Create lighteval_tasks.py

lighteval_tasks.py

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+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))