_5needle_zh_datasets
from .needlebench_single import needlebench_datasets_en as needlebench_origin_en_datasets
from .needlebench_single import needlebench_datasets_zh as needlebench_origin_zh_datasets
from .needlebench_multi_retrieval import needlebench_datasets_en as needlebench_parallel_en_datasets
from .needlebench_multi_retrieval import needlebench_datasets_zh as needlebench_parallel_zh_datasets
needlebench_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
configs/datasets/needlebench/needlebench_8k/needlebench_multi_reasoning.py 0 → 100644
View file @ 8142f399
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.needlebench.multi import NeedleBenchMultiDataset
from opencompass.datasets.needlebench.multi import NeedleBenchMultiEvaluator
from opencompass.datasets.needlebench.origin import needlebench_postprocess
from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
import math
def logistic(x, L=100, x0=50, k=0.1):
return round(L / (1 + math.exp(-k * (x - x0))), 3)
def generate_linear_space(start, end, num):
if num == 1:
return [start]
elif num < 1:
raise ValueError("num must be at least 1.")
step = (end - start) / (num - 1)
return [start + step * i for i in range(num)]
def generate_depth_percents(intervals, interval_type):
if interval_type == 'linear':
return generate_linear_space(0, 100, intervals)
elif interval_type == 'sigmoid':
linear_space = generate_linear_space(0, 100, intervals)
return [logistic(x) for x in linear_space]
else:
raise ValueError('Unsupported interval type')
needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
needlebench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{prompt}'),
dict(role='BOT', prompt='{answer}\n'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
needlebench_eval_cfg = dict(
evaluator=dict(type=NeedleBenchMultiEvaluator),
pred_postprocessor=dict(type=needlebench_postprocess),
dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
pred_role='BOT')
context_lengths = list(range(5000, 9000, 1000))
document_depth_percent_intervals = 20
document_depth_percent_interval_type = "linear"
# ----------English Version----------
base_path = './data/needlebench'
file_list = ['PaulGrahamEssays.jsonl']
needle_file_name = 'multi_needle_reasoning_en.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_en = []
language = 'English'
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 1000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_en.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 1000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_en.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 1000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_en.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_en = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_en_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 1000,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_en.append(dataset_dict)
# ----------Chinese Version----------
base_path = './data/needlebench'
file_list = ['zh_finance.jsonl']
needle_file_name = 'multi_needle_reasoning_zh.json'
diff = 10
num_needles = 2
needlebench_datasets_2needle_zh = []
language = 'Chinese'
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_2needle_zh.append(dataset_dict)
num_needles = 3
needlebench_datasets_3needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_3needle_zh.append(dataset_dict)
num_needles = 4
needlebench_datasets_4needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_4needle_zh.append(dataset_dict)
num_needles = 5
needlebench_datasets_5needle_zh = []
for original_context_length in context_lengths:
for depth_percent in generate_depth_percents(
document_depth_percent_intervals,
document_depth_percent_interval_type):
dataset_dict = {
'abbr': f'Length{original_context_length}'
f'Depth{int(depth_percent)}_{num_needles}needle_zh_8k',
'type': NeedleBenchMultiDataset,
'path': base_path,
'length': original_context_length,
'depth': int(depth_percent),
'tokenizer_model': 'gpt-4',
'file_list': file_list,
'num_repeats_per_file': 10,
'length_buffer': 200,
'guide': True,
'language': language,
'needle_file_name': needle_file_name,
'num_needles': num_needles,
'diff': diff,
'reader_cfg': needlebench_reader_cfg,
'infer_cfg': needlebench_infer_cfg,
'eval_cfg': needlebench_eval_cfg
}
needlebench_datasets_5needle_zh.append(dataset_dict)
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