From 720c46a2fcd3ce8f3fd93785caea84176ea2a788 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 17 Feb 2026 04:35:18 +0000 Subject: [PATCH 01/11] add best of n sampling baselines + generate test baselines --- examples/TTSwithVerification/README.md | 41 ++ .../TTSwithVerification/bestofk_baseline.py | 608 ++++++++++++++++++ 2 files changed, 649 insertions(+) create mode 100644 examples/TTSwithVerification/bestofk_baseline.py diff --git a/examples/TTSwithVerification/README.md b/examples/TTSwithVerification/README.md index ed7fbb4e..a2c52dd4 100644 --- a/examples/TTSwithVerification/README.md +++ b/examples/TTSwithVerification/README.md @@ -156,6 +156,39 @@ The Z3 solver handles diagonal directions (`Northwest`, `Northeast`, `Southwest` --- +# Best-of-K Baseline + +A simple best-of-K baseline that generates K independent reasoning traces per example and selects the best based on: +1. **Ground-truth matching** (default): Greedy selection of first correct answer among K samples +2. **Critic model evaluation** (optional): Use a separate critic LLM to evaluate correctness without access to ground truth + +This baseline demonstrates that with sufficient sampling, even simple CoT can achieve good performance. + +## Usage + +```bash +# Best-of-K with ground-truth evaluation +python ./examples/TTSwithVerification/bestofk_baseline.py --task game24 -n 10 --k 4 + +# Best-of-K with critic model evaluation +python ./examples/TTSwithVerification/bestofk_baseline.py --task game24 -n 10 --k 4 --use_critic --critic_model Qwen/Qwen3-30B-A3B-Thinking-2507 --critic_port 8001 +``` + +### Parameters + +| Argument | Description | Default | +|----------|-------------|---------| +| `--task` | Task: `game24`, `maze`, or `spatialmap` | required | +| `--k` | Number of samples per example | `4` | +| `--use_critic` | Use critic model for evaluation instead of ground truth | `False` | +| `--critic_model` | Model to use for critic evaluation | MAIN_MODEL | +| `--critic_port` | vLLM server port for critic model | `8001` | +| `--num_examples`, `-n` | Number of examples to run | varies | +| `--main_model` | Model for generation | `Qwen/Qwen3-30B-A3B-Thinking-2507` | +| `--port` | vLLM server port for main model | `8000` | + +--- + ## Example Scripts Each script runs a full evaluation: loading a dataset, building structured prompts, running inference with step verification, and computing accuracy/token statistics. @@ -169,6 +202,14 @@ python ./examples/TTSwithVerification/maze_stepverifier.py -n 1 # SpatialMap with step verification python ./examples/TTSwithVerification/spatialmap_stepverifier.py -n 1 + +# Best-of-K baseline (standard CoT, no monitors) +python ./examples/TTSwithVerification/bestofk_baseline.py --task game24 -n 1 --k 4 +python ./examples/TTSwithVerification/bestofk_baseline.py --task maze -n 1 --k 4 +python ./examples/TTSwithVerification/bestofk_baseline.py --task spatialmap -n 1 --k 4 + +# Best-of-K with critic model evaluation +python ./examples/TTSwithVerification/bestofk_baseline.py --task game24 -n 1 --k 4 --use_critic ``` ### Common arguments diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py new file mode 100644 index 00000000..8864a38a --- /dev/null +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -0,0 +1,608 @@ +import argparse +import asyncio +import json +import logging +import os +import re +from dataclasses import dataclass +from typing import List, Optional + +import numpy as np +from datasets import load_dataset +from tqdm import tqdm +from transformers import AutoTokenizer + +from interwhen import stream_completion + +# ============== MODEL CONFIGURATION ============== +MAIN_MODEL = "Qwen/Qwen3-30B-A3B-Thinking-2507" +# ================================================= + +logger = logging.getLogger(__name__) + + +@dataclass +class SampleResult: + output: str + correct: bool + extracted: Optional[str] + message: str + tokens: int + critic_correct: Optional[bool] = None + + +def get_model_short_name(model_name: str) -> str: + short_name = model_name.split("/")[-1] + return short_name.replace(" ", "_").replace(":", "-") + + +def get_output_dirs(task: str, main_model: str, base_dir: str = "../../b-pchanda/Outputs_TTS/BestOfKResults"): + model_short_name = get_model_short_name(main_model) + output_base = os.path.join(base_dir, task, model_short_name) + dirs = { + "base": output_base, + "reasoning": os.path.join(output_base, "Reasoning_output"), + } + for dir_path in dirs.values(): + os.makedirs(dir_path, exist_ok=True) + return dirs + + +def init_llm_server(model_name, max_tokens=32768, port=8000, temperature=0.6, seed=42): + url = f"http://localhost:{port}/v1/completions" + payload = { + "model": model_name, + "max_tokens": max_tokens, + "top_k": 20, + "top_p": 0.95, + "min_p": 0.0, + "do_sample": True, + "temperature": temperature, + "stream": True, + "logprobs": 20, + "use_beam_search": False, + "prompt_cache": True, + "seed": seed, + } + headers = {"Content-Type": "application/json"} + return {"url": url, "payload": payload, "headers": headers} + + +def count_tokens(text: str, tokenizer) -> int: + tokens = tokenizer.encode(text, add_special_tokens=False) + return len(tokens) + + +def save_outputs(idx: int, outputs: List[SampleResult], best_idx: int, output_dir: str): + os.makedirs(output_dir, exist_ok=True) + filepath = os.path.join(output_dir, f"output_{idx}.txt") + with open(filepath, "w", encoding="utf-8") as f: + f.write(f"BEST_INDEX={best_idx}\n") + for i, result in enumerate(outputs): + f.write("\n" + "=" * 80 + "\n") + f.write(f"SAMPLE {i}\n") + f.write(f"CORRECT={result.correct}\n") + f.write(f"CRITIC_CORRECT={result.critic_correct}\n") + f.write(f"EXTRACTED={result.extracted}\n") + f.write(f"TOKENS={result.tokens}\n") + f.write(f"MESSAGE={result.message}\n") + f.write("\n") + f.write(result.output) + f.write("\n") + logger.info(f"Saved outputs to {filepath}") + + +# --------------------- Game24 helpers --------------------- + +def build_game24_prompt(nums): + a, b, c, d = nums + boxed = r"\\boxed{}" + base_prompt = f""" +You are solving the Game of 24. + +You are given four numbers: {a}, {b}, {c}, {d} + +Your job is to produce a valid arithmetic expression using: +- ALL four numbers exactly once +- ONLY +, -, *, / +- The expression must evaluate to exactly 24. + +Please reason step by step, and put your final answer containing only the expression within {boxed}. +""".strip() + return base_prompt + + +def extract_solution_game24(text): + boxed_pattern = r"\\boxed\{" + matches = list(re.finditer(boxed_pattern, text)) + if not matches: + return None + last_match = matches[-1] + start = last_match.end() + brace_count = 1 + end = start + while end < len(text) and brace_count > 0: + if text[end] == "{": + brace_count += 1 + elif text[end] == "}": + brace_count -= 1 + end += 1 + expr = text[start:end - 1].strip() + + frac_pattern = r"\\frac\{([^{}]+)\}\{([^{}]+)\}" + while re.search(frac_pattern, expr): + expr = re.sub(frac_pattern, r"(\1/\2)", expr) + + replacements = { + r"\times": "*", + r"\cdot": "*", + r"\div": "/", + } + for latex, op in replacements.items(): + expr = expr.replace(latex, op) + + expr = expr.replace(r"\\,", "").replace(r"\\ ", "") + expr = re.sub(r"\)\s*\(", ")*(", expr) + expr = re.sub(r"\)\s*(\d)", r")*\1", expr) + expr = re.sub(r"(\d)\s*\(", r"\1*(", expr) + + return expr + + +def extract_numbers_from_expr(expr): + numbers = re.findall(r"\d+\.?\d*", expr) + return [int(float(n)) if float(n).is_integer() else float(n) for n in numbers] + + +def validate_numbers_used(expr, expected_nums): + used_nums = extract_numbers_from_expr(expr) + return sorted(used_nums) == sorted(expected_nums) + + +def evaluate_expression(expr, expected_nums=None): + try: + if expected_nums is not None and not validate_numbers_used(expr, expected_nums): + return False + value = eval(expr, {"__builtins__": None}, {}) + return abs(value - 24) < 1e-6 + except Exception: + return False + + +def evaluate_game24_answer(answer, nums): + expr = extract_solution_game24(answer) + if not expr: + return False, None, "No expression found" + if evaluate_expression(expr, expected_nums=nums): + return True, expr, "Correct solution (evaluates to 24 using exactly the given numbers)" + used_nums = extract_numbers_from_expr(expr) + if sorted(used_nums) != sorted(nums): + return False, expr, f"Incorrect: Expression uses {used_nums}, expected {nums}" + return False, expr, "Expression does not evaluate to 24" + + +# --------------------- Maze/SpatialMap helpers --------------------- + +def remove_last_paragraph(s: str) -> str: + return s[:-143] if len(s) > 143 else s + + +def build_maze_prompt(example): + pre_prompt = ( + "You are an expert problem solver. Carefully read the following multiple-choice question " + "and think through the solution step-by-step before providing your final answer. " + "Provide your final answer option by enclosing it within \\boxed{A/B/C/D}.:" + ) + description = remove_last_paragraph(str(example.get("prompt"))) + return pre_prompt, description + + +def build_spatialmap_prompt(example): + pre_prompt = ( + "You are an expert problem solver. Carefully read the following multiple-choice question " + "and think through the solution step-by-step before providing your final answer." + "Provide your final answer option by enclosing it within \\boxed{A/B/C/D}.:" + ) + description = remove_last_paragraph(str(example.get("prompt"))) + return pre_prompt, description + + +def extract_solution_mcq(text): + matches = re.findall(r"\\boxed\{([^}]*)\}", text) + if not matches: + return None + expr = matches[-1].strip() + choice_match = re.search(r"\b([ABCD])\b", expr, flags=re.IGNORECASE) + if not choice_match: + return None + return choice_match.group(1).upper() + + +def extract_options_from_prompt(prompt_text, target_options): + pattern = r"\b([A-D])\.\s*(.*?)(?=\s*[A-D]\.\s*|$)" + raw = re.findall(pattern, prompt_text, flags=re.DOTALL) + options = {k: v.strip().rstrip(".") for k, v in raw} + if target_options: + options = {k: v for k, v in options.items() if k in target_options} + return options + + +def evaluate_mcq_answer(answer, options, ground_truth): + sol = extract_solution_mcq(answer) + gt_sol = str(ground_truth).strip() + if not sol: + return False, None, "No expression found" + sol = sol.strip() + if sol in options: + if options[sol] == gt_sol: + return True, sol, f"Correct: option {sol} -> {options[sol]}" + return False, sol, f"Incorrect: expected '{gt_sol}', got '{options[sol]}' (option {sol})" + if sol.lower() == gt_sol.lower(): + return True, sol, f"Correct: answer text matches ground truth: {sol}" + for opt_letter, opt_value in options.items(): + if sol.lower() == opt_value.lower(): + if opt_value == gt_sol: + return True, sol, f"Correct: answer text {sol} (option {opt_letter})" + return False, sol, f"Incorrect: expected '{gt_sol}', got '{opt_value}' (option {opt_letter})" + return False, sol, f"Solution '{sol}' not found in options or ground truth" + + +def build_full_prompt(task, example, nums=None): + if task == "game24": + prompt = build_game24_prompt(nums) + return f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" + if task == "maze": + system_prompt, user_prompt = build_maze_prompt(example) + else: + system_prompt, user_prompt = build_spatialmap_prompt(example) + return ( + f"<|im_start|>system\n{system_prompt}<|im_end|>\n" + f"<|im_start|>user\n{user_prompt}<|im_end|>\n" + f"<|im_start|>assistant\n" + ) + + +def load_dataset_for_task(task): + if task == "game24": + return load_dataset("nlile/24-game", split="train") + if task == "maze": + return load_dataset("microsoft/VISION_LANGUAGE", "maze", split="val") + if task == "spatialmap": + return load_dataset("microsoft/VISION_LANGUAGE", "spatial_map_text_only", split="val") + raise ValueError(f"Unsupported task: {task}") + + +def resolve_indices(task, dataset_len, args): + if args.indices: + return [int(x.strip()) for x in args.indices.split(",")] + if args.xrange: + parts = args.xrange.split("-") + if len(parts) == 2: + try: + start = int(parts[0].strip()) + end = int(parts[1].strip()) + return range(start, end) + except ValueError: + raise ValueError(f"Invalid xrange format: {args.xrange}. Use 'start-end'") + if args.num_examples: + return np.linspace(0, dataset_len - 1, args.num_examples, dtype=int) + # Default: use full range + start = args.start if args.start is not None else 0 + end = args.end if args.end is not None else dataset_len + return range(start, end) + + +def run_k_samples(prompt, llm_server, k, seed): + outputs = [] + for i in range(k): + llm_server["payload"]["seed"] = seed + i + outputs.append(asyncio.run(stream_completion( + prompt, + llm_server=llm_server, + monitors=(), + add_delay=False, + termination_requires_validation=False, + async_execution=True, + ))) + return outputs + + +# --------------------- Critic model helpers --------------------- + +def build_game24_critic_prompt(nums, reasoning_output): + """Build critic prompt to evaluate Game of 24 solution.""" + return f"""You are a math verifier. Evaluate the following Game of 24 solution. + +Numbers: {nums} +Target: 24 + +Student's reasoning and answer: +{reasoning_output} + +Verify: +1. Does it use ALL four numbers exactly once? +2. Does each step follow correct arithmetic? +3. Does the final expression evaluate to exactly 24? + +Respond with ONLY: "CORRECT" or "INCORRECT" +""" + + +def build_mcq_critic_prompt(task, task_description, reasoning_output): + """Build critic prompt to evaluate MCQ solution.""" + task_name = "Maze" if task == "maze" else "Spatial Reasoning" + return f"""You are an expert {task_name} verifier. Evaluate the following solution. + +Task: +{task_description} + +Student's reasoning and answer: +{reasoning_output} + +Verify the correctness of the step-by-step reasoning and final answer. + +Respond with ONLY: "CORRECT" or "INCORRECT" +""" + + +async def evaluate_with_critic(output_text, task, example, critic_llm_server, tokenizer, nums=None): + """Use critic model to evaluate correctness of output.""" + try: + if task == "game24": + critic_prompt = build_game24_critic_prompt(nums, output_text) + else: + if task == "maze": + _, task_desc = build_maze_prompt(example) + else: + _, task_desc = build_spatialmap_prompt(example) + critic_prompt = build_mcq_critic_prompt(task, task_desc, output_text) + + critic_system = "You are a strict academic verifier." + full_prompt = f"<|im_start|>system\n{critic_system}<|im_end|>\n<|im_start|>user\n{critic_prompt}<|im_end|>\n<|im_start|>assistant\n" + + critic_output = await stream_completion( + full_prompt, + llm_server=critic_llm_server, + monitors=(), + add_delay=False, + termination_requires_validation=False, + async_execution=True, + ) + + is_correct = "CORRECT" in critic_output.upper() + return is_correct, critic_output + except Exception as e: + logger.warning(f"Critic evaluation failed: {e}") + return False, "" + + +def run_k_samples_with_critic( + prompt, + llm_server, + critic_llm_server, + k, + seed, + task, + example, + tokenizer, + eval_fn, + nums=None, + early_stop=False, +): + """Run up to K samples, evaluate with critic, and score with ground truth.""" + sample_results = [] + for i in range(k): + llm_server["payload"]["seed"] = seed + i + output = asyncio.run(stream_completion( + prompt, + llm_server=llm_server, + monitors=(), + add_delay=False, + termination_requires_validation=False, + async_execution=True, + )) + + critic_correct, critic_response = asyncio.run(evaluate_with_critic( + output, task, example, critic_llm_server, tokenizer, nums=nums + )) + is_correct, extracted, message = eval_fn(output) + token_count = count_tokens(output, tokenizer) + + sample_results.append(SampleResult( + output=output, + correct=is_correct, + extracted=extracted, + message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", + tokens=token_count, + critic_correct=critic_correct, + )) + + if early_stop and critic_correct: + break + + return sample_results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Best-of-K baseline (standard CoT) for TTSwithVerification datasets") + parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap"], + help="Task to run") + parser.add_argument("--k", type=int, default=4, help="Number of samples per example") + parser.add_argument("--num_examples", "-n", type=int, default=None, + help="Number of examples to run (overrides start/end)") + parser.add_argument("--indices", type=str, default=None, + help="Comma-separated indices to run") + parser.add_argument("--xrange", type=str, default=None, + help="Range of indices to run (format: 'start-end')") + parser.add_argument("--start", type=int, default=None, help="Start index") + parser.add_argument("--end", type=int, default=None, help="End index") + parser.add_argument("--main_model", type=str, default=MAIN_MODEL, help="Main model to use for generation") + parser.add_argument("--port", type=int, default=8000, help="vLLM server port") + parser.add_argument("--use_critic", action="store_true", help="Use critic model for evaluation instead of ground truth") + parser.add_argument("--critic_model", type=str, default=MAIN_MODEL, help="Critic model to use for evaluation") + parser.add_argument("--critic_port", type=int, default=8000, help="vLLM server port for critic model (default: same as main model port)") + parser.add_argument("--critic_early_stop", action="store_true", help="Stop sampling after first critic-correct trace") + parser.add_argument("--seed", type=int, default=42, help="Base random seed") + parser.add_argument("--max_tokens", type=int, default=32768, help="Max tokens for generation") + parser.add_argument("--temperature", type=float, default=0.6, help="Sampling temperature") + parser.add_argument("--debug", "-d", action="store_true", help="Enable debug logging") + args = parser.parse_args() + + log_level = logging.DEBUG if args.debug else logging.INFO + logging.basicConfig(level=log_level, format="%(message)s") + + + dataset = load_dataset_for_task(args.task) + indices = resolve_indices(args.task, len(dataset), args) + + llm_server = init_llm_server( + args.main_model, + max_tokens=args.max_tokens, + port=args.port, + temperature=args.temperature, + seed=args.seed, + ) + + critic_llm_server = None + if args.use_critic: + critic_llm_server = init_llm_server( + args.critic_model, + max_tokens=512, + port=args.critic_port, + temperature=0.2, + seed=args.seed, + ) + logger.info(f"Using critic model: {args.critic_model} on port {args.critic_port}") + + logger.info(f"Loading tokenizer for {args.main_model}...") + tokenizer = AutoTokenizer.from_pretrained(args.main_model, trust_remote_code=True) + logger.info("Tokenizer loaded successfully.") + + output_dirs = get_output_dirs(args.task, args.main_model) + + total_examples = 0 + total_correct = 0 + total_correct_samples = 0 + total_samples = 0 + critic_correct_samples = 0 + critic_total_samples = 0 + total_tokens = 0 + total_tokens_all_samples = 0 + results = [] + + for idx in tqdm(indices, desc="Processing examples", unit="example"): + example = dataset[int(idx)] + if args.task == "game24": + nums = example["numbers"] + prompt = build_full_prompt(args.task, example, nums=nums) + eval_fn = lambda output: evaluate_game24_answer(output, nums) + options = None + else: + prompt = build_full_prompt(args.task, example) + gt = str(example.get("ground_truth", "")).strip() + if gt == "Q4": + target_options = ["A", "B"] + else: + target_options = ["A", "B", "C", "D"] + if args.task == "maze": + _, user_prompt = build_maze_prompt(example) + else: + _, user_prompt = build_spatialmap_prompt(example) + options = extract_options_from_prompt(user_prompt, target_options) + eval_fn = lambda output: evaluate_mcq_answer(output, options, gt) + + logger.info(f"---- Example {idx} ----") + + if args.use_critic: + sample_results = run_k_samples_with_critic( + prompt, llm_server, critic_llm_server, args.k, args.seed, + args.task, example, tokenizer, eval_fn, nums=(nums if args.task == "game24" else None), + early_stop=args.critic_early_stop + ) + else: + outputs = run_k_samples(prompt, llm_server, args.k, args.seed) + sample_results = [] + for output in outputs: + is_correct, extracted, message = eval_fn(output) + token_count = count_tokens(output, tokenizer) + sample_results.append(SampleResult( + output=output, + correct=is_correct, + extracted=extracted, + message=message, + tokens=token_count, + critic_correct=None, + )) + + if args.use_critic: + best_idx = next((i for i, r in enumerate(sample_results) if r.critic_correct), 0) + else: + best_idx = next((i for i, r in enumerate(sample_results) if r.correct), 0) + best_result = sample_results[best_idx] + any_correct = any(r.correct for r in sample_results) + correct_samples = sum(1 for r in sample_results if r.correct) + critic_correct_samples_example = sum(1 for r in sample_results if r.critic_correct) + + save_outputs(idx, sample_results, best_idx, output_dirs["reasoning"]) + + total_examples += 1 + if any_correct: + total_correct += 1 + total_correct_samples += correct_samples + total_samples += len(sample_results) + critic_correct_samples += critic_correct_samples_example + critic_total_samples += len(sample_results) + total_tokens += best_result.tokens + total_tokens_all_samples += sum(r.tokens for r in sample_results) + + results.append({ + "idx": int(idx), + "best_idx": best_idx, + "any_correct": any_correct, + "best_correct": best_result.correct, + "best_critic_correct": best_result.critic_correct, + "best_extracted": best_result.extracted, + "best_message": best_result.message, + "best_tokens": best_result.tokens, + "all_tokens": [r.tokens for r in sample_results], + "all_correct": [r.correct for r in sample_results], + "all_critic_correct": [r.critic_correct for r in sample_results], + "options": options, + }) + + logger.info(f"Best sample: {best_idx} | Correct in K: {any_correct}") + logger.info(f"Best message: {best_result.message}") + + accuracy = total_correct / total_examples if total_examples else 0 + avg_best_tokens = total_tokens / total_examples if total_examples else 0 + avg_all_tokens = total_tokens_all_samples / total_examples if total_examples else 0 + + summary = { + "task": args.task, + "model": args.main_model, + "k": args.k, + "use_critic": args.use_critic, + "total_examples": total_examples, + "correct": total_correct, + "correct_samples": total_correct_samples, + "total_samples": total_samples, + "critic_correct_samples": critic_correct_samples, + "critic_total_samples": critic_total_samples, + "critic_accuracy": (critic_correct_samples / critic_total_samples) if critic_total_samples else 0, + "accuracy": accuracy, + "avg_best_tokens": avg_best_tokens, + "avg_all_tokens": avg_all_tokens, + "total_tokens_best": total_tokens, + "total_tokens_all_samples": total_tokens_all_samples, + "results": results, + } + + if args.use_critic: + summary["critic_model"] = args.critic_model + summary["critic_port"] = args.critic_port + summary["critic_early_stop"] = args.critic_early_stop + + summary_path = os.path.join(output_dirs["base"], "summary.json") + with open(summary_path, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + logger.info(f"Saved summary to {summary_path}") From bfc7d774b372b9d67f8240892408bccb48cca16a Mon Sep 17 00:00:00 2001 From: Prateek Chanda Date: Fri, 20 Feb 2026 04:49:21 +0000 Subject: [PATCH 02/11] add fixes --- .../TTSwithVerification/bestofk_baseline.py | 119 ++++++++++++++++-- 1 file changed, 106 insertions(+), 13 deletions(-) diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index 8864a38a..0a1a0c18 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -29,6 +29,7 @@ class SampleResult: message: str tokens: int critic_correct: Optional[bool] = None + critic_feedback: Optional[str] = None def get_model_short_name(model_name: str) -> str: @@ -86,6 +87,8 @@ def save_outputs(idx: int, outputs: List[SampleResult], best_idx: int, output_di f.write(f"EXTRACTED={result.extracted}\n") f.write(f"TOKENS={result.tokens}\n") f.write(f"MESSAGE={result.message}\n") + if result.critic_feedback: + f.write(f"CRITIC_FEEDBACK={result.critic_feedback}\n") f.write("\n") f.write(result.output) f.write("\n") @@ -292,25 +295,31 @@ def resolve_indices(task, dataset_len, args): return range(start, end) -def run_k_samples(prompt, llm_server, k, seed): - outputs = [] +async def run_k_samples_async(prompt, llm_server, k, seed): + """Parallelized version - runs all k samples concurrently.""" + tasks = [] for i in range(k): llm_server["payload"]["seed"] = seed + i - outputs.append(asyncio.run(stream_completion( + tasks.append(stream_completion( prompt, llm_server=llm_server, monitors=(), add_delay=False, termination_requires_validation=False, async_execution=True, - ))) - return outputs + )) + return await asyncio.gather(*tasks) + + +def run_k_samples(prompt, llm_server, k, seed): + """Wrapper to run parallelized k samples.""" + return asyncio.run(run_k_samples_async(prompt, llm_server, k, seed)) # --------------------- Critic model helpers --------------------- def build_game24_critic_prompt(nums, reasoning_output): - """Build critic prompt to evaluate Game of 24 solution.""" + """Build critic prompt to evaluate Game of 24 solution and provide reasoning.""" return f"""You are a math verifier. Evaluate the following Game of 24 solution. Numbers: {nums} @@ -324,12 +333,17 @@ def build_game24_critic_prompt(nums, reasoning_output): 2. Does each step follow correct arithmetic? 3. Does the final expression evaluate to exactly 24? -Respond with ONLY: "CORRECT" or "INCORRECT" +Respond in the following format: +VERDICT: CORRECT or INCORRECT +REASONING: Your detailed explanation + +If CORRECT, briefly explain why. +If INCORRECT, explain what went wrong and how to fix it. """ def build_mcq_critic_prompt(task, task_description, reasoning_output): - """Build critic prompt to evaluate MCQ solution.""" + """Build critic prompt to evaluate MCQ solution and provide reasoning.""" task_name = "Maze" if task == "maze" else "Spatial Reasoning" return f"""You are an expert {task_name} verifier. Evaluate the following solution. @@ -341,12 +355,17 @@ def build_mcq_critic_prompt(task, task_description, reasoning_output): Verify the correctness of the step-by-step reasoning and final answer. -Respond with ONLY: "CORRECT" or "INCORRECT" +Respond in the following format: +VERDICT: CORRECT or INCORRECT +REASONING: Your detailed explanation + +If CORRECT, briefly explain why. +If INCORRECT, explain what went wrong and suggest the correct approach. """ async def evaluate_with_critic(output_text, task, example, critic_llm_server, tokenizer, nums=None): - """Use critic model to evaluate correctness of output.""" + """Use critic model to evaluate correctness of output and extract reasoning feedback.""" try: if task == "game24": critic_prompt = build_game24_critic_prompt(nums, output_text) @@ -370,7 +389,15 @@ async def evaluate_with_critic(output_text, task, example, critic_llm_server, to ) is_correct = "CORRECT" in critic_output.upper() - return is_correct, critic_output + + # Extract reasoning from verdict and reasoning format + reasoning = "" + if "REASONING:" in critic_output: + reasoning = critic_output.split("REASONING:", 1)[1].strip() + elif "VERDICT:" not in critic_output: + reasoning = critic_output + + return is_correct, reasoning except Exception as e: logger.warning(f"Critic evaluation failed: {e}") return False, "" @@ -390,6 +417,22 @@ def run_k_samples_with_critic( early_stop=False, ): """Run up to K samples, evaluate with critic, and score with ground truth.""" + if early_stop: + # Sequential execution for early stopping + return _run_k_samples_with_critic_sequential( + prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums + ) + else: + # Parallelized execution when not using early stopping + return asyncio.run(_run_k_samples_with_critic_parallel( + prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums + )) + + +def _run_k_samples_with_critic_sequential( + prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums=None +): + """Sequential version - required for early stopping.""" sample_results = [] for i in range(k): llm_server["payload"]["seed"] = seed + i @@ -402,7 +445,7 @@ def run_k_samples_with_critic( async_execution=True, )) - critic_correct, critic_response = asyncio.run(evaluate_with_critic( + critic_correct, critic_feedback = asyncio.run(evaluate_with_critic( output, task, example, critic_llm_server, tokenizer, nums=nums )) is_correct, extracted, message = eval_fn(output) @@ -415,14 +458,60 @@ def run_k_samples_with_critic( message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", tokens=token_count, critic_correct=critic_correct, + critic_feedback=critic_feedback, )) - if early_stop and critic_correct: + if critic_correct: break return sample_results +async def _run_k_samples_with_critic_parallel( + prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums=None +): + """Parallelized version - runs all k samples concurrently and evaluates with critic.""" + # Step 1: Generate all outputs in parallel + output_tasks = [] + for i in range(k): + llm_server["payload"]["seed"] = seed + i + output_tasks.append(stream_completion( + prompt, + llm_server=llm_server, + monitors=(), + add_delay=False, + termination_requires_validation=False, + async_execution=True, + )) + outputs = await asyncio.gather(*output_tasks) + + # Step 2: Evaluate all outputs in parallel + evaluation_tasks = [] + for output in outputs: + evaluation_tasks.append(evaluate_with_critic( + output, task, example, critic_llm_server, tokenizer, nums=nums + )) + critic_evaluations = await asyncio.gather(*evaluation_tasks) + + # Step 3: Compile results + sample_results = [] + for output, (critic_correct, critic_feedback) in zip(outputs, critic_evaluations): + is_correct, extracted, message = eval_fn(output) + token_count = count_tokens(output, tokenizer) + + sample_results.append(SampleResult( + output=output, + correct=is_correct, + extracted=extracted, + message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", + tokens=token_count, + critic_correct=critic_correct, + critic_feedback=critic_feedback, + )) + + return sample_results + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Best-of-K baseline (standard CoT) for TTSwithVerification datasets") parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap"], @@ -442,6 +531,7 @@ def run_k_samples_with_critic( parser.add_argument("--critic_model", type=str, default=MAIN_MODEL, help="Critic model to use for evaluation") parser.add_argument("--critic_port", type=int, default=8000, help="vLLM server port for critic model (default: same as main model port)") parser.add_argument("--critic_early_stop", action="store_true", help="Stop sampling after first critic-correct trace") + parser.add_argument("--critic_feedback_baseline", action="store_true", help="Use critic feedback as a separate baseline for post-hoc correction") parser.add_argument("--seed", type=int, default=42, help="Base random seed") parser.add_argument("--max_tokens", type=int, default=32768, help="Max tokens for generation") parser.add_argument("--temperature", type=float, default=0.6, help="Sampling temperature") @@ -563,10 +653,12 @@ def run_k_samples_with_critic( "best_critic_correct": best_result.critic_correct, "best_extracted": best_result.extracted, "best_message": best_result.message, + "best_critic_feedback": best_result.critic_feedback, "best_tokens": best_result.tokens, "all_tokens": [r.tokens for r in sample_results], "all_correct": [r.correct for r in sample_results], "all_critic_correct": [r.critic_correct for r in sample_results], + "all_critic_feedback": [r.critic_feedback for r in sample_results], "options": options, }) @@ -601,6 +693,7 @@ def run_k_samples_with_critic( summary["critic_model"] = args.critic_model summary["critic_port"] = args.critic_port summary["critic_early_stop"] = args.critic_early_stop + summary["critic_feedback_baseline"] = args.critic_feedback_baseline summary_path = os.path.join(output_dirs["base"], "summary.json") with open(summary_path, "w", encoding="utf-8") as f: From fb28eba2e6e4bd52eabf3f32ddd1eaa0771f7ad5 Mon Sep 17 00:00:00 2001 From: Prateek Chanda Date: Sat, 21 Feb 2026 09:15:56 +0000 Subject: [PATCH 03/11] add multiprocess vllm setup and ignore outputs --- .gitignore | 2 + .../MULTIPROCESS_README.md | 67 +++ .../TTSwithVerification/bestofk_baseline.py | 493 +++++++++++------- examples/TTSwithVerification/cleanup_vllm.sh | 26 + .../TTSwithVerification/run_experiments.py | 164 ++++++ .../start_vllm_multiprocess.sh | 98 ++++ 6 files changed, 676 insertions(+), 174 deletions(-) create mode 100644 examples/TTSwithVerification/MULTIPROCESS_README.md create mode 100755 examples/TTSwithVerification/cleanup_vllm.sh create mode 100644 examples/TTSwithVerification/run_experiments.py create mode 100755 examples/TTSwithVerification/start_vllm_multiprocess.sh diff --git a/.gitignore b/.gitignore index b7faf403..d7b37631 100644 --- a/.gitignore +++ b/.gitignore @@ -198,6 +198,8 @@ cython_debug/ # Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to # exclude from AI features like autocomplete and code analysis. Recommended for sensitive data # refer to https://docs.cursor.com/context/ignore-files +.*/Outputs_TTS/ +Outputs_TTS/ .cursorignore .cursorindexingignore diff --git a/examples/TTSwithVerification/MULTIPROCESS_README.md b/examples/TTSwithVerification/MULTIPROCESS_README.md new file mode 100644 index 00000000..4a64612d --- /dev/null +++ b/examples/TTSwithVerification/MULTIPROCESS_README.md @@ -0,0 +1,67 @@ +# Multi-Process vLLM Setup for Best-of-K Baseline + +This directory contains scripts and code for running the best-of-K baseline with multi-process vLLM serving. + +## Setup + +### 1. Start vLLM with 4 processes (2 GPUs each) + +```bash +bash start_vllm_multiprocess.sh +``` + +This launches 4 vLLM OpenAI-compatible API servers: +- **Process 1**: GPUs 0-1, Port 8000 +- **Process 2**: GPUs 2-3, Port 8001 +- **Process 3**: GPUs 4-5, Port 8002 +- **Process 4**: GPUs 6-7, Port 8003 + +Each process uses `tensor-parallel-size 2` for distributed inference. + +### 2. Run the baseline + +In a separate terminal: + +```bash +# Test with 1 example +python bestofk_baseline.py --task game24 --num_examples 1 --k 4 --use_critic + +# Run on maze dataset +python bestofk_baseline.py --task maze --num_examples 10 --k 4 + +# Run on spatialmap dataset +python bestofk_baseline.py --task spatialmap --num_examples 5 --k 4 +``` + +Or use the test script: +```bash +bash run_multiprocess_test.sh game24 5 +``` + +## Load Balancing + +- Requests are distributed **round-robin** across the 4 vLLM instances +- Each generation request goes to the next available port (8000 → 8001 → 8002 → 8003 → 8000 ...) +- Critic evaluation requests use separate round-robin tracking (independent counter) +- This ensures even load distribution across all 4 GPU pairs + +## Stopping vLLM + +```bash +pkill -9 -f "vllm.entrypoints.openai.api_server" +``` + +## Configuration + +Edit `start_vllm_multiprocess.sh` to change: +- `MODEL`: Model name (default: `Qwen/QwQ-32B`) +- `MAX_TOKENS`: Maximum sequence length (default: 8192) +- `GPU_MEMORY`: GPU memory utilization (default: 0.4) +- `TENSOR_PARALLEL`: Must be ≤ 2 for this 8-GPU setup + +## Benefits + +- **Better throughput**: 4 independent processes handle requests in parallel +- **Fault tolerance**: If one process crashes, others continue +- **GPU utilization**: Balanced load across all 8 GPUs (2 GPUs per process) +- **Reduced latency**: Each process has dedicated GPU resources diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index 0a1a0c18..9b52206d 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -4,10 +4,14 @@ import logging import os import re +import sys +from contextlib import contextmanager from dataclasses import dataclass -from typing import List, Optional +from typing import Dict, List, Optional import numpy as np +import pandas as pd +import aiohttp from datasets import load_dataset from tqdm import tqdm from transformers import AutoTokenizer @@ -15,12 +19,30 @@ from interwhen import stream_completion # ============== MODEL CONFIGURATION ============== -MAIN_MODEL = "Qwen/Qwen3-30B-A3B-Thinking-2507" -# ================================================= +MAIN_MODEL = "Qwen/QwQ-32B" +# Multi-process vLLM configuration +VLLM_PORTS = [8000, 8001, 8002, 8003] # 4 instances with tensor-parallel-size 2 each +REQUEST_COUNTER = {"main": 0, "critic": 0} # Track request count for round-robin load balancing + logger = logging.getLogger(__name__) +@contextmanager +def suppress_output(): + """Context manager to suppress stdout and stderr.""" + with open(os.devnull, 'w') as devnull: + old_stdout = sys.stdout + old_stderr = sys.stderr + sys.stdout = devnull + sys.stderr = devnull + try: + yield + finally: + sys.stdout = old_stdout + sys.stderr = old_stderr + + @dataclass class SampleResult: output: str @@ -37,15 +59,27 @@ def get_model_short_name(model_name: str) -> str: return short_name.replace(" ", "_").replace(":", "-") -def get_output_dirs(task: str, main_model: str, base_dir: str = "../../b-pchanda/Outputs_TTS/BestOfKResults"): +def get_next_port(server_type: str = "main") -> int: + """Get next vLLM port in round-robin fashion.""" + global REQUEST_COUNTER + port = VLLM_PORTS[REQUEST_COUNTER[server_type] % len(VLLM_PORTS)] + REQUEST_COUNTER[server_type] += 1 + return port + + +def get_output_dirs(task: str, main_model: str, use_critic: bool, critic_early_stop: bool, base_dir: str = "../../b-pchanda/Outputs_TTS/BestOfKResults"): model_short_name = get_model_short_name(main_model) - output_base = os.path.join(base_dir, task, model_short_name) + critic_status = "on" if use_critic else "off" + earlystop_status = "on" if critic_early_stop else "off" + output_base = os.path.join(base_dir, task, model_short_name, f"critic_{critic_status}", f"earlystop_{earlystop_status}") dirs = { "base": output_base, "reasoning": os.path.join(output_base, "Reasoning_output"), + "critic": os.path.join(output_base, "Critic_output") if use_critic else None, } for dir_path in dirs.values(): - os.makedirs(dir_path, exist_ok=True) + if dir_path: + os.makedirs(dir_path, exist_ok=True) return dirs @@ -59,7 +93,7 @@ def init_llm_server(model_name, max_tokens=32768, port=8000, temperature=0.6, se "min_p": 0.0, "do_sample": True, "temperature": temperature, - "stream": True, + "stream": False, "logprobs": 20, "use_beam_search": False, "prompt_cache": True, @@ -70,8 +104,16 @@ def init_llm_server(model_name, max_tokens=32768, port=8000, temperature=0.6, se def count_tokens(text: str, tokenizer) -> int: - tokens = tokenizer.encode(text, add_special_tokens=False) - return len(tokens) + """Count tokens in text, with fallback to character count.""" + try: + if not text or len(text.strip()) == 0: + return 0 + tokens = tokenizer.encode(text, add_special_tokens=False) + return len(tokens) + except Exception as e: + logger.warning(f"Tokenization failed: {e}, using character count estimate") + # Rough estimate: ~4 characters per token + return max(1, len(text) // 4) def save_outputs(idx: int, outputs: List[SampleResult], best_idx: int, output_dir: str): @@ -92,7 +134,7 @@ def save_outputs(idx: int, outputs: List[SampleResult], best_idx: int, output_di f.write("\n") f.write(result.output) f.write("\n") - logger.info(f"Saved outputs to {filepath}") + # logger.info(f"Saved outputs to {filepath}") # --------------------- Game24 helpers --------------------- @@ -211,14 +253,30 @@ def build_spatialmap_prompt(example): def extract_solution_mcq(text): - matches = re.findall(r"\\boxed\{([^}]*)\}", text) - if not matches: - return None - expr = matches[-1].strip() - choice_match = re.search(r"\b([ABCD])\b", expr, flags=re.IGNORECASE) - if not choice_match: - return None - return choice_match.group(1).upper() + """Extract MCQ solution from model output.""" + # Try multiple boxed patterns + patterns = [ + r"\\boxed\{([^}]*)\}", # \boxed{...} + r"boxed\{([^}]*)\}", # boxed{...} without escape + r"\*\*([A-D])\*\*", # **A** format + r"answer[:\s]*([A-D])", # answer: A format + r"(?:^|\n)([A-D])(?:\s|$|\.)", # Standalone letter + ] + + for pattern in patterns: + matches = re.findall(pattern, text, re.IGNORECASE) + if matches: + expr = matches[-1].strip() + choice_match = re.search(r"\b([ABCD])\b", expr, flags=re.IGNORECASE) + if choice_match: + return choice_match.group(1).upper() + + # Last resort: look for any standalone A, B, C, or D + standalone = re.findall(r"\b([ABCD])\b", text) + if standalone: + return standalone[-1].upper() + + return None def extract_options_from_prompt(prompt_text, target_options): @@ -269,7 +327,7 @@ def load_dataset_for_task(task): if task == "game24": return load_dataset("nlile/24-game", split="train") if task == "maze": - return load_dataset("microsoft/VISION_LANGUAGE", "maze", split="val") + return load_dataset("microsoft/VISION_LANGUAGE", "maze_text_only", split="val") if task == "spatialmap": return load_dataset("microsoft/VISION_LANGUAGE", "spatial_map_text_only", split="val") raise ValueError(f"Unsupported task: {task}") @@ -295,25 +353,75 @@ def resolve_indices(task, dataset_len, args): return range(start, end) -async def run_k_samples_async(prompt, llm_server, k, seed): - """Parallelized version - runs all k samples concurrently.""" - tasks = [] +def batch_generate_samples(prompt, llm_server, k, seed, quiet=True): + """Generate k samples using vLLM batch processing via API across multiple instances.""" + payload_template = llm_server["payload"].copy() + headers = llm_server["headers"] + + # Create k requests with different seeds + batch_payloads = [] for i in range(k): - llm_server["payload"]["seed"] = seed + i - tasks.append(stream_completion( - prompt, - llm_server=llm_server, - monitors=(), - add_delay=False, - termination_requires_validation=False, - async_execution=True, - )) - return await asyncio.gather(*tasks) + payload = payload_template.copy() + payload["prompt"] = prompt + payload["seed"] = seed + i + batch_payloads.append(payload) + + # Send requests to vLLM instances in parallel (true concurrency) + async def _fetch_one(session, sem, idx, url, payload): + async with sem: + try: + async with session.post(url, json=payload, headers=headers, timeout=300) as resp: + text = await resp.text() + if resp.status >= 400: + logger.warning(f"HTTP error for seed {seed + idx} on {url}: {resp.status} - {text[:200]}") + return idx, "" + try: + result = json.loads(text) + except json.JSONDecodeError: + logger.warning(f"Invalid JSON for seed {seed + idx} on {url}") + return idx, "" + except Exception as e: + logger.warning(f"Batch generation failed for seed {seed + idx} on {url}: {e}") + return idx, "" + + if "choices" in result and len(result["choices"]) > 0: + choice = result["choices"][0] + if isinstance(choice, dict): + output_text = choice.get("text") or choice.get("message", {}).get("content", "") + else: + output_text = str(choice) + if output_text and len(output_text.strip()) > 0: + return idx, output_text + logger.warning(f"Empty output for seed {seed + idx} on {url}") + return idx, "" + + logger.warning(f"No choices in response for seed {seed + idx} on {url}: {result.keys() if isinstance(result, dict) else type(result)}") + return idx, "" + + async def _run_parallel(): + sem = asyncio.Semaphore(len(VLLM_PORTS)) + async with aiohttp.ClientSession() as session: + tasks = [] + for i, payload in enumerate(batch_payloads): + port = get_next_port(server_type="main") + url = f"http://localhost:{port}/v1/completions" + tasks.append(asyncio.create_task(_fetch_one(session, sem, i, url, payload))) + results = await asyncio.gather(*tasks) + return results + + if quiet: + with suppress_output(): + results = asyncio.run(_run_parallel()) + else: + results = asyncio.run(_run_parallel()) + outputs = [""] * k + for idx, output_text in results: + outputs[idx] = output_text + if output_text and not quiet: + print(f"[Generated sample {idx}] {len(output_text)} chars, {len(output_text.split())} words") -def run_k_samples(prompt, llm_server, k, seed): - """Wrapper to run parallelized k samples.""" - return asyncio.run(run_k_samples_async(prompt, llm_server, k, seed)) + return outputs # --------------------- Critic model helpers --------------------- @@ -364,43 +472,86 @@ def build_mcq_critic_prompt(task, task_description, reasoning_output): """ -async def evaluate_with_critic(output_text, task, example, critic_llm_server, tokenizer, nums=None): - """Use critic model to evaluate correctness of output and extract reasoning feedback.""" - try: - if task == "game24": - critic_prompt = build_game24_critic_prompt(nums, output_text) - else: - if task == "maze": - _, task_desc = build_maze_prompt(example) +def batch_evaluate_with_critic(outputs_df, task, example, critic_llm_server, tokenizer, nums=None, quiet=True): + """Batch evaluate outputs using vLLM API across multiple instances. Outputs_df should have columns: 'output', 'seed_idx'""" + payload_template = critic_llm_server["payload"].copy() + headers = critic_llm_server["headers"] + + async def _fetch_one(session, sem, idx, url, payload): + async with sem: + try: + async with session.post(url, json=payload, headers=headers, timeout=300) as resp: + text = await resp.text() + if resp.status >= 400: + logger.warning(f"HTTP error for critic sample {idx} on {url}: {resp.status} - {text[:200]}") + return idx, "", False + try: + result = json.loads(text) + except json.JSONDecodeError: + logger.warning(f"Invalid JSON for critic sample {idx} on {url}") + return idx, "", False + except Exception as e: + logger.warning(f"Critic evaluation failed for sample {idx} on {url}: {e}") + return idx, "", False + + if "choices" in result and len(result["choices"]) > 0: + choice = result["choices"][0] + critic_output = choice.get("text") or choice.get("message", {}).get("content", "") else: - _, task_desc = build_spatialmap_prompt(example) - critic_prompt = build_mcq_critic_prompt(task, task_desc, output_text) - - critic_system = "You are a strict academic verifier." - full_prompt = f"<|im_start|>system\n{critic_system}<|im_end|>\n<|im_start|>user\n{critic_prompt}<|im_end|>\n<|im_start|>assistant\n" - - critic_output = await stream_completion( - full_prompt, - llm_server=critic_llm_server, - monitors=(), - add_delay=False, - termination_requires_validation=False, - async_execution=True, - ) - - is_correct = "CORRECT" in critic_output.upper() - - # Extract reasoning from verdict and reasoning format - reasoning = "" - if "REASONING:" in critic_output: - reasoning = critic_output.split("REASONING:", 1)[1].strip() - elif "VERDICT:" not in critic_output: - reasoning = critic_output - - return is_correct, reasoning - except Exception as e: - logger.warning(f"Critic evaluation failed: {e}") - return False, "" + critic_output = "" + + is_correct = "CORRECT" in critic_output.upper() + reasoning = "" + if "REASONING:" in critic_output: + reasoning = critic_output.split("REASONING:", 1)[1].strip() + elif "VERDICT:" not in critic_output: + reasoning = critic_output + + return idx, reasoning, is_correct + + async def _run_parallel(): + sem = asyncio.Semaphore(len(VLLM_PORTS)) + async with aiohttp.ClientSession() as session: + tasks = [] + for idx, row in outputs_df.iterrows(): + output_text = row["output"] + if task == "game24": + critic_prompt = build_game24_critic_prompt(nums, output_text) + else: + if task == "maze": + _, task_desc = build_maze_prompt(example) + else: + _, task_desc = build_spatialmap_prompt(example) + critic_prompt = build_mcq_critic_prompt(task, task_desc, output_text) + + critic_system = "You are a strict academic verifier." + full_prompt = f"<|im_start|>system\n{critic_system}<|im_end|>\n<|im_start|>user\n{critic_prompt}<|im_end|>\n<|im_start|>assistant\n" + + payload = payload_template.copy() + payload["prompt"] = full_prompt + payload["seed"] = row.get("critic_seed", idx) + + port = get_next_port(server_type="critic") + url = f"http://localhost:{port}/v1/completions" + tasks.append(asyncio.create_task(_fetch_one(session, sem, idx, url, payload))) + + return await asyncio.gather(*tasks) + + if quiet: + with suppress_output(): + results = asyncio.run(_run_parallel()) + else: + results = asyncio.run(_run_parallel()) + + rows = [] + for sample_idx, reasoning, is_correct in results: + rows.append({ + "sample_idx": sample_idx, + "critic_correct": is_correct, + "critic_feedback": reasoning, + }) + + return pd.DataFrame(rows) def run_k_samples_with_critic( @@ -415,101 +566,84 @@ def run_k_samples_with_critic( eval_fn, nums=None, early_stop=False, + quiet=True, ): - """Run up to K samples, evaluate with critic, and score with ground truth.""" + """Run k samples with critic evaluation using vLLM batching.""" + # Generate k samples + outputs = batch_generate_samples(prompt, llm_server, k, seed, quiet=quiet) + + # Create dataframe with outputs + df_samples = pd.DataFrame({ + "sample_idx": range(k), + "output": outputs, + "seed": [seed + i for i in range(k)], + }) + + # If early stop mode, stop at first critic-correct if early_stop: - # Sequential execution for early stopping - return _run_k_samples_with_critic_sequential( - prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums - ) + sample_results = [] + for idx, row in df_samples.iterrows(): + output = row["output"] + + # Evaluate with critic + df_critic = batch_evaluate_with_critic( + pd.DataFrame([{"output": output, "seed_idx": idx}]), + task, example, critic_llm_server, tokenizer, nums=nums, quiet=quiet + ) + critic_correct = df_critic.iloc[0]["critic_correct"] if len(df_critic) > 0 else False + critic_feedback = df_critic.iloc[0]["critic_feedback"] if len(df_critic) > 0 else "" + + # Evaluate with ground truth + is_correct, extracted, message = eval_fn(output) + token_count = count_tokens(output, tokenizer) + + sample_results.append(SampleResult( + output=output, + correct=is_correct, + extracted=extracted, + message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", + tokens=token_count, + critic_correct=critic_correct, + critic_feedback=critic_feedback, + )) + + if critic_correct: + break + + return sample_results else: - # Parallelized execution when not using early stopping - return asyncio.run(_run_k_samples_with_critic_parallel( - prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums - )) + # Batch critic evaluation + df_critic = batch_evaluate_with_critic( + df_samples, task, example, critic_llm_server, tokenizer, nums=nums, quiet=quiet + ) + + # Merge critic results + df_samples = df_samples.merge(df_critic, left_index=True, right_on="sample_idx", how="left") + + # Process all results + sample_results = [] + for idx, row in df_samples.iterrows(): + output = row["output"] + critic_correct = row.get("critic_correct", False) + critic_feedback = row.get("critic_feedback", "") + + is_correct, extracted, message = eval_fn(output) + token_count = count_tokens(output, tokenizer) + + sample_results.append(SampleResult( + output=output, + correct=is_correct, + extracted=extracted, + message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", + tokens=token_count, + critic_correct=critic_correct, + critic_feedback=critic_feedback, + )) + + return sample_results + -def _run_k_samples_with_critic_sequential( - prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums=None -): - """Sequential version - required for early stopping.""" - sample_results = [] - for i in range(k): - llm_server["payload"]["seed"] = seed + i - output = asyncio.run(stream_completion( - prompt, - llm_server=llm_server, - monitors=(), - add_delay=False, - termination_requires_validation=False, - async_execution=True, - )) - - critic_correct, critic_feedback = asyncio.run(evaluate_with_critic( - output, task, example, critic_llm_server, tokenizer, nums=nums - )) - is_correct, extracted, message = eval_fn(output) - token_count = count_tokens(output, tokenizer) - - sample_results.append(SampleResult( - output=output, - correct=is_correct, - extracted=extracted, - message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", - tokens=token_count, - critic_correct=critic_correct, - critic_feedback=critic_feedback, - )) - - if critic_correct: - break - - return sample_results - - -async def _run_k_samples_with_critic_parallel( - prompt, llm_server, critic_llm_server, k, seed, task, example, tokenizer, eval_fn, nums=None -): - """Parallelized version - runs all k samples concurrently and evaluates with critic.""" - # Step 1: Generate all outputs in parallel - output_tasks = [] - for i in range(k): - llm_server["payload"]["seed"] = seed + i - output_tasks.append(stream_completion( - prompt, - llm_server=llm_server, - monitors=(), - add_delay=False, - termination_requires_validation=False, - async_execution=True, - )) - outputs = await asyncio.gather(*output_tasks) - - # Step 2: Evaluate all outputs in parallel - evaluation_tasks = [] - for output in outputs: - evaluation_tasks.append(evaluate_with_critic( - output, task, example, critic_llm_server, tokenizer, nums=nums - )) - critic_evaluations = await asyncio.gather(*evaluation_tasks) - - # Step 3: Compile results - sample_results = [] - for output, (critic_correct, critic_feedback) in zip(outputs, critic_evaluations): - is_correct, extracted, message = eval_fn(output) - token_count = count_tokens(output, tokenizer) - - sample_results.append(SampleResult( - output=output, - correct=is_correct, - extracted=extracted, - message=f"Critic verdict: {'CORRECT' if critic_correct else 'INCORRECT'} | {message}", - tokens=token_count, - critic_correct=critic_correct, - critic_feedback=critic_feedback, - )) - - return sample_results if __name__ == "__main__": @@ -538,11 +672,16 @@ async def _run_k_samples_with_critic_parallel( parser.add_argument("--debug", "-d", action="store_true", help="Enable debug logging") args = parser.parse_args() - log_level = logging.DEBUG if args.debug else logging.INFO + log_level = logging.DEBUG if args.debug else logging.ERROR logging.basicConfig(level=log_level, format="%(message)s") + quiet_mode = not args.debug - dataset = load_dataset_for_task(args.task) + if quiet_mode: + with suppress_output(): + dataset = load_dataset_for_task(args.task) + else: + dataset = load_dataset_for_task(args.task) indices = resolve_indices(args.task, len(dataset), args) llm_server = init_llm_server( @@ -562,13 +701,17 @@ async def _run_k_samples_with_critic_parallel( temperature=0.2, seed=args.seed, ) - logger.info(f"Using critic model: {args.critic_model} on port {args.critic_port}") + # logger.info(f"Using critic model: {args.critic_model} on port {args.critic_port}") - logger.info(f"Loading tokenizer for {args.main_model}...") - tokenizer = AutoTokenizer.from_pretrained(args.main_model, trust_remote_code=True) - logger.info("Tokenizer loaded successfully.") + # logger.info(f"Loading tokenizer for {args.main_model}...") + if quiet_mode: + with suppress_output(): + tokenizer = AutoTokenizer.from_pretrained(args.main_model, trust_remote_code=True) + else: + tokenizer = AutoTokenizer.from_pretrained(args.main_model, trust_remote_code=True) + # logger.info("Tokenizer loaded successfully.") - output_dirs = get_output_dirs(args.task, args.main_model) + output_dirs = get_output_dirs(args.task, args.main_model, args.use_critic, args.critic_early_stop) total_examples = 0 total_correct = 0 @@ -601,16 +744,18 @@ async def _run_k_samples_with_critic_parallel( options = extract_options_from_prompt(user_prompt, target_options) eval_fn = lambda output: evaluate_mcq_answer(output, options, gt) - logger.info(f"---- Example {idx} ----") + #logger.info(f"---- Example {idx} ----") + + quiet_mode = not args.debug if args.use_critic: sample_results = run_k_samples_with_critic( prompt, llm_server, critic_llm_server, args.k, args.seed, args.task, example, tokenizer, eval_fn, nums=(nums if args.task == "game24" else None), - early_stop=args.critic_early_stop + early_stop=args.critic_early_stop, quiet=quiet_mode ) else: - outputs = run_k_samples(prompt, llm_server, args.k, args.seed) + outputs = batch_generate_samples(prompt, llm_server, args.k, args.seed, quiet=quiet_mode) sample_results = [] for output in outputs: is_correct, extracted, message = eval_fn(output) @@ -662,8 +807,8 @@ async def _run_k_samples_with_critic_parallel( "options": options, }) - logger.info(f"Best sample: {best_idx} | Correct in K: {any_correct}") - logger.info(f"Best message: {best_result.message}") + #logger.info(f"Best sample: {best_idx} | Correct in K: {any_correct}") + #logger.info(f"Best message: {best_result.message}") accuracy = total_correct / total_examples if total_examples else 0 avg_best_tokens = total_tokens / total_examples if total_examples else 0 @@ -698,4 +843,4 @@ async def _run_k_samples_with_critic_parallel( summary_path = os.path.join(output_dirs["base"], "summary.json") with open(summary_path, "w", encoding="utf-8") as f: json.dump(summary, f, indent=2) - logger.info(f"Saved summary to {summary_path}") + # logger.info(f"Saved summary to {summary_path}") diff --git a/examples/TTSwithVerification/cleanup_vllm.sh b/examples/TTSwithVerification/cleanup_vllm.sh new file mode 100755 index 00000000..f552ec11 --- /dev/null +++ b/examples/TTSwithVerification/cleanup_vllm.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Cleanup script to kill all vLLM processes and Python instances + +echo "Stopping all vLLM processes..." +pkill -9 -f "vllm.entrypoints.openai.api_server" + +echo "Stopping Python processes..." +pkill -9 -f "bestofk_baseline.py" + +sleep 2 + +echo "Verifying all processes stopped..." +if pgrep -f "vllm.entrypoints.openai.api_server" > /dev/null; then + echo "WARNING: Some vLLM processes still running" +else + echo "✓ All vLLM processes stopped" +fi + +if pgrep -f "bestofk_baseline.py" > /dev/null; then + echo "WARNING: Some Python processes still running" +else + echo "✓ All Python processes stopped" +fi + +echo "Cleanup complete" diff --git a/examples/TTSwithVerification/run_experiments.py b/examples/TTSwithVerification/run_experiments.py new file mode 100644 index 00000000..a52952b0 --- /dev/null +++ b/examples/TTSwithVerification/run_experiments.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +""" +Job scheduler for running bestofk_baseline.py experiments sequentially. +""" +import subprocess +import sys +import time +from datetime import datetime +from pathlib import Path + +# Base command +BASE_CMD = "python /data/b-pchanda/interwhen/examples/TTSwithVerification/bestofk_baseline.py" + +# Define your experiment configurations +EXPERIMENTS = [ + # Maze experiments + { + "name": "maze_k4_critic", + "args": "--task maze --k 4 --use_critic" + }, + { + "name": "maze_k4_critic_earlystop", + "args": "--task maze --k 4 --use_critic --critic_early_stop" + }, + { + "name": "maze_k4_no_critic", + "args": "--task maze --k 4" + }, + + # Game24 experiments + { + "name": "game24_k4_critic", + "args": "--task game24 --k 4 --use_critic" + }, + { + "name": "game24_k4_critic_earlystop", + "args": "--task game24 --k 4 --use_critic --critic_early_stop" + }, + { + "name": "game24_k4_no_critic", + "args": "--task game24 --k 4" + }, + + # Spatialmap experiments + { + "name": "spatialmap_k4_critic", + "args": "--task spatialmap --k 4 --use_critic" + }, + { + "name": "spatialmap_k4_critic_earlystop", + "args": "--task spatialmap --k 4 --use_critic --critic_early_stop" + }, + { + "name": "spatialmap_k4_no_critic", + "args": "--task spatialmap --k 4" + }, +] + + +def run_experiment(exp_config, exp_num, total_exps): + """Run a single experiment.""" + name = exp_config["name"] + args = exp_config["args"] + + print("\n" + "="*80) + print(f"Experiment [{exp_num}/{total_exps}]: {name}") + print(f"Command: {BASE_CMD} {args}") + print("="*80) + + start_time = time.time() + start_ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + try: + # Run the command + result = subprocess.run( + f"{BASE_CMD} {args}", + shell=True, + capture_output=False, # Show output in real-time + text=True + ) + + elapsed = time.time() - start_time + end_ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + if result.returncode == 0: + print(f"\n✓ Experiment '{name}' completed successfully") + print(f" Started: {start_ts}") + print(f" Finished: {end_ts}") + print(f" Duration: {elapsed:.1f}s ({elapsed/60:.1f} min)") + return True, elapsed + else: + print(f"\n✗ Experiment '{name}' failed with exit code {result.returncode}") + print(f" Duration: {elapsed:.1f}s") + return False, elapsed + + except KeyboardInterrupt: + print(f"\n\n⚠ Experiment '{name}' interrupted by user") + raise + except Exception as e: + elapsed = time.time() - start_time + print(f"\n✗ Experiment '{name}' failed with exception: {e}") + print(f" Duration: {elapsed:.1f}s") + return False, elapsed + + +def main(): + """Run all experiments sequentially.""" + print("="*80) + print("JOB SCHEDULER - Running experiments sequentially") + print("="*80) + print(f"Total experiments: {len(EXPERIMENTS)}") + print(f"Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + + results = [] + total_start = time.time() + + try: + for i, exp in enumerate(EXPERIMENTS, 1): + success, duration = run_experiment(exp, i, len(EXPERIMENTS)) + results.append({ + "name": exp["name"], + "success": success, + "duration": duration + }) + + # Brief pause between experiments + if i < len(EXPERIMENTS): + print("\nWaiting 5 seconds before next experiment...") + time.sleep(5) + + except KeyboardInterrupt: + print("\n\n⚠ Job scheduler interrupted by user") + + finally: + # Print summary + total_elapsed = time.time() - total_start + print("\n" + "="*80) + print("SUMMARY") + print("="*80) + + successful = sum(1 for r in results if r["success"]) + failed = len(results) - successful + + print(f"\nCompleted: {len(results)}/{len(EXPERIMENTS)} experiments") + print(f"Successful: {successful}") + print(f"Failed: {failed}") + print(f"\nTotal time: {total_elapsed:.1f}s ({total_elapsed/60:.1f} min, {total_elapsed/3600:.2f} hrs)") + + print("\nDetailed results:") + for i, r in enumerate(results, 1): + status = "✓" if r["success"] else "✗" + print(f" {i}. {status} {r['name']:40s} - {r['duration']:.1f}s ({r['duration']/60:.1f} min)") + + if failed > 0: + print("\nFailed experiments:") + for i, r in enumerate(results, 1): + if not r["success"]: + print(f" {i}. {r['name']}") + + sys.exit(0 if failed == 0 else 1) + + +if __name__ == "__main__": + main() diff --git a/examples/TTSwithVerification/start_vllm_multiprocess.sh b/examples/TTSwithVerification/start_vllm_multiprocess.sh new file mode 100755 index 00000000..080b58b7 --- /dev/null +++ b/examples/TTSwithVerification/start_vllm_multiprocess.sh @@ -0,0 +1,98 @@ +#!/bin/bash + +# Start 4 vLLM processes with explicit GPU assignment +# Process 1: GPUs 0-1, Port 8000 +# Process 2: GPUs 2-3, Port 8001 +# Process 3: GPUs 4-5, Port 8002 +# Process 4: GPUs 6-7, Port 8003 + +MODEL="Qwen/QwQ-32B" +GPU_MEMORY=0.4 +TENSOR_PARALLEL=2 + +echo "Killing any existing vLLM processes..." +pkill -9 -f "vllm.entrypoints.openai.api_server" +sleep 2 + +echo "Starting 4 vLLM processes..." + +# Process 1 - GPUs 0,1 +( + export CUDA_VISIBLE_DEVICES=0,1 + python -m vllm.entrypoints.openai.api_server \ + --model $MODEL \ + --port 8000 \ + --tensor-parallel-size $TENSOR_PARALLEL \ + --gpu-memory-utilization $GPU_MEMORY \ + --disable-log-requests \ + > /tmp/vllm_8000.log 2>&1 +) & +PID1=$! +echo "Started Process 1 (GPUs 0-1, Port 8000) - PID: $PID1" + +sleep 5 + +# Process 2 - GPUs 2,3 +( + export CUDA_VISIBLE_DEVICES=2,3 + python -m vllm.entrypoints.openai.api_server \ + --model $MODEL \ + --port 8001 \ + --tensor-parallel-size $TENSOR_PARALLEL \ + --gpu-memory-utilization $GPU_MEMORY \ + --disable-log-requests \ + > /tmp/vllm_8001.log 2>&1 +) & +PID2=$! +echo "Started Process 2 (GPUs 2-3, Port 8001) - PID: $PID2" + +sleep 5 + +# Process 3 - GPUs 4,5 +( + export CUDA_VISIBLE_DEVICES=4,5 + python -m vllm.entrypoints.openai.api_server \ + --model $MODEL \ + --port 8002 \ + --tensor-parallel-size $TENSOR_PARALLEL \ + --gpu-memory-utilization $GPU_MEMORY \ + --disable-log-requests \ + > /tmp/vllm_8002.log 2>&1 +) & +PID3=$! +echo "Started Process 3 (GPUs 4-5, Port 8002) - PID: $PID3" + +sleep 5 + +# Process 4 - GPUs 6,7 +( + export CUDA_VISIBLE_DEVICES=6,7 + python -m vllm.entrypoints.openai.api_server \ + --model $MODEL \ + --port 8003 \ + --tensor-parallel-size $TENSOR_PARALLEL \ + --gpu-memory-utilization $GPU_MEMORY \ + --disable-log-requests \ + > /tmp/vllm_8003.log 2>&1 +) & +PID4=$! +echo "Started Process 4 (GPUs 6-7, Port 8003) - PID: $PID4" + +echo "" +echo "All 4 vLLM processes started successfully." +echo "Process PIDs: $PID1 $PID2 $PID3 $PID4" +echo "" +echo "Log files:" +echo " /tmp/vllm_8000.log - Process 1" +echo " /tmp/vllm_8001.log - Process 2" +echo " /tmp/vllm_8002.log - Process 3" +echo " /tmp/vllm_8003.log - Process 4" +echo "" +echo "To stop all processes, run:" +echo " pkill -9 -f 'vllm.entrypoints.openai.api_server'" +echo "" +echo "Waiting for processes to initialize (this may take 60-120 seconds)..." +echo "" + +# Wait for all processes +wait $PID1 $PID2 $PID3 $PID4 From 36ca8a21bee30c02153bce610d124bdd686fd746 Mon Sep 17 00:00:00 2001 From: ashmitkx <66110457+ashmitkx@users.noreply.github.com> Date: Tue, 3 Mar 2026 12:35:47 +0000 Subject: [PATCH 04/11] add zebralogic to bon+gentest --- .../TTSwithVerification/bestofk_baseline.py | 59 +- interwhen/data/__init__.py | 0 interwhen/data/zebralogic_ir_map.json | 285374 +++++++++++++++ interwhen/utils/zebralogic_helper.py | 284 + 4 files changed, 285716 insertions(+), 1 deletion(-) create mode 100644 interwhen/data/__init__.py create mode 100644 interwhen/data/zebralogic_ir_map.json create mode 100644 interwhen/utils/zebralogic_helper.py diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index 9b52206d..a168ceec 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -16,6 +16,8 @@ from tqdm import tqdm from transformers import AutoTokenizer +from interwhen.utils.zebralogic_helper import SYSTEM_PROMPT_VANILLA, USER_PROMPT_TEMPLATE, get_zebralogic_dataset, extract_last_json, zebra_correctness + from interwhen import stream_completion # ============== MODEL CONFIGURATION ============== @@ -307,6 +309,24 @@ def evaluate_mcq_answer(answer, options, ground_truth): return False, sol, f"Incorrect: expected '{gt_sol}', got '{opt_value}' (option {opt_letter})" return False, sol, f"Solution '{sol}' not found in options or ground truth" +# --------------------- ZebraLogic helpers --------------------- + +def evaluate_zebralogic_answer(answer, example): + """Evaluate a zebralogic answer against ground truth using zebra_correctness.""" + candidate = extract_last_json(answer) + if not candidate: + return False, None, "No valid JSON solution found" + correct, skipped, missing, total = zebra_correctness(example, candidate) + is_correct = correct == total + msg = f"Correct={correct}/{total}, skipped={skipped}, missing={missing}" + return is_correct, candidate, msg + + +def build_zebralogic_prompt(example): + system_prompt = SYSTEM_PROMPT_VANILLA + user_prompt = USER_PROMPT_TEMPLATE.format(problem_text=example['puzzle_clean']) + return system_prompt, user_prompt + def build_full_prompt(task, example, nums=None): if task == "game24": @@ -314,6 +334,8 @@ def build_full_prompt(task, example, nums=None): return f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" if task == "maze": system_prompt, user_prompt = build_maze_prompt(example) + elif task == 'zebralogic': + system_prompt, user_prompt = build_zebralogic_prompt(example) else: system_prompt, user_prompt = build_spatialmap_prompt(example) return ( @@ -330,6 +352,8 @@ def load_dataset_for_task(task): return load_dataset("microsoft/VISION_LANGUAGE", "maze_text_only", split="val") if task == "spatialmap": return load_dataset("microsoft/VISION_LANGUAGE", "spatial_map_text_only", split="val") + if task == "zebralogic": + return get_zebralogic_dataset() raise ValueError(f"Unsupported task: {task}") @@ -450,6 +474,30 @@ def build_game24_critic_prompt(nums, reasoning_output): """ +def build_zebralogic_critic_prompt(task_description, reasoning_output): + """Build critic prompt to evaluate ZebraLogic solution and provide reasoning.""" + return f"""You are an expert logic puzzle verifier. Evaluate the following ZebraLogic solution. + +Task: +{task_description} + +Student's reasoning and answer: +{reasoning_output} + +Verify: +1. Does the solution assign exactly one value per feature per house? +2. Are all constraints/clues satisfied? +3. Is the JSON output well-formed and complete? + +Respond in the following format: +VERDICT: CORRECT or INCORRECT +REASONING: Your detailed explanation + +If CORRECT, briefly explain why. +If INCORRECT, explain what went wrong and suggest corrections. +""" + + def build_mcq_critic_prompt(task, task_description, reasoning_output): """Build critic prompt to evaluate MCQ solution and provide reasoning.""" task_name = "Maze" if task == "maze" else "Spatial Reasoning" @@ -517,6 +565,9 @@ async def _run_parallel(): output_text = row["output"] if task == "game24": critic_prompt = build_game24_critic_prompt(nums, output_text) + elif task == "zebralogic": + _, task_desc = build_zebralogic_prompt(example) + critic_prompt = build_zebralogic_critic_prompt(task_desc, output_text) else: if task == "maze": _, task_desc = build_maze_prompt(example) @@ -648,7 +699,7 @@ def run_k_samples_with_critic( if __name__ == "__main__": parser = argparse.ArgumentParser(description="Best-of-K baseline (standard CoT) for TTSwithVerification datasets") - parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap"], + parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap", "zebralogic"], help="Task to run") parser.add_argument("--k", type=int, default=4, help="Number of samples per example") parser.add_argument("--num_examples", "-n", type=int, default=None, @@ -730,6 +781,12 @@ def run_k_samples_with_critic( prompt = build_full_prompt(args.task, example, nums=nums) eval_fn = lambda output: evaluate_game24_answer(output, nums) options = None + + elif args.task == "zebralogic": + prompt = build_full_prompt(args.task, example) + eval_fn = lambda output, ex=example: evaluate_zebralogic_answer(output, ex) + options = None + else: prompt = build_full_prompt(args.task, example) gt = str(example.get("ground_truth", "")).strip() diff --git a/interwhen/data/__init__.py b/interwhen/data/__init__.py new file mode 100644 index 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"type": "same_house", + "A": [ + "color", + "blue" + ], + "B": [ + "name", + "peter" + ] + } + ] + ], + [ + [ + { + "type": "same_house", + "A": [ + "name", + "bob" + ], + "B": [ + "flower", + "tulips" + ] + } + ] + ], + [ + [ + { + "type": "place", + "entity": [ + "name", + "alice" + ], + "pos": 1 + } + ] + ], + [ + [ + { + "type": "pos_relation", + "A": [ + "favoritesport", + "baseball" + ], + "B": [ + "cigar", + "blue master" + ], + "direction": "left", + "dist": "1" + } + ] + ], + [ + [ + { + "type": "pos_relation", + "A": [ + "phonemodel", + "google pixel 6" + ], + "B": [ + "cigar", + "blends" + ], + "direction": "right", + "dist": "?" + } + ] + ], + [ + [ + { + "type": "same_house", + "A": [ + "name", + "carol" + ], + "B": [ + "favoritesport", + "soccer" + ] + } + ] + ], + [ + [ + { + "type": "pos_relation", + "A": [ + "flower", + "carnations" + ], + "B": [ + "cigar", + "blends" + ], + "direction": "left", + "dist": "1" + } + ] + ], + [ + [ + { + "type": "same_house", + "A": [ + "name", + "eric" + ], + "B": [ + "cigar", + "blends" + ] + } + ] + ], + [ + [ + { + "type": "same_house", + "A": [ + "favoritesport", + "volleyball" + ], + "B": [ + "phonemodel", + "iphone 13" + ] + } + ] + ] + ], + "solution_irs": [ + [ + { + "type": "place", + "entity": [ + "name", + "alice" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "samsung galaxy s21" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "cigar", + "yellow monster" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "flower", + "iris" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "color", + "red" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "baseball" + ], + "pos": 1 + }, + { + "type": "place", + "entity": [ + "name", + "carol" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "oneplus 9" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "cigar", + "blue master" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "flower", + "carnations" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "color", + "green" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "soccer" + ], + "pos": 2 + }, + { + "type": "place", + "entity": [ + "name", + "eric" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "xiaomi mi 11" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "cigar", + "blends" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "flower", + "roses" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "color", + "yellow" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "tennis" + ], + "pos": 3 + }, + { + "type": "place", + "entity": [ + "name", + "peter" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "iphone 13" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "cigar", + "dunhill" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "flower", + "lilies" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "color", + "blue" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "volleyball" + ], + "pos": 4 + }, + { + "type": "place", + "entity": [ + "name", + "arnold" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "huawei p50" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "cigar", + "prince" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "flower", + "daffodils" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "color", + "purple" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "basketball" + ], + "pos": 5 + }, + { + "type": "place", + "entity": [ + "name", + "bob" + ], + "pos": 6 + }, + { + "type": "place", + "entity": [ + "phonemodel", + "google pixel 6" + ], + "pos": 6 + }, + { + "type": "place", + "entity": [ + "cigar", + "pall mall" + ], + "pos": 6 + }, + { + "type": "place", + "entity": [ + "flower", + "tulips" + ], + "pos": 6 + }, + { + "type": "place", + "entity": [ + "color", + "white" + ], + "pos": 6 + }, + { + "type": "place", + "entity": [ + "favoritesport", + "swimming" + ], + "pos": 6 + } + ] + ] + } +} \ No newline at end of file diff --git a/interwhen/utils/zebralogic_helper.py b/interwhen/utils/zebralogic_helper.py new file mode 100644 index 00000000..cde8a55b --- /dev/null +++ b/interwhen/utils/zebralogic_helper.py @@ -0,0 +1,284 @@ +""" +Prompt and dataset utilities for WildEval/ZebraLogic. +""" + +import re +import json +from collections import defaultdict +from typing import Dict, List, Tuple, Optional, Set +from importlib import resources +import datasets + + +# ============== Prompt Templates ============== + +SYSTEM_PROMPT_VANILLA = """\ +# Problem Description + +You are solving a house grid problem. You are given: +1. Features and Domains + - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. + - A set of features (e.g., color, name, pet, book genre). + - Each feature has a finite domain of possible values. +2. Constraint: + - Each house has exactly one value per feature. + - No two houses share the same value for the same feature. +3. Clues / Constraints descrbing: + - Houses and their positions + - Feature values + - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") + +Solve to your best ability the arrangement of features across the houses. + +# Final Answer Format + +```json +{ + "House 1": { "feature1": "value1", "feature2": "value2", ... }, + "House 2": { "feature1": "value1", "feature2": "value2", ... }, + ... +} +``` + +Make sure to use the exact feature/value names as given in the problem description. +Make sure the JSON is valid and parsable.""" + +SYSTEM_PROMPT_STATEPOLL = """\ +# Problem Description + +You are solving a house grid problem. You are given: +1. Features and Domains + - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. + - A set of features (e.g., color, name, pet, book genre). + - Each feature has a finite domain of possible values. +2. Constraint: + - Each house has exactly one value per feature. + - No two houses share the same value for the same feature. +3. Clues / Constraints describing: + - Houses and their positions + - Feature values + - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") + +# Rules for Solving + +1. Reason about the problem in text. +2. After every inference, no matter how minor or tentative, immediately report the updated partial assignments. + - Always output partial assignments frequently as the reasoning progresses, not only at major steps or when confident. + - If an inference adds, removes, or narrows even a single possibility, report it. + +# House/Feature Partial Assignment Reporting Format + +```json +{ + "House N": { "feature1": "value1", "feature2": "value2", ... }, + ... +} +``` + +Omit any unassigned features. +Make sure to use the exact feature/value names as given in the problem description. +Make sure the JSON is valid and parsable.""" + +SYSTEM_PROMPT_STATEFEEDBACK = """\ +# Problem Description + +You are solving a house grid problem. You are given: +1. Features and Domains + - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. + - A set of features (e.g., color, name, pet, book genre). + - Each feature has a finite domain of possible values. +2. Constraint: + - Each house has exactly one value per feature. + - No two houses share the same value for the same feature. +3. Clues / Constraints describing: + - Houses and their positions + - Feature values + - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") + +# Rules for Solving + +1. Reason about the problem in text. +2. You may receive feedback from the user if anything is wrong. Use any feedback to guide your reasoning until a complete solution is reached. +3. Do not stop responding until you've assigned each and every variable. + +# Final Answer Reporting Format + +```json +{ + "House 1": { "feature1": "value1", "feature2": "value2", ... }, + "House 2": { "feature1": "value1", "feature2": "value2", ... }, + ... +} +``` + +Make sure to use the exact feature/value names as given in the problem description. +Make sure the JSON is valid and parsable.""" + +USER_PROMPT_TEMPLATE = "{problem_text}" + +# ============== Dataset Loading ============== + +def clean_problem_text(problem_text: str, features: dict) -> str: + """Clean up problem text giving explicit feature domains.""" + desc, clues = problem_text.split('## clues:') + line0 = desc.splitlines()[0] + + feature_text = '' + for feature, values in features.items(): + values_str = ', '.join(f"'{v}'" for v in values) + feature_text += f"- '{feature}': {values_str}\n" + + return f"{line0}\n{feature_text}\n## clues:{clues}".strip() + + +def process_zebralogic_problem(problem: dict, ir_map: dict) -> dict: + """Process a raw ZebraLogic problem into the format needed by ZebraLogicProblem. + + Args: + problem: Raw problem dict from the HuggingFace dataset. + ir_map: Dict mapping problem IDs to their IR representations. + + Returns: + Processed problem dict with keys: n_houses, n_features, features, clues, + clue_irs, solution_irs, solution, puzzle_clean, etc. + """ + def apply_text_replacements(problem): + replacements = [ + ['january', 'jan'], ['february', 'feb'], ['march', 'mar'], + ['august', 'aug'], ['september', 'sept'], ['f-150', 'f150'], + ['animal', 'pet'], ['loves the spaghetti eater', 'loves spaghetti'], + ['very short', 'veryshort'], ['super short', 'supershort'], + ['very tall', 'verytall'], ['super tall', 'supertall'], + ] + problem_str = json.dumps(problem) + for old, new in replacements: + problem_str = problem_str.replace(old, new) + return json.loads(problem_str) + + pid = problem['id'] + size = problem["size"] + n_houses, n_features = map(int, size.split("*")) + + problem_text = problem["puzzle"].lower() + clues_raw = re.split(r"##\s*clues\s*:", problem_text, flags=re.IGNORECASE)[1].strip().split("\n") + clues = [] + for clue in clues_raw: + clue_text_index, clue_text = clue.strip().split(". ", 1) + clues.append({ + "text_index": int(clue_text_index.strip()), + "text": clue_text.strip() + }) + + solution = problem["solution"] + solution['header'] = [h.lower() for h in solution['header']] + solution['rows'] = [[v.lower() for v in row] for row in solution['rows']] + + features = defaultdict(list) + for row in solution['rows']: + for fname, value in zip(solution['header'], row): + if fname.lower() == 'house': + continue + features[fname].append(value) + features = dict(features) + for fname in features: + features[fname] = sorted(list(set(features[fname]))) + assert len(features[fname]) == n_houses + assert len(features) == n_features + + processed_solution = {f'House {i+1}': {} for i in range(n_houses)} + for house_i, row in enumerate(solution['rows']): + for fname, value in zip(solution['header'][1:], row[1:]): + processed_solution[f'House {house_i+1}'][fname.lower()] = value.lower() + problem['solution'] = processed_solution + + problem['puzzle'] = problem_text + problem["clues"] = clues + problem["features"] = features + problem["n_houses"] = n_houses + problem["n_features"] = n_features + problem["clue_irs"] = ir_map[pid]["clue_irs"] + problem["solution_irs"] = ir_map[pid]["solution_irs"] + problem['puzzle_clean'] = clean_problem_text(problem['puzzle'], problem['features']) + + return apply_text_replacements(problem) + +def get_zebralogic_dataset() -> list: + """Load and process the ZebraLogic dataset from HuggingFace. + + Loads WildEval/ZebraLogic grid_mode test split and processes each problem + with the IR map. + + The IR map file must be at interwhen/data/zebralogic_ir_map.json + + Returns: + List of processed problem dicts. + """ + + dataset = datasets.load_dataset("WildEval/ZebraLogic", "grid_mode", split="test").to_list() + + pkg = "interwhen.data" + with resources.files(pkg).joinpath("zebralogic_ir_map.json").open("r") as f: + ir_map = json.load(f) + + # Known problematic problem IDs (unsolvable or malformed) + bad_ids = { + 'lgp-test-6x5-2', 'lgp-test-6x6-5', 'lgp-test-2x5-1', 'lgp-test-4x5-5', + 'lgp-test-2x4-6', 'lgp-test-2x6-11', 'lgp-test-4x6-35', 'lgp-test-3x5-15', + 'lgp-test-5x5-37', 'lgp-test-5x5-17', 'lgp-test-4x5-15', 'lgp-test-6x6-2', + 'lgp-test-5x6-4', 'lgp-test-5x6-2', 'lgp-test-5x5-1' + } + dataset = [p for p in dataset if p['id'] not in bad_ids] + dataset = [process_zebralogic_problem(p, ir_map) for p in dataset] + return dataset + +def extract_last_json(text): + """Extract the last JSON object from the model's output text.""" + json_text = text.split('')[-1].strip() + + # try with md tags + matches = re.findall(r'```json(.*?)```', json_text, re.DOTALL) + if matches and len(matches) > 0: + json_match = matches[-1] + return json.loads(json_match.strip()) + + # try without assuming md tags + matches = re.findall(r'\{.*\}\s*?}', json_text, re.DOTALL) + if matches and len(matches) > 0: + json_match = matches[0] + return json.loads(json_match.strip()) + + return None + +def zebra_correctness(problem: dict, candidate_solution: dict) -> Tuple[int, int, int, int]: + """Check candidate solution against ground truth. + + Args: + problem: Processed problem dict with 'solution', 'n_houses', 'n_features', 'features'. + candidate_solution: Dict mapping "House N" -> {feature: value}. + + Returns: + (correct, skipped, missing, total) counts where: + correct: Number of matching assignments + skipped: Assignments for invalid houses/features + missing: Features not in candidate solution + total: Total expected assignments (n_houses * n_features) + """ + c, s = 0, 0 + t_soln = 0 + t = problem['n_houses'] * problem['n_features'] + solution = problem['solution'] + + for house in candidate_solution: + if house not in solution: + s += len(problem['features']) + continue + for fname in candidate_solution[house]: + if fname not in solution[house]: + s += 1 + continue + t_soln += 1 + if candidate_solution[house][fname] == solution[house][fname]: + c += 1 + + m = t - t_soln + return c, s, m, t From f424846ebe07f9fad757719d17e088e2dff55e45 Mon Sep 17 00:00:00 2001 From: ashmitkx <66110457+ashmitkx@users.noreply.github.com> Date: Tue, 3 Mar 2026 12:38:35 +0000 Subject: [PATCH 05/11] add paralization accross dataset --- .../TTSwithVerification/bestofk_baseline.py | 50 +++++++++++++------ 1 file changed, 34 insertions(+), 16 deletions(-) diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index a168ceec..ebfb6d39 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -1,10 +1,12 @@ import argparse import asyncio +from datetime import datetime import json import logging import os import re import sys +from multiprocessing.pool import ThreadPool from contextlib import contextmanager from dataclasses import dataclass from typing import Dict, List, Optional @@ -29,6 +31,9 @@ logger = logging.getLogger(__name__) +# Save the real stderr so tqdm always works even if suppress_output is active +_real_stderr = sys.stderr + @contextmanager def suppress_output(): @@ -720,6 +725,7 @@ def run_k_samples_with_critic( parser.add_argument("--seed", type=int, default=42, help="Base random seed") parser.add_argument("--max_tokens", type=int, default=32768, help="Max tokens for generation") parser.add_argument("--temperature", type=float, default=0.6, help="Sampling temperature") + parser.add_argument("--processes", "-p", type=int, default=1, help="Number of examples to process in parallel (default: 1, sequential)") parser.add_argument("--debug", "-d", action="store_true", help="Enable debug logging") args = parser.parse_args() @@ -774,7 +780,8 @@ def run_k_samples_with_critic( total_tokens_all_samples = 0 results = [] - for idx in tqdm(indices, desc="Processing examples", unit="example"): + def process_example(idx): + """Process a single example: generate k samples, evaluate, return result dict.""" example = dataset[int(idx)] if args.task == "game24": nums = example["numbers"] @@ -837,17 +844,7 @@ def run_k_samples_with_critic( save_outputs(idx, sample_results, best_idx, output_dirs["reasoning"]) - total_examples += 1 - if any_correct: - total_correct += 1 - total_correct_samples += correct_samples - total_samples += len(sample_results) - critic_correct_samples += critic_correct_samples_example - critic_total_samples += len(sample_results) - total_tokens += best_result.tokens - total_tokens_all_samples += sum(r.tokens for r in sample_results) - - results.append({ + return { "idx": int(idx), "best_idx": best_idx, "any_correct": any_correct, @@ -862,10 +859,31 @@ def run_k_samples_with_critic( "all_critic_correct": [r.critic_correct for r in sample_results], "all_critic_feedback": [r.critic_feedback for r in sample_results], "options": options, - }) - - #logger.info(f"Best sample: {best_idx} | Correct in K: {any_correct}") - #logger.info(f"Best message: {best_result.message}") + "_any_correct": any_correct, + "_correct_samples": correct_samples, + "_critic_correct_samples": critic_correct_samples_example, + "_n_samples": len(sample_results), + "_best_tokens": best_result.tokens, + "_all_tokens_sum": sum(r.tokens for r in sample_results), + } + + with ThreadPool(processes=args.processes) as pool: + for result in tqdm(pool.imap_unordered(process_example, indices), total=len(indices), desc="Processing examples", unit="example", file=_real_stderr): + total_examples += 1 + if result["_any_correct"]: + total_correct += 1 + total_correct_samples += result["_correct_samples"] + total_samples += result["_n_samples"] + critic_correct_samples += result["_critic_correct_samples"] + critic_total_samples += result["_n_samples"] + total_tokens += result["_best_tokens"] + total_tokens_all_samples += result["_all_tokens_sum"] + + # Remove internal keys before appending + for k in list(result.keys()): + if k.startswith("_"): + del result[k] + results.append(result) accuracy = total_correct / total_examples if total_examples else 0 avg_best_tokens = total_tokens / total_examples if total_examples else 0 From ceaa92bbfe0fdca2874c7e33e9abceac8bd198d1 Mon Sep 17 00:00:00 2001 From: Vijval9 <114911861+Vijval9@users.noreply.github.com> Date: Tue, 3 Mar 2026 23:20:34 +0530 Subject: [PATCH 06/11] Added verina (code) support to bestofk_baseline.py Added Verina support and helper functions for evaluation. --- .../TTSwithVerification/bestofk_baseline.py | 94 ++++++++++++++++++- 1 file changed, 91 insertions(+), 3 deletions(-) diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index ebfb6d39..c17f4732 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -6,10 +6,13 @@ import os import re import sys +import shutil +import subprocess from multiprocessing.pool import ThreadPool from contextlib import contextmanager from dataclasses import dataclass -from typing import Dict, List, Optional +from pathlib import Path +from typing import Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -21,13 +24,18 @@ from interwhen.utils.zebralogic_helper import SYSTEM_PROMPT_VANILLA, USER_PROMPT_TEMPLATE, get_zebralogic_dataset, extract_last_json, zebra_correctness from interwhen import stream_completion +from verina_utils import * # ============== MODEL CONFIGURATION ============== MAIN_MODEL = "Qwen/QwQ-32B" # Multi-process vLLM configuration VLLM_PORTS = [8000, 8001, 8002, 8003] # 4 instances with tensor-parallel-size 2 each REQUEST_COUNTER = {"main": 0, "critic": 0} # Track request count for round-robin load balancing - +# Verina paths +_SCRIPT_DIR = Path(__file__).parent.resolve() +VERINA_ROOT = (_SCRIPT_DIR / "../../../verina").resolve() +VERINA_DATASETS_PATH = VERINA_ROOT / "datasets" / "verina" +LEAN_PLAYGROUND_DIR = VERINA_ROOT / "lean-playground" logger = logging.getLogger(__name__) @@ -332,6 +340,27 @@ def build_zebralogic_prompt(example): user_prompt = USER_PROMPT_TEMPLATE.format(problem_text=example['puzzle_clean']) return system_prompt, user_prompt +# verina helpers +def evaluate_verina_answer(output: str, data: BenchmarkData, task_idx: int) -> Tuple[bool, str, str]: + """Evaluate Verina code generation output - wrapper for best-of-k interface""" + generated_code = extract_code_from_response(output) + + if not generated_code.strip(): + return False, "", "No code extracted from response" + + compiles, all_tests_pass, compile_output, test_results = evaluate_generated_code(data, generated_code, task_idx) + + num_tests = len(data.tests) if data.tests else 0 + num_passed = sum(1 for v in test_results.values() if v == "pass") + + if compiles and all_tests_pass: + return True, generated_code, f"Code compiles and all {num_tests} tests pass" + elif compiles: + return False, generated_code, f"Compilation succeeded but {num_tests - num_passed}/{num_tests} tests failed" + else: + error_preview = compile_output[:300] if compile_output else "Unknown error" + return False, generated_code, f"Compilation failed: {error_preview}" + def build_full_prompt(task, example, nums=None): if task == "game24": @@ -341,6 +370,8 @@ def build_full_prompt(task, example, nums=None): system_prompt, user_prompt = build_maze_prompt(example) elif task == 'zebralogic': system_prompt, user_prompt = build_zebralogic_prompt(example) + elif task == "verina": + return build_verina_prompt(example) else: system_prompt, user_prompt = build_spatialmap_prompt(example) return ( @@ -359,6 +390,8 @@ def load_dataset_for_task(task): return load_dataset("microsoft/VISION_LANGUAGE", "spatial_map_text_only", split="val") if task == "zebralogic": return get_zebralogic_dataset() + if task == "verina": + return load_verina_dataset() raise ValueError(f"Unsupported task: {task}") @@ -524,6 +557,52 @@ def build_mcq_critic_prompt(task, task_description, reasoning_output): If INCORRECT, explain what went wrong and suggest the correct approach. """ +def build_verina_critic_prompt(data: BenchmarkData, reasoning_output: str) -> str: + """Build critic prompt to evaluate Verina Lean code generation and provide reasoning.""" + signature = data.signature + func_name = signature.get("name", "solution") + return_type = signature.get("return_type", "Bool") + param_list = render_param_list(signature) + + precond = data.lean_data.get("precond", "True").strip() + postcond = data.lean_data.get("postcond", "").strip() + + return f"""You are an expert Lean 4 code verifier. Evaluate the following code generation attempt. + +## Task Description +{data.description} + +## Function Signature +```lean4 +def {func_name} {param_list} (h_precond : {func_name}_precond ...) : {return_type} +``` + +## Precondition +```lean4 +{precond} +``` + +## Postcondition +```lean4 +{postcond} +``` + +## Student's Reasoning and Generated Code +{reasoning_output} + +Verify: +1. Is the generated code syntactically valid Lean 4? +2. Does it match the expected function signature and return type ({return_type})? +3. Does the logic appear to satisfy the postcondition given the precondition? +4. Are there any obvious bugs, infinite loops, or incorrect base cases? + +Respond in the following format: +VERDICT: CORRECT or INCORRECT +REASONING: Your detailed explanation + +If CORRECT, briefly explain why +If INCORRECT, explain what went wrong and suggest how to fix it. +""" def batch_evaluate_with_critic(outputs_df, task, example, critic_llm_server, tokenizer, nums=None, quiet=True): """Batch evaluate outputs using vLLM API across multiple instances. Outputs_df should have columns: 'output', 'seed_idx'""" @@ -573,6 +652,8 @@ async def _run_parallel(): elif task == "zebralogic": _, task_desc = build_zebralogic_prompt(example) critic_prompt = build_zebralogic_critic_prompt(task_desc, output_text) + elif task == "verina": + critic_prompt = build_verina_critic_prompt(example, output_text) else: if task == "maze": _, task_desc = build_maze_prompt(example) @@ -704,7 +785,7 @@ def run_k_samples_with_critic( if __name__ == "__main__": parser = argparse.ArgumentParser(description="Best-of-K baseline (standard CoT) for TTSwithVerification datasets") - parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap", "zebralogic"], + parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap", "zebralogic","verina"], help="Task to run") parser.add_argument("--k", type=int, default=4, help="Number of samples per example") parser.add_argument("--num_examples", "-n", type=int, default=None, @@ -793,6 +874,13 @@ def process_example(idx): prompt = build_full_prompt(args.task, example) eval_fn = lambda output, ex=example: evaluate_zebralogic_answer(output, ex) options = None + elif args.task == "verina": + # For verina, example is a BenchmarkData object + prompt = build_full_prompt(args.task, example) + current_idx = int(idx) + current_data = example + eval_fn = lambda output, data=current_data, task_idx=current_idx: evaluate_verina_answer(output, data, task_idx) + options = None else: prompt = build_full_prompt(args.task, example) From a0fbce4b7a4f6174b342313042b1a91ce73b380d Mon Sep 17 00:00:00 2001 From: Vijval9 <114911861+Vijval9@users.noreply.github.com> Date: Tue, 3 Mar 2026 23:21:59 +0530 Subject: [PATCH 07/11] Added verina (code) utils Add utility functions for Verina benchmark tasks, including loading data, parsing Lean files, and rendering unit tests. --- examples/TTSwithVerification/verina_utils.py | 649 +++++++++++++++++++ 1 file changed, 649 insertions(+) create mode 100644 examples/TTSwithVerification/verina_utils.py diff --git a/examples/TTSwithVerification/verina_utils.py b/examples/TTSwithVerification/verina_utils.py new file mode 100644 index 00000000..84db2338 --- /dev/null +++ b/examples/TTSwithVerification/verina_utils.py @@ -0,0 +1,649 @@ +import argparse +import asyncio +import json +import logging +import os +import re +import sys +import shutil +import subprocess +from contextlib import contextmanager +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Tuple +import numpy as np +import pandas as pd +import aiohttp +from datasets import load_dataset +from tqdm import tqdm +from transformers import AutoTokenizer + +__all__ = [ + # Path constants + "VERINA_ROOT", + "VERINA_DATASETS_PATH", + "LEAN_PLAYGROUND_DIR", + # Classes + "BenchmarkData", + # Constants + "CODE_TEST_MSG_MARKER", + "DECIDABLE_ERR_MSG", + # Functions + "parse_benchmark_lean_data", + "load_benchmark_data_from_task_dir", + "load_verina_dataset", + "render_param_list", + "build_verina_prompt", + "strip_function_definition", + "extract_code_from_response", + "create_lean_file", + "check_lean_compile", + "render_unit_test_value", + "render_code_unit_test", + "build_test_lean_file", + "parse_unit_test_results", + "evaluate_generated_code", +] + +_SCRIPT_DIR = Path(__file__).parent.resolve() +VERINA_ROOT = (_SCRIPT_DIR / "../../../verina").resolve() +VERINA_DATASETS_PATH = VERINA_ROOT / "datasets" / "verina" +LEAN_PLAYGROUND_DIR = VERINA_ROOT / "lean-playground" + + +class BenchmarkData: + """Verina benchmark data structure""" + def __init__(self, data_id: str, description: str, signature: dict, + lean_data: dict, spec_desc: dict, tests: list, metadata: dict): + self.data_id = data_id + self.description = description + self.signature = signature # {"name": str, "parameters": list, "return_type": str} + self.lean_data = lean_data # contains task_imports, task_aux, code, precond, postcond, proof, etc. + self.spec_desc = spec_desc # {"precond_desc": str, "postcond_desc": str} + self.tests = tests + self.metadata = metadata + + +def parse_benchmark_lean_data(raw_lean_data: str) -> dict: + """Parse a .lean file with !benchmark markers into sections""" + lines = raw_lean_data.strip().splitlines() + + lean_data = { + "task_imports": "", + "solution_imports": "", + "task_aux": "", + "solution_aux": "", + "code_aux": "", + "precond_aux": "", + "postcond_aux": "", + "proof_aux": "", + "code": "", + "precond": "True", + "postcond": "", + "proof": "sorry", + } + + current_section = None + current_content = [] + current_args = {} + + for line in lines: + if "-- !benchmark" in line: + marker_part = line.split("-- !benchmark", 1)[1].strip() + + if marker_part.startswith("@start"): + # Save previous section if any + if current_section is not None: + content = "\n".join(current_content).strip() + if current_section == "import": + import_type = current_args.get("type", "task") + if import_type == "task": + lean_data["task_imports"] += content + "\n" + elif import_type == "solution": + lean_data["solution_imports"] += content + "\n" + elif current_section in lean_data: + lean_data[current_section] = content + + # Start new section + parts = marker_part.split("@start", 1)[1].strip().split(None, 1) + current_section = parts[0].strip() + current_args = {} + current_content = [] + + if len(parts) > 1: + for arg in parts[1].strip().split(): + if "=" in arg: + key, value = arg.split("=", 1) + current_args[key] = value + + elif marker_part.startswith("@end"): + if current_section is not None: + content = "\n".join(current_content).strip() + if current_section == "import": + import_type = current_args.get("type", "task") + if import_type == "task": + lean_data["task_imports"] += content + "\n" + elif import_type == "solution": + lean_data["solution_imports"] += content + "\n" + elif current_section in lean_data: + lean_data[current_section] = content + current_section = None + current_content = [] + current_args = {} + else: + if current_section is not None: + current_content.append(line) + + return lean_data + + +def load_benchmark_data_from_task_dir(task_dir: Path) -> Optional[BenchmarkData]: + """Load a single benchmark task from its directory""" + task_path = task_dir / "task.json" + if not task_path.exists(): + return None + + try: + with open(task_path, "r") as f: + task_data = json.load(f) + + task_id = task_data.get("id") + if not task_id: + return None + + # Read description + desc_path = task_dir / task_data.get("description_file", "description.txt") + description = desc_path.read_text().strip() if desc_path.exists() else "" + + # Read signature + signature = task_data.get("signature", {}) + + # Read lean file + lean_path = task_dir / task_data.get("lean_file", "task.lean") + if lean_path.exists(): + lean_data = parse_benchmark_lean_data(lean_path.read_text()) + else: + lean_data = {} + + # Read spec description + spec_desc = { + "precond_desc": task_data.get("specification", {}).get("preconditions", ""), + "postcond_desc": task_data.get("specification", {}).get("postconditions", ""), + } + + # Read tests + test_path = task_dir / task_data.get("test_file", "test.json") + tests = [] + if test_path.exists(): + with open(test_path, "r") as f: + tests = json.load(f) + + metadata = task_data.get("metadata", {}) + + return BenchmarkData( + data_id=task_id, + description=description, + signature=signature, + lean_data=lean_data, + spec_desc=spec_desc, + tests=tests, + metadata=metadata, + ) + except Exception as e: + return None + + +def load_verina_dataset() -> List[BenchmarkData]: + """Load all verina benchmark tasks from the datasets directory""" + results = [] + + # Get all task directories sorted by ID + task_dirs = sorted( + [d for d in VERINA_DATASETS_PATH.glob("verina_*") if d.is_dir()], + key=lambda x: (x.name.split("_")[1], int(x.name.split("_")[-1])), + ) + + for task_dir in task_dirs: + data = load_benchmark_data_from_task_dir(task_dir) + if data: + results.append(data) + + return results + + +def render_param_list(signature: dict) -> str: + """Render the parameter list for a function signature""" + params = signature.get("parameters", []) + rendered = "" + for param in params: + rendered += f"({param['param_name']} : {param['param_type']}) " + return rendered.strip() + + +def build_verina_prompt(data: BenchmarkData) -> str: + """Build prompt for Verina Lean code generation (build_code_gen_prompt + build_full_prompt)""" + system_prompt = "You are an expert Lean 4 programmer. Generate valid Lean 4 code for the function body. Wrap your final code in [CODE] [/CODE] tags strictly." + + signature = data.signature + func_name = signature.get("name", "solution") + return_type = signature.get("return_type", "Bool") + param_list = render_param_list(signature) + params = signature.get("parameters", []) + + precond_name = f"{func_name}_precond" + param_names_str = ' '.join([f"({p['param_name']})" for p in params]) + + # Get auxiliary definitions (only if they exist) + solution_aux = data.lean_data.get("solution_aux", "").strip() + task_aux = data.lean_data.get("task_aux", "").strip() + code_aux = data.lean_data.get("code_aux", "").strip() + precond = data.lean_data.get("precond", "True").strip() + postcond = data.lean_data.get("postcond", "").strip() + + # Build helper section only if there are helpers + helper_section = "" + all_aux = "\n".join(filter(None, [solution_aux, task_aux, code_aux])) + if all_aux: + helper_section = f""" +## Helper Definitions +```lean4 +{all_aux} +``` +""" + + user_prompt = f"""## Task +{data.description} + +## Function Signature +```lean4 +def {func_name} {param_list} (h_precond : {precond_name} {param_names_str}) : {return_type} := + -- YOUR CODE HERE (just output this part inside [CODE] [/CODE] tags) +``` + +## Precondition +```lean4 +def {precond_name} {param_list} : Prop := {precond} +``` + +## Postcondition +```lean4 +def {func_name}_postcond {param_list} (result: {return_type}) : Prop := {postcond} +``` +{helper_section} +Provide ONLY the function body expression wrapped in [CODE]...[/CODE] tags.""" + + return ( + f"<|im_start|>system\n{system_prompt}<|im_end|>\n" + f"<|im_start|>user\n{user_prompt}<|im_end|>\n" + f"<|im_start|>assistant\n" + ) + + +def strip_function_definition(code: str) -> str: + """ + Strip function definition prefix if the model accidentally included it. + + The prompt asks for just the function body, but sometimes the model outputs: + def FunctionName (params) (h_precond : ...) : ReturnType := + actual_body + + We need to extract just 'actual_body' and dedent it properly. + """ + import textwrap + + code = code.strip() + + # Pattern to match Lean function definition: + # def (h_precond : ) : := + # The function body follows after := + func_def_pattern = r'^def\s+\w+\s+.*?:=[ \t]*\n?' + + match = re.match(func_def_pattern, code, re.DOTALL) + if match: + # Extract everything after the := + body = code[match.end():] + # Dedent to remove common leading whitespace from all lines + body = textwrap.dedent(body).strip() + if body: + return body + + return code + + +def extract_code_from_response(response: str) -> str: + """Extract code from the LAST [CODE]...[/CODE] tags or lean code blocks. + + Handles cases where: + 1. Response has ... reasoning block + 2. [CODE] tag exists but [/CODE] may be missing (truncated response) + 3. Code is in markdown lean blocks + 4. Model outputs [CORE] or other variants instead of [CODE] + 5. Model uses mismatched tags like [CORE]...[/CODE] + 6. Model includes full function definition instead of just the body + """ + # Step 1: Remove ... block entirely (case insensitive) + # This prevents extracting reasoning text as code + cleaned = re.sub(r'.*?', '', response, flags=re.DOTALL | re.IGNORECASE) + + # If exists but doesn't match (partial), take everything after + if not cleaned.strip() or cleaned.strip() == response.strip(): + think_end = response.lower().rfind("") + if think_end != -1: + cleaned = response[think_end + len(""):] + + extracted_code = None + + # Step 2: Find the LAST closing tag [/CODE] or [/CORE] and work backwards to find opening tag + # This handles mismatched tags like [CORE]...[/CODE] + closing_pattern = r'\[/(?:CODE|CORE)\]' + closing_matches = list(re.finditer(closing_pattern, cleaned, re.IGNORECASE)) + + if closing_matches: + # Get position of the last closing tag + last_close = closing_matches[-1] + close_pos = last_close.start() + + # Search backwards for the last opening tag before this closing tag + text_before_close = cleaned[:close_pos] + opening_pattern = r'\[(?:CODE|CORE|CORRECTED CODE)\]' + opening_matches = list(re.finditer(opening_pattern, text_before_close, re.IGNORECASE)) + + if opening_matches: + last_open = opening_matches[-1] + extracted_code = cleaned[last_open.end():close_pos].strip() + + # Step 3: Try [CODE] without closing tag (truncated response) - find the LAST one + if extracted_code is None: + code_start_matches = list(re.finditer(r'\[(?:CODE|CORE|CORRECTED CODE)\]\s*', cleaned, re.DOTALL | re.IGNORECASE)) + if code_start_matches: + # Get the last [CODE] tag position and extract everything after it + last_match = code_start_matches[-1] + code = cleaned[last_match.end():].strip() + # Remove any trailing incomplete text that looks like reasoning + # Stop at any line that looks like it's not code (e.g., starts with "Wait", "So", etc.) + lines = code.split('\n') + code_lines = [] + for line in lines: + stripped = line.strip() + # Stop if we hit obvious non-code reasoning text + if stripped and re.match(r'^(Wait|So |But |Now|Note|The |This |However|Therefore|Thus|In |Since)', stripped): + break + code_lines.append(line) + if code_lines: + extracted_code = '\n'.join(code_lines).strip() + + # Step 4: Try markdown lean code blocks (find the LAST one) + if extracted_code is None: + lean_matches = list(re.finditer(r'```lean4?\s*\n(.*?)```', cleaned, re.DOTALL | re.IGNORECASE)) + if lean_matches: + extracted_code = lean_matches[-1].group(1).strip() + + # Step 5: Try lean block without closing (truncated) - find the LAST one + if extracted_code is None: + lean_start_matches = list(re.finditer(r'```lean4?\s*\n', cleaned, re.DOTALL | re.IGNORECASE)) + if lean_start_matches: + last_match = lean_start_matches[-1] + code = cleaned[last_match.end():].strip() + # Remove trailing ``` if present + extracted_code = re.sub(r'```\s*$', '', code).strip() + + # Step 6: Last resort, return cleaned content if it looks like code + if extracted_code is None: + cleaned = cleaned.strip() + if cleaned: + # Filter out lines that look like reasoning + lines = cleaned.split('\n') + code_lines = [] + for line in lines: + stripped = line.strip() + if stripped and not re.match(r'^(Wait|So |But |Now|Note|The |This |However|Therefore|Thus|In |Since|I |We |You )', stripped): + code_lines.append(line) + if code_lines: + extracted_code = '\n'.join(code_lines).strip() + + # Step 7: Strip function definition prefix if model included it + # The prompt asks for just the body, but sometimes model outputs full "def ... :=" + if extracted_code: + extracted_code = strip_function_definition(extracted_code) + return extracted_code + + return "" + + +def create_lean_file(file_name: str, content: str) -> Path: + """Create a lean file in the playground directory""" + LEAN_PLAYGROUND_DIR.mkdir(parents=True, exist_ok=True) + lean_file = LEAN_PLAYGROUND_DIR / f"{file_name}.lean" + with open(lean_file, "w") as f: + f.write(content) + return lean_file + + +def check_lean_compile(lean_file: Path, timeout: int = 120) -> Tuple[bool, str]: + """Check if the Lean file compiles successfully""" + try: + result = subprocess.run( + ["lake", "lean", str(lean_file)], + check=False, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=timeout, + cwd=VERINA_ROOT, + ) + + output = result.stdout.decode() + "\n" + result.stderr.decode() + + if result.returncode == 0: + return True, output + else: + return False, output + + except subprocess.TimeoutExpired: + return False, "TIMEOUT" + except Exception as e: + return False, f"ERROR: {e}" + + +# Unit Test Rendering +CODE_TEST_MSG_MARKER = "code_test" +DECIDABLE_ERR_MSG = "did not evaluate to `true`" + + +def render_unit_test_value(lean_type: str, value) -> str: + """Convert a Python value to Lean syntax based on type""" + if lean_type == "Bool": + return str(value).lower() + elif lean_type == "String": + return f'"{value}"' + elif lean_type == "Char": + return f"'{value}'" + else: + return str(value) + + +def render_code_unit_test(signature: dict, test_case: dict, test_idx: int) -> str: + """Render a single unit test using #guard""" + func_name = signature.get("name", "solution") + params = signature.get("parameters", []) + return_type = signature.get("return_type", "Bool") + + rendered = f'#print "<{CODE_TEST_MSG_MARKER}>{test_idx}"\n\n' + rendered += f"#guard {func_name}" + + for param in params: + param_name = param["param_name"] + param_type = param["param_type"] + input_value = test_case["input"].get(param_name, "") + rendered += f" ({render_unit_test_value(param_type, input_value)})" + + # Add (by sorry) to satisfy precondition hypothesis + rendered += " (by sorry)" + + # Add expected value comparison + expected = test_case.get("expected", "") + rendered += f" == ({render_unit_test_value(return_type, expected)})" + + return rendered + + +def build_test_lean_file(data: BenchmarkData, generated_code: str, include_unit_tests: bool = True) -> str: + """Build a complete Lean file to test the generated code""" + signature = data.signature + func_name = signature.get("name", "solution") + return_type = signature.get("return_type", "Bool") + param_list = render_param_list(signature) + params = signature.get("parameters", []) + param_names = " ".join([f"({p['param_name']})" for p in params]) + + # Indent multiline generated_code so all lines have proper indentation + # First line gets 2 spaces from template, subsequent lines need explicit indentation + if '\n' in generated_code: + lines = generated_code.split('\n') + # First line has no extra indent (template adds 2 spaces) + # Subsequent lines need 2 spaces prepended + indented_lines = [lines[0]] + [' ' + line if line.strip() else line for line in lines[1:]] + generated_code = '\n'.join(indented_lines) + + # Build imports - include both task and solution imports + task_imports = data.lean_data.get("task_imports", "").strip() + solution_imports = data.lean_data.get("solution_imports", "").strip() + imports = task_imports + if solution_imports: + imports += "\n" + solution_imports + if "import Mathlib" not in imports: + imports = "import Mathlib\n" + imports + + # Build auxiliary definitions - include solution_aux which has helper functions + solution_aux = data.lean_data.get("solution_aux", "").strip() + task_aux = data.lean_data.get("task_aux", "").strip() + precond_aux = data.lean_data.get("precond_aux", "").strip() + postcond_aux = data.lean_data.get("postcond_aux", "").strip() + code_aux = data.lean_data.get("code_aux", "").strip() + + # Build precondition + precond = data.lean_data.get("precond", "True").strip() + precond_name = f"{func_name}_precond" + + # Build postcondition + postcond = data.lean_data.get("postcond", "").strip() + postcond_name = f"{func_name}_postcond" + + lean_content = f"""{imports} + +-- Solution auxiliary definitions (helper functions) +{solution_aux} + +-- Task auxiliary definitions +{task_aux} + +-- Precondition auxiliary definitions +{precond_aux} + +@[reducible, simp] +def {precond_name} {param_list} : Prop := + {precond} + +-- Postcondition auxiliary definitions +{postcond_aux} + +-- Code auxiliary definitions +{code_aux} + +def {func_name} {param_list} (h_precond : {precond_name} {param_names}) : {return_type} := + {generated_code} + +@[reducible, simp] +def {postcond_name} {param_list} (result: {return_type}) (h_precond : {precond_name} {param_names}) : Prop := + {postcond} + +-- Verification theorem (compilation test) +-- If this compiles, the code at least type-checks +#check {func_name} +""" + + # Add unit tests if requested + if include_unit_tests and data.tests: + lean_content += "\n-- Unit Tests\n" + for idx, test_case in enumerate(data.tests): + lean_content += "\n" + render_code_unit_test(signature, test_case, idx) + "\n" + + return lean_content + + +def parse_unit_test_results(compile_output: str, num_tests: int) -> Tuple[int, int, dict]: + """ + Parse the compilation output to determine which unit tests passed/failed. + + Returns: (num_passed, num_failed, test_results_dict) + """ + test_results = {} + + # If compilation succeeded with no errors, all tests passed + if "error" not in compile_output.lower(): + for idx in range(num_tests): + test_results[idx] = "pass" + return num_tests, 0, test_results + + # Parse the output to find which tests failed + # Look for markers like 0 followed by error messages + code_test_start = f"<{CODE_TEST_MSG_MARKER}>" + code_test_end = f"" + + # Split by start marker to get test sections + parts = compile_output.split(code_test_start) + + # Build a map of test index to message + test_messages = {} + for part in parts[1:]: # Skip first part (before any marker) + if code_test_end in part: + idx_str, rest = part.split(code_test_end, 1) + try: + test_idx = int(idx_str.strip()) + test_messages[test_idx] = rest + except ValueError: + continue + + num_passed = 0 + num_failed = 0 + + for idx in range(num_tests): + msg = test_messages.get(idx, "") + if DECIDABLE_ERR_MSG in msg: + test_results[idx] = "fail" + num_failed += 1 + elif "error" in msg.lower(): + # Some other error (e.g., type mismatch) - count as fail + test_results[idx] = "error" + num_failed += 1 + else: + test_results[idx] = "pass" + num_passed += 1 + + return num_passed, num_failed, test_results + + +def evaluate_generated_code(data: BenchmarkData, generated_code: str, task_idx: int) -> Tuple[bool, bool, str, dict]: + """ + Evaluate the generated code by compiling it with Lean and running unit tests. + + Returns: (compiles, all_tests_pass, output, test_results) + """ + lean_content = build_test_lean_file(data, generated_code, include_unit_tests=True) + + # Create lean file + lean_file = create_lean_file(f"test_{data.data_id}_{task_idx}", lean_content) + + # Check compilation (which also runs unit tests via #guard) + compiles, output = check_lean_compile(lean_file) + + # Parse unit test results + num_tests = len(data.tests) if data.tests else 0 + if num_tests > 0: + num_passed, num_failed, test_results = parse_unit_test_results(output, num_tests) + all_tests_pass = (num_failed == 0) and compiles + else: + # No tests, just check compilation + test_results = {} + all_tests_pass = compiles + + return compiles, all_tests_pass, output, test_results From 7cab672de9aea2721e8f204ab49a460ec7c8a62c Mon Sep 17 00:00:00 2001 From: root Date: Wed, 4 Mar 2026 12:54:58 +0000 Subject: [PATCH 08/11] add fixes --- .../TTSwithVerification/bestofk_baseline.py | 25 +++--- examples/TTSwithVerification/check_tokens.py | 27 ++++++ .../start_vllm_multiprocess.sh | 36 ++------ .../test_maze_extraction.py | 86 +++++++++++++++++++ examples/TTSwithVerification/test_vllm_api.py | 40 +++++++++ 5 files changed, 175 insertions(+), 39 deletions(-) create mode 100644 examples/TTSwithVerification/check_tokens.py create mode 100644 examples/TTSwithVerification/test_maze_extraction.py create mode 100644 examples/TTSwithVerification/test_vllm_api.py diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index 9b52206d..999ec6f8 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -19,9 +19,9 @@ from interwhen import stream_completion # ============== MODEL CONFIGURATION ============== -MAIN_MODEL = "Qwen/QwQ-32B" +MAIN_MODEL = "Qwen/Qwen3-30B-A3B-Thinking-2507" # Multi-process vLLM configuration -VLLM_PORTS = [8000, 8001, 8002, 8003] # 4 instances with tensor-parallel-size 2 each +VLLM_PORTS = [8000, 8001, 8002] # 3 instances with tensor-parallel-size 2 each REQUEST_COUNTER = {"main": 0, "critic": 0} # Track request count for round-robin load balancing @@ -67,7 +67,7 @@ def get_next_port(server_type: str = "main") -> int: return port -def get_output_dirs(task: str, main_model: str, use_critic: bool, critic_early_stop: bool, base_dir: str = "../../b-pchanda/Outputs_TTS/BestOfKResults"): +def get_output_dirs(task: str, main_model: str, use_critic: bool, critic_early_stop: bool, base_dir: str = "../../b-pchanda/Outputs_TTS_temp/BestOfKResults"): model_short_name = get_model_short_name(main_model) critic_status = "on" if use_critic else "off" earlystop_status = "on" if critic_early_stop else "off" @@ -82,26 +82,25 @@ def get_output_dirs(task: str, main_model: str, use_critic: bool, critic_early_s os.makedirs(dir_path, exist_ok=True) return dirs - -def init_llm_server(model_name, max_tokens=32768, port=8000, temperature=0.6, seed=42): +def init_llm_server(modelname, max_tokens=200, port=8000, temperature=0.6, seed=42): # url = f"http://localhost:{port}/v1/completions" payload = { - "model": model_name, + "model": modelname, "max_tokens": max_tokens, "top_k": 20, "top_p": 0.95, "min_p": 0.0, - "do_sample": True, + "do_sample" : True, "temperature": temperature, "stream": False, "logprobs": 20, "use_beam_search": False, "prompt_cache": True, - "seed": seed, + "seed" : seed } headers = {"Content-Type": "application/json"} return {"url": url, "payload": payload, "headers": headers} - + def count_tokens(text: str, tokenizer) -> int: """Count tokens in text, with fallback to character count.""" @@ -346,7 +345,9 @@ def resolve_indices(task, dataset_len, args): except ValueError: raise ValueError(f"Invalid xrange format: {args.xrange}. Use 'start-end'") if args.num_examples: - return np.linspace(0, dataset_len - 1, args.num_examples, dtype=int) + max_idx = dataset_len - 1 + upper_bound = min(max_idx, 1362) if task == "game24" else min(max_idx, 1499) + return list((np.linspace(0, upper_bound, args.num_examples)).astype(int)) # Default: use full range start = args.start if args.start is not None else 0 end = args.end if args.end is not None else dataset_len @@ -650,8 +651,8 @@ def run_k_samples_with_critic( parser = argparse.ArgumentParser(description="Best-of-K baseline (standard CoT) for TTSwithVerification datasets") parser.add_argument("--task", type=str, required=True, choices=["game24", "maze", "spatialmap"], help="Task to run") - parser.add_argument("--k", type=int, default=4, help="Number of samples per example") - parser.add_argument("--num_examples", "-n", type=int, default=None, + parser.add_argument("--k", type=int, default=1, help="Number of samples per example") + parser.add_argument("--num_examples", "-n", type=int, default=100, help="Number of examples to run (overrides start/end)") parser.add_argument("--indices", type=str, default=None, help="Comma-separated indices to run") diff --git a/examples/TTSwithVerification/check_tokens.py b/examples/TTSwithVerification/check_tokens.py new file mode 100644 index 00000000..4380c194 --- /dev/null +++ b/examples/TTSwithVerification/check_tokens.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Check what payload is actually being sent.""" +import requests +import json + +url = "http://localhost:8000/v1/completions" +payload = { + "model": "Qwen/QwQ-32B", + "prompt": "What is 2+2?", + "max_tokens": 50, + "temperature": 0.0, + "stream": False, +} + +print("Payload being sent:") +print(json.dumps(payload, indent=2)) + +resp = requests.post(url, json=payload, timeout=10) +result = resp.json() +output = result["choices"][0]["text"] + +print(f"\nResponse usage:") +print(f" Tokens generated: {result['usage'].get('completion_tokens', '?')}") +print(f" Total tokens: {result['usage'].get('total_tokens', '?')}") + +print(f"\nOutput ({len(output)} chars):") +print(repr(output)) diff --git a/examples/TTSwithVerification/start_vllm_multiprocess.sh b/examples/TTSwithVerification/start_vllm_multiprocess.sh index 080b58b7..601918a2 100755 --- a/examples/TTSwithVerification/start_vllm_multiprocess.sh +++ b/examples/TTSwithVerification/start_vllm_multiprocess.sh @@ -1,12 +1,11 @@ #!/bin/bash -# Start 4 vLLM processes with explicit GPU assignment -# Process 1: GPUs 0-1, Port 8000 -# Process 2: GPUs 2-3, Port 8001 -# Process 3: GPUs 4-5, Port 8002 -# Process 4: GPUs 6-7, Port 8003 +# Start 3 vLLM processes with explicit GPU assignment +# Process 1: GPUs 0-1, Port 8000, TP=2 +# Process 2: GPUs 2-3, Port 8001, TP=2 +# Process 3: GPUs 4-5, Port 8002, TP=2 -MODEL="Qwen/QwQ-32B" +MODEL="Qwen/Qwen3-30B-A3B-Thinking-2507" GPU_MEMORY=0.4 TENSOR_PARALLEL=2 @@ -14,7 +13,7 @@ echo "Killing any existing vLLM processes..." pkill -9 -f "vllm.entrypoints.openai.api_server" sleep 2 -echo "Starting 4 vLLM processes..." +echo "Starting 3 vLLM processes..." # Process 1 - GPUs 0,1 ( @@ -62,31 +61,14 @@ sleep 5 PID3=$! echo "Started Process 3 (GPUs 4-5, Port 8002) - PID: $PID3" -sleep 5 - -# Process 4 - GPUs 6,7 -( - export CUDA_VISIBLE_DEVICES=6,7 - python -m vllm.entrypoints.openai.api_server \ - --model $MODEL \ - --port 8003 \ - --tensor-parallel-size $TENSOR_PARALLEL \ - --gpu-memory-utilization $GPU_MEMORY \ - --disable-log-requests \ - > /tmp/vllm_8003.log 2>&1 -) & -PID4=$! -echo "Started Process 4 (GPUs 6-7, Port 8003) - PID: $PID4" - echo "" -echo "All 4 vLLM processes started successfully." -echo "Process PIDs: $PID1 $PID2 $PID3 $PID4" +echo "All 3 vLLM processes started successfully." +echo "Process PIDs: $PID1 $PID2 $PID3" echo "" echo "Log files:" echo " /tmp/vllm_8000.log - Process 1" echo " /tmp/vllm_8001.log - Process 2" echo " /tmp/vllm_8002.log - Process 3" -echo " /tmp/vllm_8003.log - Process 4" echo "" echo "To stop all processes, run:" echo " pkill -9 -f 'vllm.entrypoints.openai.api_server'" @@ -95,4 +77,4 @@ echo "Waiting for processes to initialize (this may take 60-120 seconds)..." echo "" # Wait for all processes -wait $PID1 $PID2 $PID3 $PID4 +wait $PID1 $PID2 $PID3 diff --git a/examples/TTSwithVerification/test_maze_extraction.py b/examples/TTSwithVerification/test_maze_extraction.py new file mode 100644 index 00000000..bd5d4632 --- /dev/null +++ b/examples/TTSwithVerification/test_maze_extraction.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +"""Test maze/spatialmap extraction with real model output.""" +import requests +import json +import re +from datasets import load_dataset + +def extract_solution_mcq(text): + """Current extraction function from baseline.""" + patterns = [ + r"\\boxed\{([^}]*)\}", # \boxed{...} + r"boxed\{([^}]*)\}", # boxed{...} without escape + r"\*\*([A-D])\*\*", # **A** format + r"answer[:\s]*([A-D])", # answer: A format + r"(?:^|\n)([A-D])(?:\s|$|\.)", # Standalone letter + ] + + for pattern in patterns: + matches = re.findall(pattern, text, re.IGNORECASE) + if matches: + expr = matches[-1].strip() + choice_match = re.search(r"\b([ABCD])\b", expr, flags=re.IGNORECASE) + if choice_match: + return choice_match.group(1).upper() + + standalone = re.findall(r"\b([ABCD])\b", text) + if standalone: + return standalone[-1].upper() + + return None + +# Load maze dataset +print("Loading maze dataset...") +dataset = load_dataset("microsoft/VISION_LANGUAGE", "maze_text_only", split="val") +example = dataset[0] + +# Build prompt like baseline does +pre_prompt = ( + "You are an expert problem solver. Carefully read the following multiple-choice question " + "and think through the solution step-by-step before providing your final answer. " + "Provide your final answer option by enclosing it within \\boxed{A/B/C/D}.:" +) +prompt_text = str(example.get("prompt", ""))[:500] # First 500 chars + +full_prompt = f"<|im_start|>system\nYou are helpful.\n<|im_end|>\n<|im_start|>user\n{pre_prompt}\n\n{prompt_text}\n<|im_end|>\n<|im_start|>assistant\n" + +print(f"\nFull prompt:\n{full_prompt[:300]}...\n") + +# Test on port 8000 +url = "http://localhost:8000/v1/completions" +payload = { + "model": "Qwen/QwQ-32B", + "prompt": full_prompt, + "max_tokens": 500, + "temperature": 0.6, + "stream": False, +} + +print("Requesting model output...") +resp = requests.post(url, json=payload, timeout=30) +if resp.status_code == 200: + result = resp.json() + output = result["choices"][0].get("text", "") + print(f"\nModel output ({len(output)} chars):") + print("="*60) + print(output) + print("="*60) + + extracted = extract_solution_mcq(output) + print(f"\nExtraction result: {extracted}") + + # Try alternative patterns + print("\nTrying alternative extraction patterns:") + if "\\boxed{" in output: + print(" - Contains \\boxed{ pattern") + if r"\boxed{" in output or r"\\boxed{" in output: + print(" - Contains escaped boxed") + if re.search(r"[Aa]nswer[:\s]*([A-D])", output): + print(" - Found 'answer: X' pattern") + response_letters = re.findall(r"\b[A-D]\b", output) + if response_letters: + print(f" - Found standalone letters: {response_letters}") + +else: + print(f"Error: {resp.status_code}") + print(resp.text[:500]) diff --git a/examples/TTSwithVerification/test_vllm_api.py b/examples/TTSwithVerification/test_vllm_api.py new file mode 100644 index 00000000..eaf88430 --- /dev/null +++ b/examples/TTSwithVerification/test_vllm_api.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Test vLLM API responses directly.""" +import requests +import json + +# Test each port +for port in [8000, 8001, 8002, 8003]: + print(f"\n{'='*60}") + print(f"Testing port {port}...") + print('='*60) + + url = f"http://localhost:{port}/v1/completions" + + # Test: simple completion + payload = { + "model": "Qwen/QwQ-32B", + "prompt": "What is 2+2? Answer:", + "max_tokens": 50, + "temperature": 0.0, + "stream": False, + } + + try: + resp = requests.post(url, json=payload, timeout=10) + print(f"Status: {resp.status_code}") + + if resp.status_code == 200: + result = resp.json() + print(f"Response keys: {result.keys()}") + if "choices" in result and len(result["choices"]) > 0: + choice = result["choices"][0] + text = choice.get("text", "") + print(f"Generated text: {repr(text[:100])}") + print(f"Text length: {len(text)}") + else: + print(f"No choices in response: {result}") + else: + print(f"Error response: {resp.text[:200]}") + except Exception as e: + print(f"Request failed: {e}") From 9802544ad33bdf622424ea207f97bd9379022251 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 4 Mar 2026 12:57:00 +0000 Subject: [PATCH 09/11] add new changes --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index d7b37631..43e0af15 100644 --- a/.gitignore +++ b/.gitignore @@ -200,6 +200,7 @@ cython_debug/ # refer to https://docs.cursor.com/context/ignore-files .*/Outputs_TTS/ Outputs_TTS/ +Outputs_TTS_temp/ .cursorignore .cursorindexingignore From c8949172b2f8159cd6389b767cada7aa4cfcaa7a Mon Sep 17 00:00:00 2001 From: Vijval9 <114911861+Vijval9@users.noreply.github.com> Date: Wed, 4 Mar 2026 18:46:16 +0530 Subject: [PATCH 10/11] Fixed some serialization issues Added a custom JSON encoder to handle numpy types when dumping JSON. --- examples/TTSwithVerification/bestofk_baseline.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/examples/TTSwithVerification/bestofk_baseline.py b/examples/TTSwithVerification/bestofk_baseline.py index 5a545681..eb6aef3d 100644 --- a/examples/TTSwithVerification/bestofk_baseline.py +++ b/examples/TTSwithVerification/bestofk_baseline.py @@ -42,6 +42,19 @@ # Save the real stderr so tqdm always works even if suppress_output is active _real_stderr = sys.stderr +class NumpyEncoder(json.JSONEncoder): + """Custom JSON encoder that handles numpy types.""" + def default(self, obj): + if isinstance(obj, np.bool_): + return bool(obj) + if isinstance(obj, np.integer): + return int(obj) + if isinstance(obj, np.floating): + return float(obj) + if isinstance(obj, np.ndarray): + return obj.tolist() + return super().default(obj) + @contextmanager def suppress_output(): @@ -1006,5 +1019,5 @@ def process_example(idx): summary_path = os.path.join(output_dirs["base"], "summary.json") with open(summary_path, "w", encoding="utf-8") as f: - json.dump(summary, f, indent=2) + json.dump(summary, f, indent=2, cls=NumpyEncoder) # logger.info(f"Saved summary to {summary_path}") From 4e72e1616940dd154b54f81127e1637c56010eef Mon Sep 17 00:00:00 2001 From: ashmitkx <66110457+ashmitkx@users.noreply.github.com> Date: Tue, 17 Mar 2026 12:17:47 +0000 Subject: [PATCH 11/11] remove zebra setup code (added in different pr) --- interwhen/data/__init__.py | 0 interwhen/data/zebralogic_ir_map.json | 285374 ----------------------- interwhen/utils/zebralogic_helper.py | 284 - 3 files changed, 285658 deletions(-) delete mode 100644 interwhen/data/__init__.py delete mode 100644 interwhen/data/zebralogic_ir_map.json delete mode 100644 interwhen/utils/zebralogic_helper.py diff --git a/interwhen/data/__init__.py b/interwhen/data/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/interwhen/data/zebralogic_ir_map.json b/interwhen/data/zebralogic_ir_map.json deleted file mode 100644 index 2558c733..00000000 --- a/interwhen/data/zebralogic_ir_map.json +++ /dev/null @@ -1,285374 +0,0 @@ -{ - "lgp-test-2x2-18": { - "clue_irs": [ - [ - [ - { - "type": "same_house", - "A": [ - "name", - "arnold" - ], - "B": [ - "height", - "veryshort" - ] - } - ] - ], - [ - [ - { - "type": "not_place", - "entity": [ - "name", - "eric" - ], - "pos": 2 - } - ] - ] - ], - "solution_irs": [ - [ - { - "type": "place", - "entity": [ - "name", - "eric" - ], - "pos": 1 - }, - { - "type": "place", - "entity": [ - "height", - "short" - ], - "pos": 1 - }, - { - "type": "place", - "entity": [ - "name", - "arnold" - ], - "pos": 2 - }, - { - "type": "place", - "entity": [ - "height", - "veryshort" - ], - "pos": 2 - } - ] - ] - }, - "lgp-test-2x2-33": { - "clue_irs": [ - [ - [ - { - "type": "pos_relation", - "A": [ - "name", - "eric" - ], - "B": [ - "name", - "arnold" - ], - "direction": "left", - "dist": "?" - } - ] - ], - [ - [ - { - "type": "not_place", - "entity": [ - "pet", - "dog" - ], - "pos": 1 - } - ] - ] - ], - "solution_irs": [ - [ - { - "type": "place", - "entity": [ - "name", - "eric" - ], - "pos": 1 - 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"flower", - "carnations" - ], - "pos": 2 - }, - { - "type": "place", - "entity": [ - "color", - "green" - ], - "pos": 2 - }, - { - "type": "place", - "entity": [ - "favoritesport", - "soccer" - ], - "pos": 2 - }, - { - "type": "place", - "entity": [ - "name", - "eric" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "phonemodel", - "xiaomi mi 11" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "cigar", - "blends" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "flower", - "roses" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "color", - "yellow" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "favoritesport", - "tennis" - ], - "pos": 3 - }, - { - "type": "place", - "entity": [ - "name", - "peter" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "phonemodel", - "iphone 13" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "cigar", - "dunhill" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "flower", - "lilies" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "color", - "blue" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "favoritesport", - "volleyball" - ], - "pos": 4 - }, - { - "type": "place", - "entity": [ - "name", - "arnold" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "phonemodel", - "huawei p50" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "cigar", - "prince" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "flower", - "daffodils" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "color", - "purple" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "favoritesport", - "basketball" - ], - "pos": 5 - }, - { - "type": "place", - "entity": [ - "name", - "bob" - ], - "pos": 6 - }, - { - "type": "place", - "entity": [ - "phonemodel", - "google pixel 6" - ], - "pos": 6 - }, - { - "type": "place", - "entity": [ - "cigar", - "pall mall" - ], - "pos": 6 - }, - { - "type": "place", - "entity": [ - "flower", - "tulips" - ], - "pos": 6 - }, - { - "type": "place", - "entity": [ - "color", - "white" - ], - "pos": 6 - }, - { - "type": "place", - "entity": [ - "favoritesport", - "swimming" - ], - "pos": 6 - } - ] - ] - } -} \ No newline at end of file diff --git a/interwhen/utils/zebralogic_helper.py b/interwhen/utils/zebralogic_helper.py deleted file mode 100644 index cde8a55b..00000000 --- a/interwhen/utils/zebralogic_helper.py +++ /dev/null @@ -1,284 +0,0 @@ -""" -Prompt and dataset utilities for WildEval/ZebraLogic. -""" - -import re -import json -from collections import defaultdict -from typing import Dict, List, Tuple, Optional, Set -from importlib import resources -import datasets - - -# ============== Prompt Templates ============== - -SYSTEM_PROMPT_VANILLA = """\ -# Problem Description - -You are solving a house grid problem. You are given: -1. Features and Domains - - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. - - A set of features (e.g., color, name, pet, book genre). - - Each feature has a finite domain of possible values. -2. Constraint: - - Each house has exactly one value per feature. - - No two houses share the same value for the same feature. -3. Clues / Constraints descrbing: - - Houses and their positions - - Feature values - - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") - -Solve to your best ability the arrangement of features across the houses. - -# Final Answer Format - -```json -{ - "House 1": { "feature1": "value1", "feature2": "value2", ... }, - "House 2": { "feature1": "value1", "feature2": "value2", ... }, - ... -} -``` - -Make sure to use the exact feature/value names as given in the problem description. -Make sure the JSON is valid and parsable.""" - -SYSTEM_PROMPT_STATEPOLL = """\ -# Problem Description - -You are solving a house grid problem. You are given: -1. Features and Domains - - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. - - A set of features (e.g., color, name, pet, book genre). - - Each feature has a finite domain of possible values. -2. Constraint: - - Each house has exactly one value per feature. - - No two houses share the same value for the same feature. -3. Clues / Constraints describing: - - Houses and their positions - - Feature values - - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") - -# Rules for Solving - -1. Reason about the problem in text. -2. After every inference, no matter how minor or tentative, immediately report the updated partial assignments. - - Always output partial assignments frequently as the reasoning progresses, not only at major steps or when confident. - - If an inference adds, removes, or narrows even a single possibility, report it. - -# House/Feature Partial Assignment Reporting Format - -```json -{ - "House N": { "feature1": "value1", "feature2": "value2", ... }, - ... -} -``` - -Omit any unassigned features. -Make sure to use the exact feature/value names as given in the problem description. -Make sure the JSON is valid and parsable.""" - -SYSTEM_PROMPT_STATEFEEDBACK = """\ -# Problem Description - -You are solving a house grid problem. You are given: -1. Features and Domains - - A fixed number of houses, indexed sequentially (e.g., House 1, House 2, …) from left to right. - - A set of features (e.g., color, name, pet, book genre). - - Each feature has a finite domain of possible values. -2. Constraint: - - Each house has exactly one value per feature. - - No two houses share the same value for the same feature. -3. Clues / Constraints describing: - - Houses and their positions - - Feature values - - Relative ordering (e.g., "next to", "to the left of", "2 houses away from") - -# Rules for Solving - -1. Reason about the problem in text. -2. You may receive feedback from the user if anything is wrong. Use any feedback to guide your reasoning until a complete solution is reached. -3. Do not stop responding until you've assigned each and every variable. - -# Final Answer Reporting Format - -```json -{ - "House 1": { "feature1": "value1", "feature2": "value2", ... }, - "House 2": { "feature1": "value1", "feature2": "value2", ... }, - ... -} -``` - -Make sure to use the exact feature/value names as given in the problem description. -Make sure the JSON is valid and parsable.""" - -USER_PROMPT_TEMPLATE = "{problem_text}" - -# ============== Dataset Loading ============== - -def clean_problem_text(problem_text: str, features: dict) -> str: - """Clean up problem text giving explicit feature domains.""" - desc, clues = problem_text.split('## clues:') - line0 = desc.splitlines()[0] - - feature_text = '' - for feature, values in features.items(): - values_str = ', '.join(f"'{v}'" for v in values) - feature_text += f"- '{feature}': {values_str}\n" - - return f"{line0}\n{feature_text}\n## clues:{clues}".strip() - - -def process_zebralogic_problem(problem: dict, ir_map: dict) -> dict: - """Process a raw ZebraLogic problem into the format needed by ZebraLogicProblem. - - Args: - problem: Raw problem dict from the HuggingFace dataset. - ir_map: Dict mapping problem IDs to their IR representations. - - Returns: - Processed problem dict with keys: n_houses, n_features, features, clues, - clue_irs, solution_irs, solution, puzzle_clean, etc. - """ - def apply_text_replacements(problem): - replacements = [ - ['january', 'jan'], ['february', 'feb'], ['march', 'mar'], - ['august', 'aug'], ['september', 'sept'], ['f-150', 'f150'], - ['animal', 'pet'], ['loves the spaghetti eater', 'loves spaghetti'], - ['very short', 'veryshort'], ['super short', 'supershort'], - ['very tall', 'verytall'], ['super tall', 'supertall'], - ] - problem_str = json.dumps(problem) - for old, new in replacements: - problem_str = problem_str.replace(old, new) - return json.loads(problem_str) - - pid = problem['id'] - size = problem["size"] - n_houses, n_features = map(int, size.split("*")) - - problem_text = problem["puzzle"].lower() - clues_raw = re.split(r"##\s*clues\s*:", problem_text, flags=re.IGNORECASE)[1].strip().split("\n") - clues = [] - for clue in clues_raw: - clue_text_index, clue_text = clue.strip().split(". ", 1) - clues.append({ - "text_index": int(clue_text_index.strip()), - "text": clue_text.strip() - }) - - solution = problem["solution"] - solution['header'] = [h.lower() for h in solution['header']] - solution['rows'] = [[v.lower() for v in row] for row in solution['rows']] - - features = defaultdict(list) - for row in solution['rows']: - for fname, value in zip(solution['header'], row): - if fname.lower() == 'house': - continue - features[fname].append(value) - features = dict(features) - for fname in features: - features[fname] = sorted(list(set(features[fname]))) - assert len(features[fname]) == n_houses - assert len(features) == n_features - - processed_solution = {f'House {i+1}': {} for i in range(n_houses)} - for house_i, row in enumerate(solution['rows']): - for fname, value in zip(solution['header'][1:], row[1:]): - processed_solution[f'House {house_i+1}'][fname.lower()] = value.lower() - problem['solution'] = processed_solution - - problem['puzzle'] = problem_text - problem["clues"] = clues - problem["features"] = features - problem["n_houses"] = n_houses - problem["n_features"] = n_features - problem["clue_irs"] = ir_map[pid]["clue_irs"] - problem["solution_irs"] = ir_map[pid]["solution_irs"] - problem['puzzle_clean'] = clean_problem_text(problem['puzzle'], problem['features']) - - return apply_text_replacements(problem) - -def get_zebralogic_dataset() -> list: - """Load and process the ZebraLogic dataset from HuggingFace. - - Loads WildEval/ZebraLogic grid_mode test split and processes each problem - with the IR map. - - The IR map file must be at interwhen/data/zebralogic_ir_map.json - - Returns: - List of processed problem dicts. - """ - - dataset = datasets.load_dataset("WildEval/ZebraLogic", "grid_mode", split="test").to_list() - - pkg = "interwhen.data" - with resources.files(pkg).joinpath("zebralogic_ir_map.json").open("r") as f: - ir_map = json.load(f) - - # Known problematic problem IDs (unsolvable or malformed) - bad_ids = { - 'lgp-test-6x5-2', 'lgp-test-6x6-5', 'lgp-test-2x5-1', 'lgp-test-4x5-5', - 'lgp-test-2x4-6', 'lgp-test-2x6-11', 'lgp-test-4x6-35', 'lgp-test-3x5-15', - 'lgp-test-5x5-37', 'lgp-test-5x5-17', 'lgp-test-4x5-15', 'lgp-test-6x6-2', - 'lgp-test-5x6-4', 'lgp-test-5x6-2', 'lgp-test-5x5-1' - } - dataset = [p for p in dataset if p['id'] not in bad_ids] - dataset = [process_zebralogic_problem(p, ir_map) for p in dataset] - return dataset - -def extract_last_json(text): - """Extract the last JSON object from the model's output text.""" - json_text = text.split('')[-1].strip() - - # try with md tags - matches = re.findall(r'```json(.*?)```', json_text, re.DOTALL) - if matches and len(matches) > 0: - json_match = matches[-1] - return json.loads(json_match.strip()) - - # try without assuming md tags - matches = re.findall(r'\{.*\}\s*?}', json_text, re.DOTALL) - if matches and len(matches) > 0: - json_match = matches[0] - return json.loads(json_match.strip()) - - return None - -def zebra_correctness(problem: dict, candidate_solution: dict) -> Tuple[int, int, int, int]: - """Check candidate solution against ground truth. - - Args: - problem: Processed problem dict with 'solution', 'n_houses', 'n_features', 'features'. - candidate_solution: Dict mapping "House N" -> {feature: value}. - - Returns: - (correct, skipped, missing, total) counts where: - correct: Number of matching assignments - skipped: Assignments for invalid houses/features - missing: Features not in candidate solution - total: Total expected assignments (n_houses * n_features) - """ - c, s = 0, 0 - t_soln = 0 - t = problem['n_houses'] * problem['n_features'] - solution = problem['solution'] - - for house in candidate_solution: - if house not in solution: - s += len(problem['features']) - continue - for fname in candidate_solution[house]: - if fname not in solution[house]: - s += 1 - continue - t_soln += 1 - if candidate_solution[house][fname] == solution[house][fname]: - c += 1 - - m = t - t_soln - return c, s, m, t