import numpy as np from functools import partial import random from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc from lighteval.metrics.metrics import Metrics, SampleLevelMetric, MetricCategory, MetricUseCase, ExactMatches from lighteval.metrics.dynamic_metrics import ( loglikelihood_acc_metric, multilingual_quasi_exact_match_metric, multilingual_quasi_f1_score_metric, ) from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES import lighteval.tasks.default_prompts as prompt from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( agieval_adapter, alghafa_adapter, ceval_adapter, get_m3exam_adapter, get_mkqa_adapter, sciqa_adapter, thai_exams_adapter, winogrand_adapter, xcodah_adapter, ) from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset from lighteval.tasks.templates.boolq import get_boolq_prompt_function from lighteval.tasks.templates.continuation import get_continuation_prompt_function from lighteval.tasks.templates.copa import get_copa_prompt_function from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function from lighteval.tasks.templates.multichoice import get_mcq_prompt_function from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) from lighteval.utils.language import Language from lighteval.tasks.multilingual.tasks import TASKS_TABLE as ML_TASKS_TABLE from .math_utils import parse_math_answer TASKS_TABLE = [] TASKS_TABLE.extend(ML_TASKS_TABLE) def bbh_prompt(line, task_name: str = None): return Doc( task_name=task_name, query="Question: " + line["input"] + "\nAnswer: ", choices=[line["target"]], gold_index=0, ) def prompt_math(line, task_name: str = None): return Doc( task_name=task_name, query=f"{line['problem']}\nPlease reason step by step, and put your final answer within \\boxed{{}}.\n\n", gold_index=0, choices=[f"{line['solution']}\n\n"], ) def gpqa(line, task_name: str = None): # Prompt template from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14 GPQA_QUERY_TEMPLATE = """ Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering. {Question} A) {A} B) {B} C) {C} D) {D} """.strip() gold_index = random.randint(0, 3) choices = [line["Incorrect Answer 1"], line["Incorrect Answer 2"], line["Incorrect Answer 3"]] choices.insert(gold_index, line["Correct Answer"]) query = GPQA_QUERY_TEMPLATE.format( A=choices[0], B=choices[1], C=choices[2], D=choices[3], Question=line["Question"] ) return Doc( task_name=task_name, query=query, choices=LETTER_INDICES[: len(choices)], gold_index=gold_index, instruction=query, ) arc_tasks = [ LightevalTaskConfig( name=f"arc_{formulation.name.lower()}:{subset.lower()}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["question"], "choices": line["choices"]["text"], "gold_idx": int(line["answerKey"]) - 1 if line["answerKey"].isdigit() else LETTER_INDICES.index(line["answerKey"]), }, formulation=formulation, ), suite=("custom",), hf_repo="allenai/ai2_arc", hf_subset=f"ARC-{subset}", hf_revision="210d026faf9955653af8916fad021475a3f00453", trust_dataset=True, evaluation_splits=("test",), few_shots_split="train", metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), loglikelihood_acc_metric(normalization=LogProbPMINorm()), ], ), ) for subset in ["Easy", "Challenge"] for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(arc_tasks) hellaswag_tasks = [ LightevalTaskConfig( name=f"hellaswag_{formulation.name.lower()}", suite=["custom"], prompt_function=get_hellaswag_prompt_function( language=Language.ENGLISH, adapter=lambda line: { "activity_label": line["activity_label"], "ctx_a": line["ctx_a"], "ctx_b": line["ctx_b"], "continuations": line["endings"], "gold_idx": int(line["label"]), }, formulation=formulation, ), hf_repo="Rowan/hellaswag", hf_subset="default", hf_revision="6002345709e0801764318f06bf06ce1e7d1a1fe3", evaluation_splits=["validation"], hf_avail_splits=["validation"], metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), ], ), trust_dataset=True, ) for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] ] TASKS_TABLE.extend(hellaswag_tasks) commonsense_qa_tasks = [ LightevalTaskConfig( name=f"commonsenseqa_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["question"], "choices": line["choices"]["text"], "gold_idx": line["choices"]["label"].index(line["answerKey"].strip()), }, formulation=formulation, ), suite=("custom",), hf_repo="tau/commonsense_qa", hf_subset="default", hf_revision="94630fe30dad47192a8546eb75f094926d47e155", metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), loglikelihood_acc_metric(normalization=LogProbPMINorm()), ], ), ) for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(commonsense_qa_tasks) openbook_qa_tasks = [ LightevalTaskConfig( name=f"openbookqa_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["question_stem"], "choices": line["choices"]["text"], "gold_idx": LETTER_INDICES.index(line["answerKey"]), }, formulation=formulation, ), suite=["custom"], hf_repo="allenai/openbookqa", hf_subset="main", hf_revision="388097ea7776314e93a529163e0fea805b8a6454", metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), ], ), ) for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(openbook_qa_tasks) winogrande_tasks = [ LightevalTaskConfig( name=f"winogrande_{formulation.name.lower()}", suite=("custom",), prompt_function=get_continuation_prompt_function( Language.ENGLISH, partial(winogrand_adapter, Language.ENGLISH), formulation=formulation ), hf_repo="allenai/winogrande", hf_subset="winogrande_xl", trust_dataset=True, hf_revision="85ac5b5a3b7a930e22d590176e39460400d19e41", metric=[ loglikelihood_acc_metric(normalization=None), loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), ], ) for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(winogrande_tasks) piqa_tasks = [ LightevalTaskConfig( name=f"piqa_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["goal"], "choices": [line['sol1'], line['sol2']], "gold_idx": int(line["label"]), }, formulation=formulation ), suite=["custom"], hf_repo="ybisk/piqa", hf_revision="2e8ac2dffd59bac8c3c6714948f4c551a0848bb0", hf_subset="plain_text", trust_dataset=True, metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), ], ), ) for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(piqa_tasks) MMLU_SUBSETS = ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] mmlu_tasks = [ LightevalTaskConfig( name=f"mmlu_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["question"], "choices": line["choices"], "gold_idx": int(line["answer"]), }, formulation=formulation, ), suite=("custom",), hf_repo="cais/mmlu", hf_subset=subset, hf_revision="c30699e8356da336a370243923dbaf21066bb9fe", trust_dataset=True, evaluation_splits=("test",), few_shots_split="dev", metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), loglikelihood_acc_metric(normalization=LogProbPMINorm()), ], ), ) for subset in MMLU_SUBSETS for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(mmlu_tasks) mmlu_pro_tasks = [ LightevalTaskConfig( name=f"mmlu_pro_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( Language.ENGLISH, lambda line: { "question": line["question"], "choices": line["options"], "gold_idx": line["answer_index"], }, formulation=formulation, ), suite=("custom",), hf_repo="TIGER-Lab/MMLU-Pro", hf_subset="default", hf_revision="3373e0b32277875b8db2aa555a333b78a08477ea", trust_dataset=True, evaluation_splits=("test",), few_shots_split="validation", metric=get_metrics_for_formulation( formulation, [ loglikelihood_acc_metric(normalization=LogProbTokenNorm()), loglikelihood_acc_metric(normalization=LogProbCharNorm()), loglikelihood_acc_metric(normalization=LogProbPMINorm()), ], ), ) for formulation in [ MCFFormulation(), CFFormulation(), HybridFormulation(), ] ] TASKS_TABLE.extend(mmlu_pro_tasks) gsm8k_tasks = [ LightevalTaskConfig( name="gsm8k", prompt_function=prompt.gsm8k, suite=("custom",), hf_repo="openai/gsm8k", hf_subset="main", hf_revision="e53f048856ff4f594e959d75785d2c2d37b678ee", hf_avail_splits=["train", "test"], evaluation_splits=["test"], metric=[Metrics.quasi_exact_match_gsm8k], generation_size=256, stop_sequence=["Question:", "Question"], few_shots_select="random_sampling_from_train", ) ] TASKS_TABLE.extend(gsm8k_tasks) quasi_exact_match_math = SampleLevelMetric( metric_name="qem", sample_level_fn=ExactMatches( strip_strings=True, normalize_pred=lambda text: parse_math_answer(text, "math"), normalize_gold=lambda text: parse_math_answer(text, "math") ).compute, category=MetricCategory.GENERATIVE, use_case=MetricUseCase.MATH, corpus_level_fn=np.mean, higher_is_better=True, ) GPQA_TASKS = [ LightevalTaskConfig( name="gpqa", suite=["lighteval"], prompt_function=gpqa, hf_repo="Idavidrein/gpqa", hf_subset="gpqa_main", hf_avail_splits=["train"], evaluation_splits=["train"], few_shots_split=None, few_shots_select="random_sampling", generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], trust_dataset=True, version=0, ) ] TASKS_TABLE.extend(GPQA_TASKS) MATH_TASKS = [ LightevalTaskConfig( name="math", prompt_function=prompt_math, suite=["custom"], hf_repo="HuggingFaceTB/math_tasks", hf_subset="math", hf_revision="3d34f1076f279000b9315583dcdacfd288898283", hf_avail_splits=["train", "test", "demo"], evaluation_splits=["test"], metric=[quasi_exact_match_math], generation_size=1024, stop_sequence=["\n\n"], few_shots_split="demo", few_shots_select="sequential", trust_dataset=True, ) ] TASKS_TABLE.extend(MATH_TASKS) BBH_TASKS = [ LightevalTaskConfig( name=f"bbh:{subset}", prompt_function=bbh_prompt, suite=["custom"], hf_repo="lighteval/big_bench_hard", hf_subset=subset, hf_revision="80610173426f05e6f1448f047e2db4840a7dd899", metric=[Metrics.exact_match], hf_avail_splits=["train"], # this is the only split available, obviously not used in training evaluation_splits=["train"], few_shots_split="train", trust_dataset=True, stop_sequence=["Question:", "Question"], ) for subset in [ "boolean_expressions", "causal_judgement", "date_understanding", "disambiguation_qa", "dyck_languages", "formal_fallacies", "geometric_shapes", "hyperbaton", "logical_deduction_five_objects", "logical_deduction_seven_objects", "logical_deduction_three_objects", "movie_recommendation", "multistep_arithmetic_two", "navigate", "object_counting", "penguins_in_a_table", "reasoning_about_colored_objects", "ruin_names", "salient_translation_error_detection", "snarks", "sports_understanding", "temporal_sequences", "tracking_shuffled_objects_five_objects", "tracking_shuffled_objects_seven_objects", "tracking_shuffled_objects_three_objects", "web_of_lies", "word_sorting", ] ] TASKS_TABLE.extend(BBH_TASKS) # remove pmi_norm from all tasks to save on double inference for task in TASKS_TABLE: task.metric = [metric for metric in task.metric if metric.category != MetricCategory.MULTICHOICE_PMI] if __name__ == "__main__": print(t.name for t in TASKS_TABLE) print(len(TASKS_TABLE))