trl/scripts/grpo.py

# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# /// script
# dependencies = [
#     "trl @ git+https://github.com/huggingface/trl.git",
#     "peft",
# ]
# ///

import argparse
import importlib
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer

from trl import GRPOConfig, GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
from trl.rewards import think_format_reward


reward_funcs_registry = {
    "think_format_reward": think_format_reward,
}


@dataclass
class GRPOScriptArguments(ScriptArguments):
    """
    Script arguments for the GRPO training script.

    Args:
        reward_model_name_or_path (`str` or `None`, *optional*, defaults to `None`):
            Reward model id of a pretrained model hosted inside a model repo on huggingface.co or local path to a
            directory containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`].
        reward_funcs (`list[str]` or `None`, *optional*, defaults to `None`):
            Reward functions to use. It can be either one of `"think_format_reward"`; or a dotted import path " (e.g.,
            `'my_lib.rewards.custom_reward'`).
    """

    reward_model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "Reward model id of a pretrained model hosted inside a model repo on huggingface.co or "
            "local path to a directory containing model weights saved using `PreTrainedModel.save_pretrained`."
        },
    )
    reward_funcs: Optional[list[str]] = field(
        default=None,
        metadata={
            "help": "Reward functions to use. It can be either one of  'think_format_reward'; or a dotted "
            "import path. (e.g., 'my_lib.rewards.custom_reward')."
        },
    )


def main(script_args, training_args, model_args):
    # Load a pretrained model
    model = AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
    )

    # Get the reward models and functions
    reward_funcs = []
    if script_args.reward_model_name_or_path:
        reward_model = AutoModelForSequenceClassification.from_pretrained(
            script_args.reward_model_name_or_path, trust_remote_code=model_args.trust_remote_code, num_labels=1
        )
        reward_funcs.append(reward_model)

    if script_args.reward_funcs:
        for func_name in script_args.reward_funcs:
            if func_name in reward_funcs_registry:
                reward_funcs.append(reward_funcs_registry[func_name])
            elif "." in func_name:
                module_path, func_name = func_name.rsplit(".", 1)
                sys.path.insert(0, os.getcwd())
                module = importlib.import_module(module_path)
                reward_func = getattr(module, func_name)
                reward_funcs.append(reward_func)
            else:
                raise ValueError(
                    f"Could not load reward function '{func_name}'. Expected one of "
                    f"{list(reward_funcs_registry.keys())} or a valid import path."
                )

    # Load the dataset
    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)

    # Initialize the GRPO trainer
    trainer = GRPOTrainer(
        model=model,
        reward_funcs=reward_funcs,
        args=training_args,
        train_dataset=dataset[script_args.dataset_train_split],
        eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
        processing_class=tokenizer,
        peft_config=get_peft_config(model_args),
    )

    # Train and push the model to the Hub
    trainer.train()

    # Save and push to hub
    trainer.save_model(training_args.output_dir)
    if training_args.push_to_hub:
        trainer.push_to_hub(dataset_name=script_args.dataset_name)


def make_parser(subparsers: argparse._SubParsersAction = None):
    dataclass_types = (GRPOScriptArguments, GRPOConfig, ModelConfig)
    if subparsers is not None:
        parser = subparsers.add_parser("grpo", help="Run the GRPO training script", dataclass_types=dataclass_types)
    else:
        parser = TrlParser(dataclass_types)
    return parser


if __name__ == "__main__":
    parser = make_parser()
    script_args, training_args, model_args = parser.parse_args_and_config()
    main(script_args, training_args, model_args)