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Countdown wip
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| """ | ||
| Preprocess dataset for countdown task - given a target number and N numbers, generate equations to reach target | ||
| """ | ||
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| import re | ||
| import os | ||
| from datasets import Dataset, load_dataset | ||
| from random import randint, seed, choice | ||
| from typing import List, Tuple | ||
| from tqdm import tqdm | ||
| from verl.utils.hdfs_io import copy, makedirs | ||
| import argparse | ||
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| def gen_dataset( | ||
| num_samples: int, | ||
| num_operands: int = 6, | ||
| max_target: int = 1000, | ||
| min_number: int = 1, | ||
| max_number: int = 100, | ||
| operations: List[str] = ['+', '-', '*', '/'], | ||
| seed_value: int = 42, | ||
| ) -> List[Tuple]: | ||
| """Generate dataset for countdown task. | ||
| Args: | ||
| num_samples: Number of samples to generate | ||
| num_operands: Number of numbers provided in each sample | ||
| max_target: Maximum value for target number | ||
| min_number: Minimum value for provided numbers | ||
| max_number: Maximum value for provided numbers | ||
| operations: List of allowed operations | ||
| seed_value: Random seed for reproducibility | ||
| Returns: | ||
| List of tuples containing (target, numbers, solution) | ||
| """ | ||
| seed(seed_value) | ||
| samples = [] | ||
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| for _ in tqdm(range(num_samples)): | ||
| # Generate random target | ||
| target = randint(1, max_target) | ||
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| # Generate random numbers | ||
| numbers = [randint(min_number, max_number) for _ in range(num_operands)] | ||
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| samples.append((target, numbers)) | ||
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| return samples | ||
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| def make_prefix(dp): | ||
| target = dp['target'] | ||
| numbers = dp['nums'] | ||
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| prefix = f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. | ||
| User: Using the numbers {numbers}, create an equation that equals {target}. You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. Show your work in <think> </think> tags. And return the final answer in <answer> </answer> tags, for example <answer> (1 + 2) / 3 </answer>. | ||
| Assistant: Let me solve this step by step. | ||
| <think>""" | ||
| return prefix | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--local_dir', default='~/data/countdown') | ||
| parser.add_argument('--hdfs_dir', default=None) | ||
| parser.add_argument('--num_samples', type=int, default=100000) | ||
| parser.add_argument('--num_operands', type=int, default=6) | ||
| parser.add_argument('--max_target', type=int, default=1000) | ||
| parser.add_argument('--min_number', type=int, default=1) | ||
| parser.add_argument('--max_number', type=int, default=100) | ||
| parser.add_argument('--train_size', type=int, default=327680) | ||
| parser.add_argument('--test_size', type=int, default=4096) | ||
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| args = parser.parse_args() | ||
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| data_source = 'countdown' | ||
| TRAIN_SIZE = args.train_size | ||
| TEST_SIZE = args.test_size | ||
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| raw_dataset = load_dataset('Jiayi-Pan/Countdown-Tasks-3to4', split='train') | ||
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| assert len(raw_dataset) > TRAIN_SIZE + TEST_SIZE | ||
| train_dataset = raw_dataset.select(range(TRAIN_SIZE)) | ||
| test_dataset = raw_dataset.select(range(TRAIN_SIZE, TRAIN_SIZE + TEST_SIZE)) | ||
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| def make_map_fn(split): | ||
| def process_fn(example, idx): | ||
| question = make_prefix(example) | ||
| solution = { | ||
| "target": example['target'], | ||
| "numbers": example['nums'] | ||
| } | ||
| data = { | ||
| "data_source": data_source, | ||
| "prompt": [{ | ||
| "role": "user", | ||
| "content": question, | ||
| }], | ||
| "ability": "math", | ||
| "reward_model": { | ||
| "style": "rule", | ||
| "ground_truth": solution | ||
| }, | ||
| "extra_info": { | ||
| 'split': split, | ||
| 'index': idx, | ||
| } | ||
| } | ||
| return data | ||
| return process_fn | ||
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| train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True) | ||
| test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True) | ||
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| local_dir = args.local_dir | ||
| hdfs_dir = args.hdfs_dir | ||
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| train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet')) | ||
| test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet')) | ||
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| if hdfs_dir is not None: | ||
| makedirs(hdfs_dir) | ||
| copy(src=local_dir, dst=hdfs_dir) |
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