chore: import upstream snapshot with attribution

python/sglang/test/simple_eval_aime25.py

@@ -0,0 +1,124 @@
+# Adapted from https://github.com/openai/simple-evals/
+
+"""
+AIME 2025 - American Invitational Mathematics Examination 2025
+Dataset: opencompass/AIME2025
+https://huggingface.co/datasets/opencompass/AIME2025
+
+The American Invitational Mathematics Examination (AIME) is a challenging
+competition math exam. All answers are integers from 000 to 999.
+"""
+
+import re
+from typing import Optional
+
+from sglang.test import simple_eval_common as common
+from sglang.test.simple_eval_common import (
+    ANSWER_PATTERN,
+    HTML_JINJA,
+    Eval,
+    EvalResult,
+    SamplerBase,
+    SingleEvalResult,
+)
+
+QUERY_TEMPLATE = """
+Solve the following AIME (American Invitational Mathematics Examination) problem step by step. The last line of your response should be of the form Answer: $ANSWER (without quotes) where $ANSWER is the answer to the problem.
+
+Note: AIME answers are always integers from 000 to 999 (inclusive). If you get a non-integer answer, you likely made a computational error.
+
+{question}
+
+Remember to put your answer on its own line after "Answer:", and express your answer as an integer from 000 to 999.
+""".strip()
+
+
+def normalize_aime_answer(answer: str) -> Optional[str]:
+    """
+    Normalize AIME answer to standard format.
+    AIME answers are integers from 000 to 999.
+    """
+    if answer is None:
+        return None
+    # Remove whitespace and convert to string
+    answer = str(answer).strip()
+    # Try to extract integer from answer
+    try:
+        # Handle various formats like "42", "042", "42.0", etc.
+        num = int(float(answer))
+        if 0 <= num <= 999:
+            return str(num)
+    except (ValueError, TypeError):
+        pass
+    return answer
+
+
+class AIME25Eval(Eval):
+    def __init__(
+        self,
+        num_examples: Optional[int],
+        num_threads: int,
+    ):
+        try:
+            from datasets import load_dataset
+        except ImportError:
+            raise ImportError(
+                "The 'datasets' package is required for AIME25 evaluation. "
+                "Please install it with: pip install datasets"
+            )
+
+        # Load AIME 2025 dataset from HuggingFace
+        dataset1 = load_dataset("opencompass/AIME2025", "AIME2025-I", split="test")
+        dataset2 = load_dataset("opencompass/AIME2025", "AIME2025-II", split="test")
+        examples1 = [
+            {"question": row["question"], "answer": str(row["answer"])}
+            for row in dataset1
+        ]
+        examples2 = [
+            {"question": row["question"], "answer": str(row["answer"])}
+            for row in dataset2
+        ]
+        examples = examples1 + examples2
+
+        if num_examples:
+            examples = examples[: min(num_examples, len(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=QUERY_TEMPLATE.format(**row), role="user")
+            ]
+            response_text = sampler(prompt_messages)
+            response_text = response_text or ""
+
+            # Extract answer from response
+            match = re.search(ANSWER_PATTERN, response_text)
+            extracted_answer = match.group(1).strip() if match else None
+
+            # Normalize both answers for comparison
+            normalized_extracted = normalize_aime_answer(extracted_answer)
+            normalized_correct = normalize_aime_answer(row["answer"])
+
+            # Score: 1.0 if correct, 0.0 otherwise
+            score = 1.0 if normalized_extracted == normalized_correct 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")]
+            return SingleEvalResult(
+                html=html,
+                score=score,
+                convo=convo,
+                metrics={"chars": len(response_text)},
+            )
+
+        results = common.map_with_progress(fn, self.examples, self.num_threads)
+        return common.aggregate_results(results)