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* Update CascadeEvaluator * Update CascadeEvaluator * Update CascadeEvaluator * Update Config * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update
142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
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import os.path as osp
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from opencompass.models import OpenAISDK
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from mmengine.config import read_base
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from opencompass.utils.text_postprocessors import extract_non_reasoning_content
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from opencompass.runners import LocalRunner
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from opencompass.partitioners import NaivePartitioner, NumWorkerPartitioner
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from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
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with read_base():
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from opencompass.configs.datasets.aime2024.aime2024_cascade_eval_gen_5e9f4f import aime2024_datasets
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from opencompass.configs.datasets.aime2025.aime2025_cascade_eval_gen_5e9f4f import aime2025_datasets
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from opencompass.configs.datasets.math.math_500_cascade_eval_gen_6ff468 import math_datasets
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#######################################################################
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# PART 0 Meta Info #
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#######################################################################
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api_meta_template = dict(round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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],
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)
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judge_cfg = dict(
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abbr='qwen2-5-32B-Instruct',
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type=OpenAISDK,
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path='Qwen/Qwen2.5-32B-Instruct',
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key='sk-1234',
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openai_api_base=[
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'http://x.x.x.x:4000/v1',
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],
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meta_template=api_meta_template,
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query_per_second=8,
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batch_size=256,
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temperature=0.001,
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# max_completion_tokens=32768,
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tokenizer_path='gpt-4o-2024-05-13',
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# verbose=True,
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max_out_len=16384,
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max_seq_len=32768,
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# max_seq_len=49152,
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mode='mid',
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retry=10
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)
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#######################################################################
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# PART 1 Datasets List #
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#######################################################################
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repeated_info = [
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(math_datasets, 4),
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(aime2024_datasets, 32),
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(aime2025_datasets, 32),
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]
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for datasets_, num in repeated_info:
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for dataset_ in datasets_:
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dataset_['n'] = num
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datasets = sum(
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(v for k, v in locals().items() if k.endswith('_datasets')),
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[],
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)
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for item in datasets:
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item['infer_cfg']['inferencer']['max_out_len'] = 32768
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try:
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if 'judge_cfg' in item['eval_cfg']['evaluator']:
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item['eval_cfg']['evaluator']['judge_cfg'] = judge_cfg
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elif'judge_cfg' in item['eval_cfg']['evaluator']['llm_evaluator']:
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item['eval_cfg']['evaluator']['llm_evaluator']['judge_cfg'] = judge_cfg
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except:
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pass
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#######################################################################
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# PART 2 Dataset Summarizer #
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#######################################################################
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summarizer = dict(
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dataset_abbrs=[
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'MATH',
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['math_prm800k_500', 'accuracy (4 runs average)'],
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['aime2024', 'accuracy (32 runs average)'],
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['aime2025', 'accuracy (32 runs average)'],
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['livemathbench_hard', 'naive_average'],
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['OlympiadBenchMath', 'accuracy'],
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['olymmath', 'naive_average'],
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],
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summary_groups = sum(
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[v for k, v in locals().items() if k.endswith('_summary_groups')], []
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),
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)
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#######################################################################
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# PART 3 Models List #
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#######################################################################
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models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
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models += [
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dict(
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abbr='Qwen_Qwen3-235B-A22B',
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type=OpenAISDK,
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path='Qwen/Qwen3-235B-A22B',
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key='sk-admin',
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openai_api_base=[
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'http://106.15.231.215:40007/v1/',
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],
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meta_template=dict(
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# begin=dict(role='SYSTEM', api_role='SYSTEM', prompt=''),
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round=[
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dict(role='HUMAN', api_role='HUMAN'),
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# XXX: all system roles are mapped to human in purpose
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dict(role='BOT', api_role='BOT', generate=True),
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]
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),
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query_per_second=16,
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batch_size=128,
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# batch_size=1,
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temperature=0.6,
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# max_completion_tokens=32768,
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tokenizer_path='gpt-4',
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# verbose=True,
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max_out_len=32768,
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max_seq_len=32768,
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pred_postprocessor=dict(type=extract_non_reasoning_content)
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),
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]
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infer = dict(
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partitioner=dict(type=NumWorkerPartitioner, num_worker=8),
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runner=dict(type=LocalRunner, task=dict(type=OpenICLInferTask)),
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)
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eval = dict(
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partitioner=dict(type=NaivePartitioner, n=8),
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runner=dict(type=LocalRunner, task=dict(type=OpenICLEvalTask)),
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)
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base_exp_dir = 'outputs/qwen3_reasoning'
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work_dir = osp.join(base_exp_dir, 'chat_objective') |