eval_code_passk_repeat_dataset.py

# This config is used for pass@k evaluation with dataset repetition
# That model cannot generate multiple response for single input
from mmengine.config import read_base
from opencompass.partitioners import SizePartitioner
from opencompass.models import HuggingFaceCausalLM
from opencompass.runners import LocalRunner
from opencompass.partitioners import SizePartitioner
from opencompass.tasks import OpenICLInferTask
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator

with read_base():
    from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
    from .datasets.mbpp.mbpp_gen_1e1056 import mbpp_datasets

humaneval_datasets[0]['abbr'] = 'openai_humaneval_pass10'
humaneval_datasets[0]['num_repeats'] = 10
mbpp_datasets[0]['abbr'] = 'mbpp_pass10'
mbpp_datasets[0]['num_repeats'] = 10
mbpp_datasets[0]['type'] = MBPPDataset_V2
mbpp_datasets[0]['eval_cfg']['evaluator']['type'] = MBPPPassKEvaluator
mbpp_datasets[0]['reader_cfg']['output_column'] = 'test_column'

datasets = []
datasets += humaneval_datasets
datasets += mbpp_datasets

_meta_template = dict(
    round=[
        dict(role="HUMAN", begin="<|User|>:", end="\n"),
        dict(role="BOT", begin="<|Bot|>:", end="<eoa>\n", generate=True),
    ],
)

models = [
    dict(
        abbr="internlm-chat-7b-hf-v11",
        type=HuggingFaceCausalLM,
        path="internlm/internlm-chat-7b-v1_1",
        tokenizer_path="internlm/internlm-chat-7b-v1_1",
        tokenizer_kwargs=dict(
            padding_side="left",
            truncation_side="left",
            use_fast=False,
            trust_remote_code=True,
        ),
        max_seq_len=2048,
        meta_template=_meta_template,
        model_kwargs=dict(trust_remote_code=True, device_map="auto"),
        generation_kwargs=dict(
            do_sample=True,
            top_p=0.95,
            temperature=0.8,
        ),
        run_cfg=dict(num_gpus=1, num_procs=1),
        batch_size=8,
    )
]


infer = dict(
    partitioner=dict(type=SizePartitioner, max_task_size=600),
    runner=dict(
        type=LocalRunner, max_num_workers=16,
        task=dict(type=OpenICLInferTask)),
)