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# ruff: noqa: F405, F403, F401 | |
""" | |
Custom evaluation tasks for lighteval | |
Do note that we ran the evals with `max_samples=1000` to speed up large evals. | |
Most custom prompt changes were in an attempt to improve signal for small models in general. | |
This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. | |
Example usage (lighteval_tasks.py is the path to this file): | |
=================== | |
accelerate launch --num_processes=1 lighteval/run_evals_accelerate.py --model_args="pretrained=HuggingFaceFW/ablation-model-fineweb-edu" \ | |
--custom_tasks "lighteval_tasks.py" --output_dir [OUTPUTPATH] --max_samples 1000 \ | |
--tasks "custom|hellaswag|0|1,custom|winogrande|0|1,custom|piqa|0|1,custom|siqa|0|1,custom|openbookqa|0|1,custom|arc:easy|0|1,custom|arc:challenge|0|1,custom|commonsense_qa|0|1,custom|mmlu:abstract_algebra|0|1,custom|mmlu:anatomy|0|1,custom|mmlu:astronomy|0|1,custom|mmlu:business_ethics|0|1,custom|mmlu:clinical_knowledge|0|1,custom|mmlu:college_biology|0|1,custom|mmlu:college_chemistry|0|1,custom|mmlu:college_computer_science|0|1,custom|mmlu:college_mathematics|0|1,custom|mmlu:college_medicine|0|1,custom|mmlu:college_physics|0|1,custom|mmlu:computer_security|0|1,custom|mmlu:conceptual_physics|0|1,custom|mmlu:econometrics|0|1,custom|mmlu:electrical_engineering|0|1,custom|mmlu:elementary_mathematics|0|1,custom|mmlu:formal_logic|0|1,custom|mmlu:global_facts|0|1,custom|mmlu:high_school_biology|0|1,custom|mmlu:high_school_chemistry|0|1,custom|mmlu:high_school_computer_science|0|1,custom|mmlu:high_school_european_history|0|1,custom|mmlu:high_school_geography|0|1,custom|mmlu:high_school_government_and_politics|0|1,custom|mmlu:high_school_macroeconomics|0|1,custom|mmlu:high_school_mathematics|0|1,custom|mmlu:high_school_microeconomics|0|1,custom|mmlu:high_school_physics|0|1,custom|mmlu:high_school_psychology|0|1,custom|mmlu:high_school_statistics|0|1,custom|mmlu:high_school_us_history|0|1,custom|mmlu:high_school_world_history|0|1,custom|mmlu:human_aging|0|1,custom|mmlu:human_sexuality|0|1,custom|mmlu:international_law|0|1,custom|mmlu:jurisprudence|0|1,custom|mmlu:logical_fallacies|0|1,custom|mmlu:machine_learning|0|1,custom|mmlu:management|0|1,custom|mmlu:marketing|0|1,custom|mmlu:medical_genetics|0|1,custom|mmlu:miscellaneous|0|1,custom|mmlu:moral_disputes|0|1,custom|mmlu:moral_scenarios|0|1,custom|mmlu:nutrition|0|1,custom|mmlu:philosophy|0|1,custom|mmlu:prehistory|0|1,custom|mmlu:professional_accounting|0|1,custom|mmlu:professional_law|0|1,custom|mmlu:professional_medicine|0|1,custom|mmlu:professional_psychology|0|1,custom|mmlu:public_relations|0|1,custom|mmlu:security_studies|0|1,custom|mmlu:sociology|0|1,custom|mmlu:us_foreign_policy|0|1,custom|mmlu:virology|0|1,custom|mmlu:world_religions|0|1" | |
=================== | |
More info here: https://github.com/huggingface/lighteval?tab=readme-ov-file#evaluate-a-model-on-extended-community-or-custom-tasks | |
For more info on differences between MMLU implementations: https://huggingface.co/blog/open-llm-leaderboard-mmlu#1001-flavors-of-mmlu | |
In particular, the default leaderboard MMLU implementation (which uses "A", "B", etc as answer targets) gives generally random results on small/non instruction tuned models. | |
Instead, we use the full MMLU answer as the target. | |
""" | |
import re | |
from typing import List, Tuple | |
from lighteval.metrics import Metrics | |
from lighteval.tasks.lighteval_task import LightevalTaskConfig | |
from lighteval.tasks.requests import Doc | |
from lighteval.tasks.tasks_prompt_formatting import LETTER_INDICES | |
_TASKS_STRINGS: List[Tuple[LightevalTaskConfig, str]] = [] | |
_TASKS: List[LightevalTaskConfig] = [] | |
## COMMON_SENSE_REASONING_TASKS ## | |
COMMON_SENSE_REASONING_TASKS = [ | |
LightevalTaskConfig( | |
name="hellaswag", | |
prompt_function="hellaswag_prompt", | |
hf_repo="hellaswag", | |
hf_subset="default", | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="winogrande", | |
prompt_function="winogrande", | |
hf_repo="winogrande", | |
hf_subset="winogrande_xl", | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="piqa", | |
prompt_function="piqa_harness", | |
hf_repo="piqa", | |
hf_subset="plain_text", | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="siqa", | |
prompt_function="siqa_prompt", | |
hf_repo="lighteval/siqa", | |
hf_subset="default", | |
hf_avail_splits=["train", "validation"], | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="openbookqa", | |
prompt_function="openbookqa", | |
hf_repo="openbookqa", | |
hf_subset="main", | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="arc:easy", | |
prompt_function="arc", | |
hf_repo="ai2_arc", | |
hf_subset="ARC-Easy", | |
evaluation_splits=["test"], | |
generation_size=1, | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="arc:challenge", | |
prompt_function="arc", | |
hf_repo="ai2_arc", | |
hf_subset="ARC-Challenge", | |
evaluation_splits=["test"], | |
generation_size=1, | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
LightevalTaskConfig( | |
name="commonsense_qa", | |
prompt_function="commonsense_qa_prompt", | |
hf_repo="commonsense_qa", | |
hf_subset="default", | |
metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], | |
), | |
] | |
def commonsense_qa_prompt(line, task_name: str = None): | |
return Doc( | |
task_name=task_name, | |
query=line["question"], | |
choices=[f" {c}" for c in line["choices"]["text"]], | |
gold_index=LETTER_INDICES.index(line["answerKey"].strip()), | |
instruction="", | |
) | |
def siqa_prompt(line, task_name: str = None): | |
return Doc( | |
task_name=task_name, | |
query=line["context"] + " " + line["question"], | |
choices=[f" {c}" for c in [line["answerA"], line["answerB"], line["answerC"]]], | |
gold_index=int(line["label"]) - 1, | |
instruction="", | |
) | |
def hellaswag_prompt(line, task_name: str = None): | |
def preprocess(text): | |
"""Comes from AiHarness""" | |
# text = text.strip() | |
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag. | |
text = text.replace(" [title]", ". ") | |
text = re.sub("\\[.*?\\]", "", text) | |
text = text.replace(" ", " ") | |
return text | |
ctx = f"{line['ctx_a']} {line['ctx_b'].capitalize()} " | |
return Doc( | |
task_name=task_name, | |
query=preprocess(line["activity_label"] + ": " + ctx), | |
choices=[" " + preprocess(ending) for ending in line["endings"]], | |
gold_index=int(line["label"]) if line["label"] != "" else -1, # -1 for test | |
# "metric": "choices_loglikelihood", | |
) | |
# 0 short for common sense | |
COMMON_SENSE_REASONING_STRING = [(t, f"custom|{t.name}|0|1") for t in COMMON_SENSE_REASONING_TASKS] | |
_TASKS_STRINGS.extend(COMMON_SENSE_REASONING_STRING) | |
_TASKS += COMMON_SENSE_REASONING_TASKS | |
## MMLU ## | |
class CustomMMLUEvaluationTask(LightevalTaskConfig): | |
def __init__( | |
self, | |
name, | |
prompt_function="mmlu_prompt", | |
hf_repo="lighteval/mmlu", | |
hf_subset=None, | |
# metric=[Metrics.loglikelihood_acc_single_token], | |
metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], | |
hf_avail_splits=None, | |
evaluation_splits=["test"], | |
few_shots_split="dev", | |
few_shots_select=None, | |
suite=None, | |
generation_size=-1, | |
stop_sequence=None, | |
output_regex=None, | |
frozen=False, | |
): | |
super().__init__( | |
name=name, | |
prompt_function=prompt_function, | |
hf_repo=hf_repo, | |
hf_subset=hf_subset, | |
metric=metric, | |
hf_avail_splits=hf_avail_splits, | |
evaluation_splits=evaluation_splits, | |
few_shots_split=few_shots_split, | |
few_shots_select=few_shots_select, | |
suite=suite, | |
generation_size=generation_size, | |
stop_sequence=stop_sequence, | |
output_regex=output_regex, | |
frozen=frozen, | |
) | |
MMLU_TASKS = [ | |
CustomMMLUEvaluationTask(name="mmlu:abstract_algebra", hf_subset="abstract_algebra"), | |
CustomMMLUEvaluationTask(name="mmlu:anatomy", hf_subset="anatomy"), | |
CustomMMLUEvaluationTask(name="mmlu:astronomy", hf_subset="astronomy"), | |
CustomMMLUEvaluationTask(name="mmlu:business_ethics", hf_subset="business_ethics"), | |
CustomMMLUEvaluationTask(name="mmlu:clinical_knowledge", hf_subset="clinical_knowledge"), | |
CustomMMLUEvaluationTask(name="mmlu:college_biology", hf_subset="college_biology"), | |
CustomMMLUEvaluationTask(name="mmlu:college_chemistry", hf_subset="college_chemistry"), | |
CustomMMLUEvaluationTask(name="mmlu:college_computer_science", hf_subset="college_computer_science"), | |
CustomMMLUEvaluationTask(name="mmlu:college_mathematics", hf_subset="college_mathematics"), | |
CustomMMLUEvaluationTask(name="mmlu:college_medicine", hf_subset="college_medicine"), | |
CustomMMLUEvaluationTask(name="mmlu:college_physics", hf_subset="college_physics"), | |
CustomMMLUEvaluationTask(name="mmlu:computer_security", hf_subset="computer_security"), | |
CustomMMLUEvaluationTask(name="mmlu:conceptual_physics", hf_subset="conceptual_physics"), | |
CustomMMLUEvaluationTask(name="mmlu:econometrics", hf_subset="econometrics"), | |
CustomMMLUEvaluationTask(name="mmlu:electrical_engineering", hf_subset="electrical_engineering"), | |
CustomMMLUEvaluationTask(name="mmlu:elementary_mathematics", hf_subset="elementary_mathematics"), | |
CustomMMLUEvaluationTask(name="mmlu:formal_logic", hf_subset="formal_logic"), | |
CustomMMLUEvaluationTask(name="mmlu:global_facts", hf_subset="global_facts"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_biology", hf_subset="high_school_biology"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_chemistry", hf_subset="high_school_chemistry"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_computer_science", hf_subset="high_school_computer_science"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_european_history", hf_subset="high_school_european_history"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_geography", hf_subset="high_school_geography"), | |
CustomMMLUEvaluationTask( | |
name="mmlu:high_school_government_and_politics", hf_subset="high_school_government_and_politics" | |
), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_macroeconomics", hf_subset="high_school_macroeconomics"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_mathematics", hf_subset="high_school_mathematics"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_microeconomics", hf_subset="high_school_microeconomics"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_physics", hf_subset="high_school_physics"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_psychology", hf_subset="high_school_psychology"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_statistics", hf_subset="high_school_statistics"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_us_history", hf_subset="high_school_us_history"), | |
CustomMMLUEvaluationTask(name="mmlu:high_school_world_history", hf_subset="high_school_world_history"), | |
CustomMMLUEvaluationTask(name="mmlu:human_aging", hf_subset="human_aging"), | |
CustomMMLUEvaluationTask(name="mmlu:human_sexuality", hf_subset="human_sexuality"), | |
CustomMMLUEvaluationTask(name="mmlu:international_law", hf_subset="international_law"), | |
CustomMMLUEvaluationTask(name="mmlu:jurisprudence", hf_subset="jurisprudence"), | |
CustomMMLUEvaluationTask(name="mmlu:logical_fallacies", hf_subset="logical_fallacies"), | |
CustomMMLUEvaluationTask(name="mmlu:machine_learning", hf_subset="machine_learning"), | |
CustomMMLUEvaluationTask(name="mmlu:management", hf_subset="management"), | |
CustomMMLUEvaluationTask(name="mmlu:marketing", hf_subset="marketing"), | |
CustomMMLUEvaluationTask(name="mmlu:medical_genetics", hf_subset="medical_genetics"), | |
CustomMMLUEvaluationTask(name="mmlu:miscellaneous", hf_subset="miscellaneous"), | |
CustomMMLUEvaluationTask(name="mmlu:moral_disputes", hf_subset="moral_disputes"), | |
CustomMMLUEvaluationTask(name="mmlu:moral_scenarios", hf_subset="moral_scenarios"), | |
CustomMMLUEvaluationTask(name="mmlu:nutrition", hf_subset="nutrition"), | |
CustomMMLUEvaluationTask(name="mmlu:philosophy", hf_subset="philosophy"), | |
CustomMMLUEvaluationTask(name="mmlu:prehistory", hf_subset="prehistory"), | |
CustomMMLUEvaluationTask(name="mmlu:professional_accounting", hf_subset="professional_accounting"), | |
CustomMMLUEvaluationTask(name="mmlu:professional_law", hf_subset="professional_law"), | |
CustomMMLUEvaluationTask(name="mmlu:professional_medicine", hf_subset="professional_medicine"), | |
CustomMMLUEvaluationTask(name="mmlu:professional_psychology", hf_subset="professional_psychology"), | |
CustomMMLUEvaluationTask(name="mmlu:public_relations", hf_subset="public_relations"), | |
CustomMMLUEvaluationTask(name="mmlu:security_studies", hf_subset="security_studies"), | |
CustomMMLUEvaluationTask(name="mmlu:sociology", hf_subset="sociology"), | |
CustomMMLUEvaluationTask(name="mmlu:us_foreign_policy", hf_subset="us_foreign_policy"), | |
CustomMMLUEvaluationTask(name="mmlu:virology", hf_subset="virology"), | |
CustomMMLUEvaluationTask(name="mmlu:world_religions", hf_subset="world_religions"), | |
] | |
def mmlu_prompt(line, task_name: str = None): | |
"""MMLU prompt without letters""" | |
topic = line["subject"] | |
prompt = f"The following are questions about {topic.replace('_', ' ')}.\nQuestion: " | |
prompt += line["question"] + "\nAnswer:" | |
return Doc( | |
task_name=task_name, | |
query=prompt, | |
choices=[f" {c}" for c in line["choices"]], | |
gold_index=line["answer"], | |
instruction=f"The following are questions about {topic.replace('_', ' ')}.\n", | |
) | |
MMLU_STRING = [(t, f"custom|{t.name}|0|1") for t in MMLU_TASKS] | |
_TASKS_STRINGS.extend(MMLU_STRING) | |
_TASKS += MMLU_TASKS | |
# common sense reasoning + mmlu | |
EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in MMLU_STRING]) | |
# Convert to dict for lighteval | |
TASKS_TABLE = [task.as_dict() for task in _TASKS] | |
# You can have a few pre-organised groups of tasks | |
TASKS_GROUPS = { | |
"early-signal": EARLY_SIGNAL_TASKS, | |
} | |