chore: import upstream snapshot with attribution

2026-07-13 12:38:16 +08:00
commit 94057c3d3e
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+# Adapted from https://github.com/openai/simple-evals/
+
+"""
+Measuring Massive Multitask Language Understanding
+Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt
+https://arxiv.org/abs/2009.03300
+"""
+
+import random
+import re
+from typing import Optional
+
+import pandas
+
+from sglang.test import simple_eval_common as common
+from sglang.test.simple_eval_common import (
+    ANSWER_PATTERN_MULTICHOICE,
+    HTML_JINJA,
+    Eval,
+    EvalResult,
+    SamplerBase,
+    SingleEvalResult,
+    format_multichoice_question,
+)
+
+subject2category = {
+    "abstract_algebra": "stem",
+    "anatomy": "other",
+    "astronomy": "stem",
+    "business_ethics": "other",
+    "clinical_knowledge": "other",
+    "college_biology": "stem",
+    "college_chemistry": "stem",
+    "college_computer_science": "stem",
+    "college_mathematics": "stem",
+    "college_medicine": "other",
+    "college_physics": "stem",
+    "computer_security": "stem",
+    "conceptual_physics": "stem",
+    "econometrics": "social_sciences",
+    "electrical_engineering": "stem",
+    "elementary_mathematics": "stem",
+    "formal_logic": "humanities",
+    "global_facts": "other",
+    "high_school_biology": "stem",
+    "high_school_chemistry": "stem",
+    "high_school_computer_science": "stem",
+    "high_school_european_history": "humanities",
+    "high_school_geography": "social_sciences",
+    "high_school_government_and_politics": "social_sciences",
+    "high_school_macroeconomics": "social_sciences",
+    "high_school_mathematics": "stem",
+    "high_school_microeconomics": "social_sciences",
+    "high_school_physics": "stem",
+    "high_school_psychology": "social_sciences",
+    "high_school_statistics": "stem",
+    "high_school_us_history": "humanities",
+    "high_school_world_history": "humanities",
+    "human_aging": "other",
+    "human_sexuality": "social_sciences",
+    "international_law": "humanities",
+    "jurisprudence": "humanities",
+    "logical_fallacies": "humanities",
+    "machine_learning": "stem",
+    "management": "other",
+    "marketing": "other",
+    "medical_genetics": "other",
+    "miscellaneous": "other",
+    "moral_disputes": "humanities",
+    "moral_scenarios": "humanities",
+    "nutrition": "other",
+    "philosophy": "humanities",
+    "prehistory": "humanities",
+    "professional_accounting": "other",
+    "professional_law": "humanities",
+    "professional_medicine": "other",
+    "professional_psychology": "social_sciences",
+    "public_relations": "social_sciences",
+    "security_studies": "social_sciences",
+    "sociology": "social_sciences",
+    "us_foreign_policy": "social_sciences",
+    "virology": "other",
+    "world_religions": "humanities",
+}
+
+
+class MMLUEval(Eval):
+    def __init__(self, filename: str, num_examples: Optional[int], num_threads: int):
+        if "://" in filename:
+            df = pandas.read_csv(filename, storage_options={"timeout": 30})
+        else:
+            df = pandas.read_csv(filename)
+        examples = [row.to_dict() for _, row in df.iterrows()]
+        if num_examples:
+            examples = random.Random(0).sample(examples, num_examples)
+        self.examples = examples
+        self.num_threads = num_threads
+
+    def __call__(self, sampler: SamplerBase) -> EvalResult:
+        def fn(row: dict):
+            prompt_messages = [
+                sampler._pack_message(
+                    content=format_multichoice_question(row), role="user"
+                )
+            ]
+            response_text = sampler(prompt_messages)
+            response_text = response_text or ""
+            match = re.search(ANSWER_PATTERN_MULTICHOICE, response_text)
+            extracted_answer = match.group(1) if match else None
+            score = 1.0 if extracted_answer == row["Answer"] else 0.0
+            html = common.jinja_env.from_string(HTML_JINJA).render(
+                prompt_messages=prompt_messages,
+                next_message=dict(content=response_text, role="assistant"),
+                score=score,
+                correct_answer=row["Answer"],
+                extracted_answer=extracted_answer,
+            )
+            convo = prompt_messages + [dict(content=response_text, role="assistant")]
+            category = subject2category.get(row["Subject"], "other")
+            return SingleEvalResult(
+                html=html, score=score, metrics={category: score}, convo=convo
+            )
+
+        results = common.map_with_progress(fn, self.examples, self.num_threads)
+        return common.aggregate_results(results)