VeRL数据处理样例脚本

VeRL框架中的样例数据处理脚本分为大语言模型和多模态模型，样例脚本如下，根据模型类型选择：

VeRL大语言模型gsm8k数据处理
多模态模型geometry3k数据处理
多轮对话Agent训练gsm8k数据处理

大语言模型gsm8k数据处理

import argparse
import os
import re
import datasets
from verl.utils.hdfs_io import copy, makedirs
def extract_solution(solution_str):
    solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str)
    assert solution is not None
    final_solution = solution.group(0)
    final_solution = final_solution.split("#### ")[1].replace(",", "")
    return final_solution
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--local_dir", default="~/data/gsm8k")
    parser.add_argument("--hdfs_dir", default=None)
    args = parser.parse_args()
    data_source = "openai/gsm8k"
    dataset = datasets.load_dataset(xxx/xxx/xxx,"main")   # xxx/xxx/xxx是数据集路径
    train_dataset = dataset["train"]
    test_dataset = dataset["test"]
    instruction_following = 'Let\'s think step by step and output the final answer after "####".'

    # add a row to each data item that represents a unique id
    def make_map_fn(split):
        def process_fn(example, idx):
            question_raw = example.pop("question")
            question = question_raw + " " + instruction_following
            answer_raw = example.pop("answer")
            solution = extract_solution(answer_raw)
            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,
                    "answer": answer_raw,
                    "question": question_raw,
                },
            }
            return data
        return process_fn
    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)

    local_dir = args.local_dir
    hdfs_dir = args.hdfs_dir

    train_dataset.to_parquet(os.path.join(local_dir, "train.parquet"))
    test_dataset.to_parquet(os.path.join(local_dir, "test.parquet"))

    if hdfs_dir is not None:
        makedirs(hdfs_dir)
        copy(src=local_dir, dst=hdfs_dir)

多模态模型geometry3k数据处理

import argparse
import os
import datasets
from verl.utils.hdfs_io import copy, makedirs

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--local_dir", default="~/data/geo3k")
    parser.add_argument("--hdfs_dir", default=None)
    args = parser.parse_args()
    data_source = "hiyouga/geometry3k"
    dataset = datasets.load_dataset(xxx/xxx/xxx) # xxx/xxx/xxx是数据集路径
    train_dataset = dataset["train"]
    test_dataset = dataset["test"]
    instruction_following = (
        r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. "
        r"The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \boxed{}."
    )

    # add a row to each data item that represents a unique id
    def make_map_fn(split):
        def process_fn(example, idx):
            problem = example.pop("problem")
            prompt = problem + " " + instruction_following
            answer = example.pop("answer")
            images = example.pop("images")

            data = {
                "data_source": data_source,
                "prompt": [
                    {
                        "role": "user",
                        "content": prompt,
                    }
                ],
                "images": images,
                "ability": "math",
                "reward_model": {"style": "rule", "ground_truth": answer},
                "extra_info": {
                    "split": split,
                    "index": idx,
                    "answer": answer,
                    "question": problem,
                },
            }
            return data
        return process_fn
    train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True, num_proc=8)
    test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True, num_proc=8)

    local_dir = args.local_dir
    hdfs_dir = args.hdfs_dir

    train_dataset.to_parquet(os.path.join(local_dir, "train.parquet"))
    test_dataset.to_parquet(os.path.join(local_dir, "test.parquet"))

    if hdfs_dir is not None:
        makedirs(hdfs_dir)
        copy(src=local_dir, dst=hdfs_dir)

多轮对话Agent训练gsm8k数据处理

import argparse
import os
import re
import datasets
from verl.utils.hdfs_io import copy, makedirs

def extract_solution(solution_str):
    solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str)
    assert solution is not None
    final_solution = solution.group(0)
    final_solution = final_solution.split("#### ")[1].replace(",", "")
    return final_solution


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.")
    parser.add_argument("--hdfs_dir", default=None)
    parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.")
    parser.add_argument(
        "--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset."
    )

    args = parser.parse_args()
    local_dataset_path = args.local_dataset_path

    data_source = "openai/gsm8k"

    if local_dataset_path is not None:
        dataset = datasets.load_dataset(local_dataset_path, "main")
    else:
        dataset = datasets.load_dataset(data_source, "main")

    train_dataset = dataset["train"]
    test_dataset = dataset["test"]

    instruction_following = "Let's think step by step and output the final answer after `####`."

    # add a row to each data item that represents a unique id
    def make_map_fn(split):
        def process_fn(example, idx):
            question_raw = example.pop("question")

            question = question_raw + " " + instruction_following

            answer_raw = example.pop("answer")
            solution = extract_solution(answer_raw)
            data = {
                "data_source": data_source,
                "agent_name": "tool_agent",
                "prompt": [
                    {
                        "role": "system",
                        "content": (
                            "You are a math expert. You are given a question and you need to solve it step by step. "
                            "Reasoning step by step before any tool call. "
                            "You should use the `calc_gsm8k_reward` tool after step by step solving the question, "
                            "before generate final answer at least once and refine your answer if necessary. "
                            "Put your final answer in the format of `#### <answer>`."
                        ),
                    },
                    {
                        "role": "user",
                        "content": question,
                    },
                ],
                "ability": "math",
                "reward_model": {"style": "rule", "ground_truth": solution},
                "extra_info": {
                    "split": split,
                    "index": idx,
                    "answer": answer_raw,
                    "question": question_raw,
                    "need_tools_kwargs": True,
                    "tools_kwargs": {
                        "calc_gsm8k_reward": {
                            "create_kwargs": {"ground_truth": solution},
                            # "execute_kwargs": {},
                            # "calc_reward_kwargs": {},
                            # "release_kwargs": {},
                        },
                    },
                    "interaction_kwargs": {
                        "query": question,
                        "ground_truth": solution,
                    },
                },
            }
            return data

        return process_fn

    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)

    hdfs_dir = args.hdfs_dir
    local_save_dir = args.local_dir
    if local_save_dir is not None:
        print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.")
    else:
        local_save_dir = args.local_save_dir

    train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet"))
    test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet"))

    if hdfs_dir is not None:
        makedirs(hdfs_dir)
        copy(src=local_save_dir, dst=hdfs_dir)

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