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* [Update] Support OlympiadBench-Math/OmniMath/LiveMathBench-Hard with LLM Verify * Update * Update * Update DeepSeek-R1 example * Update DeepSeek-R1 example * Update DeepSeek-R1 example
213 lines
7.5 KiB
Python
213 lines
7.5 KiB
Python
# Support AIME-2024 with Repeat8
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# Support MATH-500
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# Support OlympiadBench
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# Support OmniMath
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# Support LiveMathBench-202412-Hard
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import os.path as osp
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from itertools import product
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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.partitioners import NaivePartitioner, NumWorkerPartitioner
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from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
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from opencompass.runners import LocalRunner
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from opencompass.models import (
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TurboMindModelwithChatTemplate,
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)
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#######################################################################
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# PART 1 Datasets List #
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#######################################################################
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with read_base():
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# You can comment out the datasets you don't want to evaluate
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# Datasets
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# from opencompass.configs.datasets.math.math_prm800k_500_llmverify_gen_6ff468 import math_datasets # 1 Run
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from opencompass.configs.datasets.aime2024.aime2024_llmverify_repeat8_gen_e8fcee import aime2024_datasets # 8 Run
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# from opencompass.configs.datasets.OlympiadBench.OlympiadBench_0shot_llmverify_gen_be8b13 import olympiadbench_datasets
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# from opencompass.configs.datasets.omni_math.omni_math_llmverify_gen_ccf9c0 import omnimath_datasets # 1 Run
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# from opencompass.configs.datasets.livemathbench.livemathbench_hard_custom_llmverify_gen_85d0ef import livemathbench_datasets
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# Summarizer
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from opencompass.configs.summarizers.groups.OlympiadBench import OlympiadBenchMath_summary_groups
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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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# Set LLM Verifier used for each dataset
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verifier_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', # You need to set your own judge model path
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key='sk-1234', # You need to set your own API key
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openai_api_base=[
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'http://172.30.56.1:4000/v1', # You need to set your own API base
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],
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meta_template=dict(
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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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query_per_second=16,
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batch_size=1024,
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temperature=0.001,
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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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)
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for item in datasets:
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# item['infer_cfg']['inferencer']['max_out_len'] = 32768 # You can unset this line if you want to avoid length cutoff
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if 'judge_cfg' in item['eval_cfg']['evaluator']:
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item['eval_cfg']['evaluator']['judge_cfg'] = verifier_cfg
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#######################################################################
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# PART 2 Model 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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# You can comment out the models you don't want to evaluate
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# All models use sampling mode
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dict(
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type=TurboMindModelwithChatTemplate,
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abbr='deepseek-r1-distill-qwen-7b-turbomind',
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path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B',
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engine_config=dict(session_len=32768, max_batch_size=128, tp=1),
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gen_config=dict(
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do_sample=True,
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temperature=0.6,
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top_p=0.95,
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max_new_tokens=32768),
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max_seq_len=32768,
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max_out_len=32768,
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batch_size=64,
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run_cfg=dict(num_gpus=1),
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pred_postprocessor=dict(type=extract_non_reasoning_content)
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),
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# dict(
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# type=TurboMindModelwithChatTemplate,
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# abbr='deepseek-r1-distill-qwen-14b-turbomind',
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# path='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B',
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# engine_config=dict(session_len=32768, max_batch_size=128, tp=2),
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# gen_config=dict(
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# do_sample=True,
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# temperature=0.6,
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# top_p=0.95,
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# max_new_tokens=32768),
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# max_seq_len=32768,
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# max_out_len=32768,
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# batch_size=128,
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# run_cfg=dict(num_gpus=2),
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# pred_postprocessor=dict(type=extract_non_reasoning_content)
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# ),
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# dict(
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# type=TurboMindModelwithChatTemplate,
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# abbr='deepseek-r1-distill-qwen-32b-turbomind',
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# path='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',
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# engine_config=dict(session_len=32768, max_batch_size=128, tp=4),
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# gen_config=dict(
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# do_sample=True,
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# temperature=0.6,
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# top_p=0.95,
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# max_new_tokens=16384),
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# max_seq_len=32768,
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# max_out_len=16384,
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# batch_size=128,
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# run_cfg=dict(num_gpus=4),
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# pred_postprocessor=dict(type=extract_non_reasoning_content)
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# ),
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]
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#######################################################################
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# PART 3 Inference/Evaluation #
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#######################################################################
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# Inference configuration
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infer = dict(
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partitioner=dict(
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type=NumWorkerPartitioner,
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num_worker=1
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# Similar with data-parallelism, how many workers for evaluation,
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# each worker will evaluate a part of the dataset. Total GPUs = num_worker * num_gpus_per_worker
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# For example, If you have 8 GPUs, for 7B model using 1 GPU for one instance, you can set num_worker=8
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# to max-utilize the GPUs.
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# If you have 8 GPUs, for 14B model using 2 GPUs for one instance, you can set num_worker=4
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),
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runner=dict(
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type=LocalRunner,
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task=dict(type=OpenICLInferTask)
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),
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)
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# Evaluation configuration
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eval = dict(
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partitioner=dict(
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type=NaivePartitioner, n=8
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),
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runner=dict(
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type=LocalRunner,
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task=dict(
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type=OpenICLEvalTask)
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),
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)
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#######################################################################
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# PART 4 Summarizer #
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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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summary_groups.extend([
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{
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'name': 'AIME2024-Aveage8',
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'subsets':[[f'aime2024-run{idx}', 'accuracy'] for idx in range(8)]
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},
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{
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'name': 'LiveMathBench-v202412-Hard-Aveage8',
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'subsets':[[
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f'livemathbench_hard_custom_{split}_run{run_idx}', 'accuracy']
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for split, run_idx in product(['hard_cn', 'hard_en'], range(8))
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]
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}
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])
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# Summarizer
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summarizer = dict(
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dataset_abbrs=[
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'MATH',
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# ['LiveMathBench-k1-n1', 'pass@1'],
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# ['LiveMathBench-v202412-greedy', 'G-Pass@1_0.0'],
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# ['aime2024', 'accuracy'],
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['math_prm800k_500-llmjudge', 'accuracy'],
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['AIME2024-Aveage8', 'naive_average'],
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['LiveMathBench-v202412-Hard-Aveage8', 'naive_average'],
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['OlympiadBenchMath', 'accuracy'],
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['OmniMath', 'accuracy'],
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],
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summary_groups=summary_groups,
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)
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#######################################################################
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# PART 5 Utils #
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#######################################################################
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work_dir = 'outputs/deepseek_r1_reasoning'
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