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@ -14,7 +14,7 @@ are extremely difficult—PhD-level experts score about 65%, skilled non-experts
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The evaluation follows the lighteval generative approach:
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- Models are prompted to "think step by step before answering"
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- Models output their reasoning followed by "Answer: X"
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- Models output their reasoning followed by "Answer: X"
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- Answer is extracted using regex patterns from the response
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- Simple string matching validates the extracted answer
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@ -31,6 +31,15 @@ from typing import Dict, List, Optional, Tuple
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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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build_mcqa_fallback_patterns,
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create_system_content,
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extract_letter_from_answer_tag,
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extract_thinking_content,
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get_default_thinking_prompt,
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save_eval_results,
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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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@ -40,16 +49,6 @@ from atroposlib.envs.base import (
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BaseEnvConfig,
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EvalHandlingEnum,
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)
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from eval_helpers import (
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extract_letter_from_answer_tag,
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validate_thinking_format,
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extract_thinking_content,
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get_default_thinking_prompt,
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create_system_content,
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save_eval_results,
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build_mcqa_fallback_patterns,
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)
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# GPQA prompt template with <answer> tag instruction
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GPQA_PROMPT_TEMPLATE = """Answer the following multiple choice question. Think step by step before answering.
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@ -114,7 +113,7 @@ class GPQAEvalConfig(BaseEnvConfig):
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description="Maximum tokens for evaluation responses. Set high to allow reasoning.",
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)
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# Prompt configuration
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# Prompt configuration
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custom_system_prompt: Optional[str] = Field(
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default=None,
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description="Custom system prompt to append after thinking prompt (if thinking_mode) or use directly.",
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@ -149,10 +148,10 @@ class GPQAEvalConfig(BaseEnvConfig):
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class GPQAEvalEnv(BaseEnv):
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"""
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GPQA-Diamond Evaluation Environment for Atropos (Generative/Reasoning Mode).
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Evaluates models on the GPQA benchmark using a generative approach where
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models reason before answering graduate-level science questions.
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Key features:
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- Loads GPQA dataset from HuggingFace (Idavidrein/gpqa)
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- Uses lighteval's exact prompt format for generative evaluation
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@ -160,7 +159,7 @@ class GPQAEvalEnv(BaseEnv):
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- Extracts answer letters from patterns like "Answer: A"
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- Shuffles answer positions for fair evaluation
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"""
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name = "gpqa_eval"
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env_config_cls = GPQAEvalConfig
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@ -176,22 +175,24 @@ class GPQAEvalEnv(BaseEnv):
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# Initialize metrics tracking
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self.eval_metrics = []
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# Set up random seed for answer shuffling
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if self.config.shuffle_seed is not None:
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self.shuffle_rng = random.Random(self.config.shuffle_seed)
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else:
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self.shuffle_rng = random.Random()
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# Pre-compile regex patterns for thinking mode
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self._think_pattern = re.compile(r"<think>")
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self._think_close_pattern = re.compile(r"</think>")
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self._think_content_pattern = re.compile(r"</think>\s*(.*)", re.DOTALL)
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self._thinking_extract_pattern = re.compile(r"<think>(.*?)</think>", re.DOTALL)
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# Pre-compile regex for <answer></answer> tag extraction (primary method)
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self._answer_tag_pattern = re.compile(r"<answer>(.*?)</answer>", re.DOTALL | re.IGNORECASE)
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self._answer_tag_pattern = re.compile(
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r"<answer>(.*?)</answer>", re.DOTALL | re.IGNORECASE
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)
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# Build fallback answer extraction patterns
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self._build_extraction_patterns()
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@ -204,48 +205,40 @@ class GPQAEvalEnv(BaseEnv):
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return 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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self.config.custom_system_prompt,
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)
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def _build_extraction_patterns(self):
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"""Build regex patterns for extracting answer letters from model responses."""
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letters = "ABCD"
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letter_pattern = rf"([{letters}]|\([{letters}]\))"
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# Patterns ordered by priority (most specific first)
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self._pattern_final_answer_hope = re.compile(
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rf"(?i:final\s+answer\s+is)\s*:?\s*{letter_pattern}\.?\s*I\s*hope",
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re.IGNORECASE
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re.IGNORECASE,
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)
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self._pattern_final_answer_is = re.compile(
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rf"(?i:final\s+answer).{{0,100}}?\s+is\s*:?\s*{letter_pattern}",
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re.IGNORECASE | re.DOTALL
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re.IGNORECASE | re.DOTALL,
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)
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self._pattern_the_answer_is = re.compile(
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rf"(?i:the\s+answer\s+is)\s*:?\s*{letter_pattern}",
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re.IGNORECASE
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rf"(?i:the\s+answer\s+is)\s*:?\s*{letter_pattern}", re.IGNORECASE
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)
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self._pattern_answer_colon = re.compile(
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rf"(?i:answer)\s*:\s*.{{0,50}}?{letter_pattern}",
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re.IGNORECASE | re.DOTALL
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rf"(?i:answer)\s*:\s*.{{0,50}}?{letter_pattern}", re.IGNORECASE | re.DOTALL
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)
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self._pattern_answer_space = re.compile(
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rf"(?i:answer)\s+{letter_pattern}",
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re.IGNORECASE
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rf"(?i:answer)\s+{letter_pattern}", re.IGNORECASE
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)
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self._pattern_start = re.compile(
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rf"^\s*\**{letter_pattern}\**[\s\.\)\:]",
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re.IGNORECASE
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rf"^\s*\**{letter_pattern}\**[\s\.\)\:]", re.IGNORECASE
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)
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self._pattern_line_start = re.compile(
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rf"\n\s*\**{letter_pattern}\**[\s\.\)\:]",
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re.IGNORECASE
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rf"\n\s*\**{letter_pattern}\**[\s\.\)\:]", re.IGNORECASE
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)
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self._pattern_standalone = re.compile(
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rf"\b{letter_pattern}\b",
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re.IGNORECASE
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)
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self._pattern_standalone = re.compile(rf"\b{letter_pattern}\b", re.IGNORECASE)
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self._extraction_patterns = [
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(0, self._pattern_final_answer_hope, "final_answer_hope"),
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(50, self._pattern_final_answer_is, "final_answer_is"),
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@ -272,8 +265,8 @@ class GPQAEvalEnv(BaseEnv):
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inference_weight=1.0,
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wandb_name="gpqa_eval",
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eval_handling=EvalHandlingEnum.STOP_TRAIN,
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max_eval_workers = 256,
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max_num_workers = 1024,
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max_eval_workers=256,
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max_num_workers=1024,
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# GPQA-specific defaults
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dataset_name="Idavidrein/gpqa",
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subset="gpqa_diamond",
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@ -281,7 +274,7 @@ class GPQAEvalEnv(BaseEnv):
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eval_max_tokens=0, # Use model default
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thinking_mode=True,
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)
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server_configs = [
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APIServerConfig(
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model_name="Hermes-3-Llama-3.1-8B",
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@ -291,7 +284,7 @@ class GPQAEvalEnv(BaseEnv):
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num_requests_for_eval=1024,
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),
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]
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return env_config, server_configs
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async def setup(self) -> None:
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@ -304,7 +297,7 @@ class GPQAEvalEnv(BaseEnv):
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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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print(f" Thinking prompt: {self._get_thinking_prompt()[:100]}...")
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# Load GPQA dataset
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try:
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dataset = load_dataset(
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@ -317,19 +310,19 @@ class GPQAEvalEnv(BaseEnv):
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except Exception as e:
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print(f"Error loading GPQA dataset: {e}")
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raise
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# Process items - shuffle answer positions
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self.all_eval_items = []
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for item in self.eval_data:
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processed = self._process_gpqa_item(item)
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self.all_eval_items.append(processed)
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self.iter = 0
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def _process_gpqa_item(self, item: Dict) -> Dict:
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"""
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Process a GPQA item - shuffle answer positions following lighteval.
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GPQA has: Question, Correct Answer, Incorrect Answer 1/2/3
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We need to shuffle them into A/B/C/D positions.
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"""
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@ -340,12 +333,12 @@ class GPQAEvalEnv(BaseEnv):
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item["Incorrect Answer 2"],
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item["Incorrect Answer 3"],
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]
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# Randomly place correct answer
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gold_index = self.shuffle_rng.randint(0, 3)
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choices = incorrect_answers.copy()
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choices.insert(gold_index, correct_answer)
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return {
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"question": item["Question"],
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"choices": choices,
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@ -358,7 +351,7 @@ class GPQAEvalEnv(BaseEnv):
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def _format_gpqa_prompt(self, question: str, choices: List[str]) -> str:
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"""
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Format a GPQA question using the lighteval template.
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Uses the exact prompt format from lighteval's gpqa_instruct_prompt.
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"""
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return GPQA_PROMPT_TEMPLATE.format(
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@ -373,13 +366,13 @@ class GPQAEvalEnv(BaseEnv):
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"""Validate thinking format and extract content after </think> tags."""
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if not self.config.thinking_mode:
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return True, response
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think_open_count = len(self._think_pattern.findall(response))
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think_close_count = len(self._think_close_pattern.findall(response))
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if think_open_count != 1 or think_close_count != 1:
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return False, response
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match = self._think_content_pattern.search(response)
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if match:
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return True, match.group(1).strip()
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@ -394,22 +387,19 @@ class GPQAEvalEnv(BaseEnv):
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return None
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def _extract_answer(
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self,
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response: str,
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num_choices: int = 4,
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choices: Optional[List[str]] = None
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self, response: str, num_choices: int = 4, choices: Optional[List[str]] = None
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) -> Tuple[Optional[str], str]:
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"""
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Extract the answer letter from the model's response.
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Primary method: Look for <answer></answer> tags, or match against choice texts.
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Fallback: Use priority-ordered regex patterns.
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"""
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if not response:
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return None, "empty_response"
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valid_letters = set(ascii_uppercase[:num_choices])
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# PRIMARY: Try <answer></answer> tags first
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# Also matches against choice texts if provided
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letter, method = extract_letter_from_answer_tag(
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@ -417,27 +407,41 @@ class GPQAEvalEnv(BaseEnv):
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)
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if letter:
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return letter, method
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# FALLBACK: Try each pattern in priority order
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for priority, pattern, method_name in self._extraction_patterns:
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matches = pattern.findall(response)
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if matches:
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match = matches[-1] if method_name in ["final_answer_is", "the_answer_is", "answer_colon", "answer_space"] else matches[0]
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match = (
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matches[-1]
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if method_name
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in [
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"final_answer_is",
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"the_answer_is",
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"answer_colon",
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"answer_space",
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]
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else matches[0]
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)
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if isinstance(match, tuple):
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match = match[0]
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letter = match.strip("()").upper()
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if letter in valid_letters:
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if self.config.full_debug:
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print(f" Extracted '{letter}' using fallback method '{method_name}'")
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print(
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f" Extracted '{letter}' using fallback method '{method_name}'"
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)
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return letter, f"fallback_{method_name}"
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for letter in reversed(list(valid_letters)):
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if letter in response.upper():
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if self.config.full_debug:
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print(f" Extracted '{letter}' using fallback 'last_valid_letter'")
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print(
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f" Extracted '{letter}' using fallback 'last_valid_letter'"
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)
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return letter, "fallback_last_valid_letter"
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return None, "no_match"
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async def get_next_item(self):
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@ -459,24 +463,24 @@ class GPQAEvalEnv(BaseEnv):
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async def rollout_and_score_eval(self, eval_item: Dict) -> Dict:
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"""Evaluate a single GPQA question using generative mode."""
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try:
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question = eval_item.get('question', '')
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choices = eval_item.get('choices', [])
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gold_letter = eval_item.get('gold_letter', 'A')
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subdomain = eval_item.get('subdomain', 'unknown')
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question = eval_item.get("question", "")
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choices = eval_item.get("choices", [])
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gold_letter = eval_item.get("gold_letter", "A")
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subdomain = eval_item.get("subdomain", "unknown")
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if not question or len(choices) != 4:
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return {"is_correct": None, "sample": None}
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# Format the prompt (lighteval style)
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formatted_prompt = self._format_gpqa_prompt(question, choices)
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# Build messages
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messages = []
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system_content = self._create_system_content()
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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": formatted_prompt})
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# Get model completion with retry logic
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model_response = None
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finish_reason = None
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@ -489,24 +493,33 @@ class GPQAEvalEnv(BaseEnv):
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max_tokens=self.config.eval_max_tokens,
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split="eval",
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)
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if completion.choices and completion.choices[0].message.content:
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model_response = completion.choices[0].message.content
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finish_reason = getattr(completion.choices[0], 'finish_reason', None)
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if len(model_response.strip()) >= self.config.min_response_length:
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finish_reason = getattr(
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completion.choices[0], "finish_reason", None
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)
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if (
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len(model_response.strip())
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>= self.config.min_response_length
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):
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break
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elif attempt < self.config.max_retries - 1:
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if self.config.full_debug:
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print(f" Response too short, retrying...")
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await asyncio.sleep(self.config.retry_delay)
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except Exception as e:
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# Always log API errors to help diagnose issues
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print(f" API Error (attempt {attempt + 1}/{self.config.max_retries}): {type(e).__name__}: {e}")
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if hasattr(e, 'response'):
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print(
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f" API Error (attempt {attempt + 1}/{self.config.max_retries}): {type(e).__name__}: {e}"
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)
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if hasattr(e, "response"):
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try:
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print(f" Response: {e.response.text[:500] if hasattr(e.response, 'text') else e.response}")
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print(
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f" Response: {e.response.text[:500] if hasattr(e.response, 'text') else e.response}"
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)
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except:
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pass
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if attempt < self.config.max_retries - 1:
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|
|
@ -514,26 +527,28 @@ class GPQAEvalEnv(BaseEnv):
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else:
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print(f" Failed after {self.config.max_retries} attempts")
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return {"is_correct": None, "sample": None}
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if not model_response:
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return {"is_correct": None, "sample": None}
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# Validate thinking format if enabled
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format_valid, content_for_extraction = self._validate_thinking_format(model_response)
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format_valid, content_for_extraction = self._validate_thinking_format(
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model_response
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)
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# Extract thinking content for logging
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thinking_content = None
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if self.config.thinking_mode:
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thinking_content = self._extract_thinking_content(model_response)
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# Extract the answer (pass choices for exact text matching)
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extracted_answer, extraction_method = self._extract_answer(
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content_for_extraction, num_choices=4, choices=choices
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)
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# Check if correct
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is_correct = extracted_answer == gold_letter if extracted_answer else False
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# Build sample record
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sample = {
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"question": question,
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|
|
@ -549,28 +564,33 @@ class GPQAEvalEnv(BaseEnv):
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"thinking_mode": self.config.thinking_mode,
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"format_valid": format_valid,
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}
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if self.config.thinking_mode:
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sample["thinking_content"] = thinking_content
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sample["response_after_think"] = content_for_extraction if format_valid else None
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sample["response_after_think"] = (
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content_for_extraction if format_valid else None
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)
|
||||
|
||||
if self.config.full_debug:
|
||||
status = "✓" if is_correct else "✗"
|
||||
print(f" [{status}] {subdomain}: gold={gold_letter}, extracted={extracted_answer}")
|
||||
|
||||
print(
|
||||
f" [{status}] {subdomain}: gold={gold_letter}, extracted={extracted_answer}"
|
||||
)
|
||||
|
||||
return {"is_correct": is_correct, "sample": sample}
|
||||
|
||||
|
||||
except Exception as e:
|
||||
if self.config.full_debug:
|
||||
print(f"Error in rollout_and_score_eval: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
return {"is_correct": None, "sample": None}
|
||||
|
||||
async def evaluate(self, *args, **kwargs) -> None:
|
||||
"""Run GPQA evaluation."""
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Starting GPQA Evaluation (Generative/Reasoning Mode)")
|
||||
print(f"{'='*60}")
|
||||
|
|
@ -579,38 +599,40 @@ class GPQAEvalEnv(BaseEnv):
|
|||
print(f" Max tokens (for reasoning): {self.config.eval_max_tokens}")
|
||||
print(f" Thinking mode: {self.config.thinking_mode}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
|
||||
try:
|
||||
eval_tasks = [
|
||||
self.rollout_and_score_eval(item) for item in self.all_eval_items
|
||||
]
|
||||
results = await tqdm_asyncio.gather(*eval_tasks, desc="Evaluating GPQA")
|
||||
|
||||
|
||||
valid_results = [
|
||||
r for r in results
|
||||
r
|
||||
for r in results
|
||||
if r and r.get("sample") is not None and r.get("is_correct") is not None
|
||||
]
|
||||
|
||||
|
||||
if not valid_results:
|
||||
print("Warning: No valid evaluation results obtained")
|
||||
return
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during evaluation: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
return
|
||||
|
||||
|
||||
end_time = time.time()
|
||||
|
||||
|
||||
# Compute metrics
|
||||
samples = [r["sample"] for r in valid_results]
|
||||
|
||||
|
||||
# Overall accuracy
|
||||
total_correct = sum(1 for r in valid_results if r["is_correct"])
|
||||
total_count = len(valid_results)
|
||||
overall_accuracy = total_correct / total_count if total_count > 0 else 0.0
|
||||
|
||||
|
||||
# Per-subdomain accuracy
|
||||
subdomain_results = {}
|
||||
for sample in samples:
|
||||
|
|
@ -620,7 +642,7 @@ class GPQAEvalEnv(BaseEnv):
|
|||
subdomain_results[subdomain]["total"] += 1
|
||||
if sample["is_correct"]:
|
||||
subdomain_results[subdomain]["correct"] += 1
|
||||
|
||||
|
||||
# Extraction method statistics
|
||||
extraction_methods = {}
|
||||
for sample in samples:
|
||||
|
|
@ -630,20 +652,22 @@ class GPQAEvalEnv(BaseEnv):
|
|||
extraction_methods[method]["count"] += 1
|
||||
if sample["is_correct"]:
|
||||
extraction_methods[method]["correct"] += 1
|
||||
|
||||
|
||||
# Average response length
|
||||
response_lengths = [s.get("response_length", 0) for s in samples]
|
||||
avg_response_length = sum(response_lengths) / len(response_lengths) if response_lengths else 0
|
||||
|
||||
avg_response_length = (
|
||||
sum(response_lengths) / len(response_lengths) if response_lengths else 0
|
||||
)
|
||||
|
||||
# Format compliance
|
||||
format_compliant = sum(1 for s in samples if s.get("format_valid", True))
|
||||
format_compliance_rate = format_compliant / len(samples) if samples else 0.0
|
||||
|
||||
|
||||
# Thinking utilization
|
||||
thinking_utilization = 0
|
||||
if self.config.thinking_mode:
|
||||
thinking_utilization = sum(1 for s in samples if s.get("thinking_content"))
|
||||
|
||||
|
||||
# Build metrics dictionary
|
||||
eval_metrics = {
|
||||
"eval/overall_accuracy": overall_accuracy,
|
||||
|
|
@ -654,46 +678,54 @@ class GPQAEvalEnv(BaseEnv):
|
|||
"eval/format_compliance_rate": format_compliance_rate,
|
||||
"eval/thinking_mode_enabled": 1.0 if self.config.thinking_mode else 0.0,
|
||||
}
|
||||
|
||||
|
||||
if self.config.thinking_mode:
|
||||
thinking_utilization_rate = thinking_utilization / len(samples) if samples else 0.0
|
||||
thinking_utilization_rate = (
|
||||
thinking_utilization / len(samples) if samples else 0.0
|
||||
)
|
||||
eval_metrics["eval/thinking_utilization_rate"] = thinking_utilization_rate
|
||||
|
||||
|
||||
# Add subdomain metrics
|
||||
for subdomain, stats in subdomain_results.items():
|
||||
if stats["total"] > 0:
|
||||
subdom_accuracy = stats["correct"] / stats["total"]
|
||||
subdom_key = subdomain.replace(" ", "_").replace("-", "_").lower()
|
||||
eval_metrics[f"eval/subdomain_{subdom_key}_accuracy"] = subdom_accuracy
|
||||
|
||||
|
||||
# Store metrics for wandb logging
|
||||
self.eval_metrics = [(k, v) for k, v in eval_metrics.items()]
|
||||
|
||||
|
||||
# Print summary
|
||||
print(f"\n{'='*60}")
|
||||
print(f"GPQA Evaluation Results ({self.config.subset})")
|
||||
print(f"{'='*60}")
|
||||
print(f"Overall Accuracy: {overall_accuracy:.4f} ({total_correct}/{total_count})")
|
||||
print(
|
||||
f"Overall Accuracy: {overall_accuracy:.4f} ({total_correct}/{total_count})"
|
||||
)
|
||||
print(f"Evaluation Time: {end_time - start_time:.1f} seconds")
|
||||
print(f"Avg Response Length: {avg_response_length:.0f} chars")
|
||||
if self.config.thinking_mode:
|
||||
print(f"Format Compliance: {format_compliance_rate:.4f}")
|
||||
print(f"Thinking Utilization: {thinking_utilization}/{total_count}")
|
||||
|
||||
|
||||
print(f"\nSubdomain Breakdown:")
|
||||
for subdomain, stats in sorted(subdomain_results.items()):
|
||||
if stats["total"] > 0:
|
||||
subdom_acc = stats["correct"] / stats["total"]
|
||||
print(f" {subdomain}: {subdom_acc:.4f} ({stats['correct']}/{stats['total']})")
|
||||
|
||||
print(
|
||||
f" {subdomain}: {subdom_acc:.4f} ({stats['correct']}/{stats['total']})"
|
||||
)
|
||||
|
||||
print(f"\nExtraction Method Statistics:")
|
||||
for method, stats in sorted(extraction_methods.items(), key=lambda x: -x[1]["count"]):
|
||||
for method, stats in sorted(
|
||||
extraction_methods.items(), key=lambda x: -x[1]["count"]
|
||||
):
|
||||
if stats["count"] > 0:
|
||||
method_acc = stats["correct"] / stats["count"]
|
||||
print(f" {method}: {stats['count']} uses, {method_acc:.4f} accuracy")
|
||||
|
||||
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
|
||||
# Log evaluation results
|
||||
try:
|
||||
await self.evaluate_log(
|
||||
|
|
@ -716,18 +748,19 @@ class GPQAEvalEnv(BaseEnv):
|
|||
"""Log metrics to wandb."""
|
||||
if wandb_metrics is None:
|
||||
wandb_metrics = {}
|
||||
|
||||
|
||||
for metric_name, metric_value in self.eval_metrics:
|
||||
wandb_metrics[metric_name] = metric_value
|
||||
self.eval_metrics = []
|
||||
|
||||
wandb_metrics["config/thinking_mode"] = 1.0 if self.config.thinking_mode else 0.0
|
||||
|
||||
wandb_metrics["config/thinking_mode"] = (
|
||||
1.0 if self.config.thinking_mode else 0.0
|
||||
)
|
||||
wandb_metrics["config/eval_max_tokens"] = self.config.eval_max_tokens
|
||||
wandb_metrics["config/subset"] = self.config.subset
|
||||
|
||||
|
||||
await super().wandb_log(wandb_metrics)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
GPQAEvalEnv.cli()
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue