reasoning-gym/examples/veRL/main_ppo_custom_reward.py

# This example is a modified version of:
# https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py


# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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.
"""
Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
"""

from typing import Optional
from omegaconf import OmegaConf, open_dict
import reasoning_gym
from reasoning_gym.utils import extract_answer

import reasoning_gym.utils
from verl import DataProto
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import PreTrainedTokenizer

import ray
import hydra


from verl.trainer.ppo.ray_trainer import RayPPOTrainer
from verl.utils.model import compute_position_id_with_mask
from verl.utils.dataset.rl_dataset import collate_fn
import verl.utils.torch_functional as verl_F


class RewardManager:
    """The reward manager."""

    def __init__(self, tokenizer, num_examine, compute_score) -> None:
        self.tokenizer = tokenizer
        self.num_examine = num_examine  # the number of batches of decoded responses to print to the console
        self.compute_score = compute_score

    def __call__(self, data: DataProto):
        """We will expand this function gradually based on the available datasets"""

        # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
        if "rm_scores" in data.batch.keys():
            return data.batch["rm_scores"]

        reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)

        already_print_data_sources = {}

        for i in range(len(data)):
            data_item = data[i]  # DataProtoItem

            prompt_ids = data_item.batch["prompts"]

            prompt_length = prompt_ids.shape[-1]

            valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum()
            valid_prompt_ids = prompt_ids[-valid_prompt_length:]

            response_ids = data_item.batch["responses"]
            valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum()
            valid_response_ids = response_ids[:valid_response_length]

            # decode
            sequences = torch.cat((valid_prompt_ids, valid_response_ids))
            sequences_str = self.tokenizer.decode(sequences)

            data_source = data_item.non_tensor_batch["data_source"]
            ground_truth = data_item.non_tensor_batch["answer"]
            index = data_item.non_tensor_batch["index"]

            score = self.compute_score(
                data_source=data_source,
                solution_str=sequences_str,
                ground_truth=ground_truth,
                index=index,
            )
            reward_tensor[i, valid_response_length - 1] = score

            if data_source not in already_print_data_sources:
                already_print_data_sources[data_source] = 0

            if already_print_data_sources[data_source] < self.num_examine:
                already_print_data_sources[data_source] += 1
                print(sequences_str)

        return reward_tensor


@hydra.main(config_path="config", config_name="ppo_trainer", version_base=None)
def main(config):
    if not ray.is_initialized():
        # this is for local ray cluster
        ray.init(runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}})

    ray.get(main_task.remote(config))


class ReasoningGymDataset(Dataset):
    def __init__(
        self,
        dataset_name: str,
        tokenizer: PreTrainedTokenizer,
        seed: int,
        size: int,
        developer_prompt: Optional[str] = None,
        developer_role: str = "system",
        max_prompt_length: int = 2048,
        truncation: str = "error",  ##  ['left', 'right', 'error']
        return_raw_chat: bool = False,
    ):
        self.tokenizer = tokenizer
        self.dataset_name = dataset_name
        self.data = reasoning_gym.create_dataset(dataset_name, seed=seed, size=size)
        self.developer_prompt = developer_prompt
        self.developer_role = developer_role
        self.max_prompt_length = max_prompt_length
        self.truncation = truncation
        self.return_raw_chat = return_raw_chat

    def __len__(self) -> int:
        return len(self.data)

    def __getitem__(self, index):
        row_dict = self.data[index].copy()
        q = row_dict["question"]

        chat = []
        if self.developer_prompt is not None:
            chat.append({"role": self.developer_role, "content": self.developer_prompt})
        chat.append({"role": "user", "content": q})

        prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

        input_ids, attention_mask = verl_F.tokenize_and_postprocess_data(
            prompt=prompt,
            tokenizer=self.tokenizer,
            max_length=self.max_prompt_length,
            pad_token_id=self.tokenizer.pad_token_id,
            left_pad=True,
            truncation=self.truncation,
        )

        position_ids = compute_position_id_with_mask(attention_mask)

        row_dict["data_source"] = "reasoning_gym/" + self.dataset_name
        row_dict["input_ids"] = input_ids[0]
        row_dict["attention_mask"] = attention_mask[0]
        row_dict["position_ids"] = position_ids[0]

        # encode prompts without chat template
        if self.return_raw_chat:
            row_dict["raw_prompt"] = chat.tolist()

        # add index for each prompt
        #  index = row_dict.get("extra_info", {}).get("index", 0)
        row_dict["index"] = index

        return row_dict


class RayPPOTrainerCustom(RayPPOTrainer):
    def __init__(
        self,
        config,
        tokenizer,
        role_worker_mapping: dict,
        resource_pool_manager,
        ray_worker_group_cls,
        reward_fn=None,
        val_reward_fn=None,
        dataset_name: str = "chain_sum",
        dataset_size: int = 10000,
    ):
        self.dataset_name = dataset_name
        self.dataset_size = dataset_size

        developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
        self.train_dataset = ReasoningGymDataset(
            dataset_name=self.dataset_name,
            tokenizer=tokenizer,
            seed=1,
            size=self.dataset_size,
            developer_prompt=developer_prompt,
        )

        self.val_dataset = ReasoningGymDataset(
            dataset_name=self.dataset_name,
            tokenizer=tokenizer,
            seed=2,
            size=self.dataset_size,
            developer_prompt=developer_prompt,
        )

        reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0, compute_score=self._compute_score)

        super().__init__(
            config,
            tokenizer,
            role_worker_mapping,
            resource_pool_manager,
            ray_worker_group_cls,
            reward_fn,
            val_reward_fn,
        )

    def _compute_score(self, data_source, solution_str, ground_truth, index) -> float:
        print("Solution:", solution_str, ground_truth, index, data_source)
        found_answer = extract_answer(solution_str, tag_name="answer")
        entry = self.train_dataset.data[index]
        return self.train_dataset.data.score_answer(found_answer, entry=entry)

    def _create_dataloader(self):
        self.train_dataloader = DataLoader(
            dataset=self.train_dataset,
            batch_size=self.config.data.train_batch_size,
            shuffle=True,
            drop_last=True,
            collate_fn=collate_fn,
        )

        self.val_dataloader = DataLoader(
            dataset=self.val_dataset,
            batch_size=len(self.val_dataset),
            shuffle=True,
            drop_last=True,
            collate_fn=collate_fn,
        )

        assert len(self.train_dataloader) >= 1
        assert len(self.val_dataloader) >= 1

        print(f"Size of train dataloader: {len(self.train_dataloader)}")
        print(f"Size of val dataloader: {len(self.val_dataloader)}")

        # inject total_training_steps to actor/critic optim_config. This is hacky.
        total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs

        if self.config.trainer.total_training_steps is not None:
            total_training_steps = self.config.trainer.total_training_steps

        self.total_training_steps = total_training_steps
        print(f"Total training steps: {self.total_training_steps}")

        OmegaConf.set_struct(self.config, True)
        with open_dict(self.config):
            self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps
            self.config.critic.optim.total_training_steps = total_training_steps


@ray.remote
def main_task(config, compute_score=None):
    from verl.utils.fs import copy_local_path_from_hdfs
    from transformers import AutoTokenizer

    # print initial config
    from pprint import pprint
    from omegaconf import OmegaConf

    pprint(OmegaConf.to_container(config, resolve=True))  # resolve=True will eval symbol values
    OmegaConf.resolve(config)

    # download the checkpoint from hdfs
    local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)

    # instantiate tokenizer
    from verl.utils import hf_tokenizer

    tokenizer = hf_tokenizer(local_path)

    # define worker classes
    if config.actor_rollout_ref.actor.strategy == "fsdp":
        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
        from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
        from verl.single_controller.ray import RayWorkerGroup

        ray_worker_group_cls = RayWorkerGroup

    elif config.actor_rollout_ref.actor.strategy == "megatron":
        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
        from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
        from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup

        ray_worker_group_cls = NVMegatronRayWorkerGroup

    else:
        raise NotImplementedError

    from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role

    role_worker_mapping = {
        Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
        Role.Critic: ray.remote(CriticWorker),
        Role.RefPolicy: ray.remote(ActorRolloutRefWorker),
    }

    global_pool_id = "global_pool"
    resource_pool_spec = {
        global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
    }
    mapping = {
        Role.ActorRollout: global_pool_id,
        Role.Critic: global_pool_id,
        Role.RefPolicy: global_pool_id,
    }

    # we should adopt a multi-source reward function here
    # - for rule-based rm, we directly call a reward score
    # - for model-based rm, we call a model
    # - for code related prompt, we send to a sandbox if there are test cases
    # - finally, we combine all the rewards together
    # - The reward type depends on the tag of the data
    if config.reward_model.enable:
        if config.reward_model.strategy == "fsdp":
            from verl.workers.fsdp_workers import RewardModelWorker
        elif config.reward_model.strategy == "megatron":
            from verl.workers.megatron_workers import RewardModelWorker
        else:
            raise NotImplementedError
        role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
        mapping[Role.RewardModel] = global_pool_id

    # Note that we always use function-based RM for validation
    val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1, compute_score=compute_score)

    resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)

    trainer = RayPPOTrainerCustom(
        config=config,
        tokenizer=tokenizer,
        role_worker_mapping=role_worker_mapping,
        resource_pool_manager=resource_pool_manager,
        ray_worker_group_cls=ray_worker_group_cls,
        val_reward_fn=val_reward_fn,
    )
    trainer.init_workers()
    trainer.fit()


if __name__ == "__main__":
    main()