Create lighteval_tasks.py

lighteval_tasks.py

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+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,
+}