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448 lines
16 KiB
Python
448 lines
16 KiB
Python
"""
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AIMO Evaluation Environment for Atropos (Generative Mode)
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This environment evaluates models on AIMO (AI Mathematical Olympiad) Progress Prize 1 -
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a training set from the Kaggle AIMO competition designed to test AI mathematical reasoning.
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Dataset: lighteval/aimo_progress_prize_1
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The evaluation follows a generative approach:
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- Models receive math olympiad problems
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- Expected to provide step-by-step reasoning
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- Final answer in \\boxed{} format
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- Uses math_verify for robust answer verification
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- Falls back to string normalization if symbolic comparison fails
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Supports thinking mode with <think></think> tags for extended reasoning.
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"""
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import asyncio
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import random
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from concurrent.futures import ProcessPoolExecutor
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from typing import Dict, List, Optional
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import wandb
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from datasets import load_dataset
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from eval_helpers import (
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THINK_CONTENT_AFTER_PATTERN,
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create_system_content,
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extract_boxed_answers,
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extract_thinking_content,
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format_math_answer_instruction,
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get_default_thinking_prompt,
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get_math_executor,
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save_eval_results,
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score_math_answer_async,
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validate_thinking_format,
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)
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from pydantic import Field
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from tqdm.asyncio import tqdm_asyncio
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from atroposlib.envs.base import (
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APIServerConfig,
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BaseEnv,
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BaseEnvConfig,
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)
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# Prompt template - AIMO doesn't have a specific template in lighteval
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# Using our standard math prompt with boxed instruction
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AIMO_PROMPT_TEMPLATE = """Solve the following mathematical olympiad problem. {answer_instruction}
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{problem}"""
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class AIMOEvalConfig(BaseEnvConfig):
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"""Configuration for AIMO evaluation environment."""
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# Dataset configuration
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dataset_name: str = Field(
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default="lighteval/aimo_progress_prize_1",
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description="HuggingFace dataset name",
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)
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eval_split: str = Field(
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default="train", description="Split to evaluate on (AIMO uses train split)"
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)
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shuffle_seed: int = Field(default=42, description="Random seed for shuffling")
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# Generation parameters
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eval_temperature: float = Field(
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default=0.6, description="Temperature for evaluation generation"
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)
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eval_max_tokens: int = Field(
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default=0, description="Max tokens for evaluation (0 = use model default)"
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)
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# System prompt configuration
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custom_system_prompt: Optional[str] = Field(
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default=None, description="Optional custom system prompt"
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)
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# Thinking mode configuration
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thinking_mode: bool = Field(
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default=True,
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description="Whether to use thinking mode with <think></think> tags",
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)
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custom_thinking_prompt: Optional[str] = Field(
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default=None, description="Optional custom thinking prompt"
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)
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# Math verification configuration
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include_hope_suffix: bool = Field(
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default=True,
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description="Whether to include 'I hope it is correct' in answer instruction",
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)
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max_math_workers: int = Field(
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default=64,
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description="Maximum workers for math verification ProcessPoolExecutor",
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)
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# Retry and debug configuration
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max_retries: int = Field(
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default=3, description="Maximum retries for failed API calls"
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)
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retry_delay: float = Field(
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default=1.0, description="Delay between retries in seconds"
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)
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min_response_length: int = Field(
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default=1, description="Minimum response length to consider valid"
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)
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full_debug: bool = Field(default=False, description="Enable full debug output")
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# Override defaults
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group_size: int = 1
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max_num_workers: int = 1024
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max_eval_workers: int = 256
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max_num_workers_per_node: int = 128
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use_wandb: bool = True
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rollout_server_url: str = "http://localhost:8000"
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total_steps: int = 1
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wandb_name: str = "aimo_eval"
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steps_per_eval: int = 1
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class AIMOEvalEnv(BaseEnv):
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"""
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AIMO Evaluation Environment.
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Evaluates mathematical olympiad problem solving using AIMO dataset.
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Uses math_verify for robust answer verification with string fallback.
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"""
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name = "aimo_eval"
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def __init__(
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self,
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config: AIMOEvalConfig,
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server_configs: List[APIServerConfig],
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slurm_job_id: Optional[str] = None,
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testing: bool = False,
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):
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super().__init__(config, server_configs, slurm_job_id, testing)
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self.config: AIMOEvalConfig = config
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self.eval_items: List[Dict] = []
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self._dataset_loaded = False
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self._math_executor: Optional[ProcessPoolExecutor] = None
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@classmethod
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def config_cls(cls) -> type:
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return AIMOEvalConfig
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async def setup(self) -> None:
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"""Initialize the environment and load the dataset."""
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await super().setup()
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# Initialize math executor
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self._math_executor = get_math_executor(self.config.max_math_workers)
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if not self._dataset_loaded:
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await self._load_dataset()
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print("\nAIMO Evaluation Setup (Generative Mode):")
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print(f" Dataset: {self.config.dataset_name}")
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print(f" Evaluation split: {self.config.eval_split}")
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print(f" Thinking mode: {self.config.thinking_mode}")
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if self.config.thinking_mode:
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thinking_prompt = get_default_thinking_prompt(
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self.config.custom_thinking_prompt
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)
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print(f" Thinking prompt: {thinking_prompt[:80]}...")
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print(f" Loaded {len(self.eval_items)} evaluation items")
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async def _load_dataset(self) -> None:
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"""Load and process the AIMO dataset."""
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print(f"Loading AIMO dataset: {self.config.dataset_name}...")
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try:
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dataset = load_dataset(self.config.dataset_name, trust_remote_code=True)
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except Exception as e:
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print(f"Error loading dataset: {e}")
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raise
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if self.config.eval_split not in dataset:
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available_splits = list(dataset.keys())
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print(
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f"Split '{self.config.eval_split}' not found. Available: {available_splits}"
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)
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# Use first available split
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if available_splits:
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split_key = available_splits[0]
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print(f"Using '{split_key}' instead")
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else:
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raise ValueError("No splits available in dataset")
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else:
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split_key = self.config.eval_split
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split_data = dataset[split_key]
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# Process items
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self.eval_items = []
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for idx, item in enumerate(split_data):
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problem = item.get("problem", "")
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answer = str(item.get("answer", "")).strip()
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self.eval_items.append(
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{
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"id": str(idx),
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"problem": problem,
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"answer": answer,
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}
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)
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# Shuffle with seed
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random.seed(self.config.shuffle_seed)
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random.shuffle(self.eval_items)
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self._dataset_loaded = True
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print(f"Loaded {len(self.eval_items)} evaluation items")
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def _format_prompt(self, item: Dict) -> str:
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"""Format the problem into a prompt."""
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answer_instruction = format_math_answer_instruction(
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include_hope=self.config.include_hope_suffix
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)
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return AIMO_PROMPT_TEMPLATE.format(
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answer_instruction=answer_instruction, problem=item["problem"]
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)
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def _create_system_content(self) -> str:
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"""Create system message content based on thinking mode."""
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return (
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create_system_content(
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self.config.thinking_mode,
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self.config.custom_thinking_prompt,
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self.config.custom_system_prompt,
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)
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or ""
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)
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async def rollout_and_score_eval(
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self,
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item: Dict,
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server: APIServerConfig,
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) -> Optional[Dict]:
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"""Run evaluation on a single item and return the result."""
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prompt = self._format_prompt(item)
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system_content = self._create_system_content()
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messages = []
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if system_content:
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messages.append({"role": "system", "content": system_content})
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messages.append({"role": "user", "content": prompt})
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# Build API call parameters
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kwargs = {
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"model": server.model_name,
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"messages": messages,
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"temperature": self.config.eval_temperature,
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}
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if self.config.eval_max_tokens > 0:
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kwargs["max_tokens"] = self.config.eval_max_tokens
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response_text = ""
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for attempt in range(self.config.max_retries):
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try:
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response = await self.server.chat_completion(**kwargs)
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response_text = response.choices[0].message.content or ""
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if len(response_text) >= self.config.min_response_length:
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break
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except Exception as e:
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if self.config.full_debug:
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print(f" API error (attempt {attempt + 1}): {e}")
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if attempt < self.config.max_retries - 1:
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await asyncio.sleep(self.config.retry_delay)
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continue
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if not response_text:
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return None
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# Validate thinking format
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is_valid_format, content_for_extraction = validate_thinking_format(
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response_text, self.config.thinking_mode
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)
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# Extract thinking content if present
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thinking_content = (
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extract_thinking_content(response_text)
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if self.config.thinking_mode
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else None
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)
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# Score using math_verify with string fallback
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gold_answer = item["answer"]
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is_correct, method, has_multiple_boxed = await score_math_answer_async(
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gold=gold_answer,
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response=response_text,
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after_think=self.config.thinking_mode,
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wrap_gold_boxed=True,
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executor=self._math_executor,
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debug=self.config.full_debug,
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)
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# Extract the boxed answer for logging
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if self.config.thinking_mode:
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match = THINK_CONTENT_AFTER_PATTERN.search(response_text)
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score_content = match.group(1) if match else response_text
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else:
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score_content = response_text
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boxed_answers = extract_boxed_answers(score_content)
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extracted_answer = boxed_answers[0] if boxed_answers else None
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if self.config.full_debug:
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print(f"\n--- Item: {item['id']} ---")
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print(f"Problem: {item['problem'][:100]}...")
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print(f"Gold answer: {gold_answer}")
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print(f"Extracted: {extracted_answer}")
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print(f"Correct: {is_correct} (method: {method})")
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return {
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"item_id": item["id"],
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"problem": item["problem"][:200],
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"gold_answer": gold_answer,
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"extracted_answer": extracted_answer,
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"verification_method": method,
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"is_correct": is_correct if is_correct is not None else False,
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"has_multiple_boxed": has_multiple_boxed,
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"format_valid": is_valid_format,
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"response": response_text,
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"thinking_content": thinking_content,
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"has_thinking": thinking_content is not None,
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}
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async def evaluate(self, *args, **kwargs) -> Dict:
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"""Run the full AIMO evaluation."""
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print(f"\n{'='*60}")
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print("Starting AIMO Evaluation (Generative Mode)")
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print(f"{'='*60}")
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print(f" Total problems: {len(self.eval_items)}")
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print(f" Thinking mode: {self.config.thinking_mode}")
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print(f"{'='*60}\n")
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# Create evaluation tasks
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async def eval_task(item):
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return await self.rollout_and_score_eval(item, self.server_configs[0])
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tasks = [eval_task(item) for item in self.eval_items]
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# Run with progress bar
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results = await tqdm_asyncio.gather(*tasks, desc="Evaluating AIMO")
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# Filter out failed results
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valid_results = [r for r in results if r is not None]
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if not valid_results:
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print("Warning: No valid evaluation results obtained")
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return {"error": "No valid results", "accuracy": 0.0}
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# Calculate overall metrics
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total = len(valid_results)
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correct = sum(1 for r in valid_results if r["is_correct"])
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accuracy = correct / total if total > 0 else 0.0
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# Count verification methods and other stats
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method_counts = {}
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for r in valid_results:
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method = r.get("verification_method", "unknown")
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method_counts[method] = method_counts.get(method, 0) + 1
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multiple_boxed = sum(
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1 for r in valid_results if r.get("has_multiple_boxed", False)
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)
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format_valid = sum(1 for r in valid_results if r.get("format_valid", True))
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has_thinking = sum(1 for r in valid_results if r.get("has_thinking", False))
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has_boxed = sum(
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1 for r in valid_results if r.get("extracted_answer") is not None
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)
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metrics = {
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"accuracy": accuracy,
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"total_evaluated": total,
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"total_correct": correct,
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"has_boxed_rate": has_boxed / total if total > 0 else 0.0,
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"multiple_boxed_rate": multiple_boxed / total if total > 0 else 0.0,
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"format_compliance_rate": format_valid / total if total > 0 else 0.0,
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"thinking_utilization_rate": has_thinking / total if total > 0 else 0.0,
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"verification_methods": method_counts,
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}
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print(f"\n{'='*60}")
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print("AIMO Evaluation Results")
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print(f"{'='*60}")
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print(f" Overall Accuracy: {accuracy:.2%} ({correct}/{total})")
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print(f" Has \\boxed{{}} Rate: {has_boxed / total:.2%}")
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if multiple_boxed > 0:
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print(f" Multiple \\boxed{{}} (failed): {multiple_boxed / total:.2%}")
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print(f" Format Compliance: {format_valid / total:.2%}")
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if self.config.thinking_mode:
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print(f" Thinking Utilization: {has_thinking / total:.2%}")
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print("\n Verification Methods:")
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for method, count in sorted(method_counts.items(), key=lambda x: -x[1]):
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print(f" {method}: {count} ({count/total:.1%})")
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print(f"{'='*60}\n")
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# Save results
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if self.config.data_dir_to_save_evals:
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self._save_results(metrics, valid_results)
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return metrics
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def _save_results(self, metrics: Dict, results: List[Dict]) -> None:
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"""Save evaluation results to disk."""
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save_eval_results(self.config.data_dir_to_save_evals, metrics, results)
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async def wandb_log(self, metrics: Dict, step: int = 0) -> None:
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"""Log metrics to Weights & Biases."""
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if not self.config.use_wandb:
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return
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log_dict = {
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"aimo/accuracy": metrics.get("accuracy", 0),
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"aimo/total_evaluated": metrics.get("total_evaluated", 0),
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"aimo/has_boxed_rate": metrics.get("has_boxed_rate", 0),
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"aimo/multiple_boxed_rate": metrics.get("multiple_boxed_rate", 0),
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"aimo/format_compliance_rate": metrics.get("format_compliance_rate", 0),
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"aimo/thinking_utilization_rate": metrics.get(
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"thinking_utilization_rate", 0
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),
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}
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wandb.log(log_dict, step=step)
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# Required abstract method implementations
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async def get_next_item(self) -> Optional[Dict]:
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"""Not used in evaluation mode."""
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return None
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async def collect_trajectories(self, item) -> List:
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"""Not used in evaluation mode."""
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return []
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async def score(self, rollout_group_data) -> Optional[List]:
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"""Not used in evaluation mode."""
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return None
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if __name__ == "__main__":
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AIMOEvalEnv.cli()
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