# 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 .dapo_ray_trainer import RayDAPOTrainer import os import ray import hydra def get_custom_reward_fn(config): import importlib.util, os reward_fn_config = config.get("custom_reward_function") or {} file_path = reward_fn_config.get("path") if not file_path: return None if not os.path.exists(file_path): raise FileNotFoundError(f"Reward function file '{file_path}' not found.") spec = importlib.util.spec_from_file_location("custom_module", file_path) module = importlib.util.module_from_spec(spec) try: spec.loader.exec_module(module) except Exception as e: raise RuntimeError(f"Error loading module from '{file_path}': {e}") function_name = reward_fn_config.get("name") if not hasattr(module, function_name): raise AttributeError(f"Reward function '{function_name}' not found in '{file_path}'.") print(f"using customized reward function '{function_name}' from '{file_path}'") return getattr(module, function_name) @hydra.main(config_path='config', config_name='dapo_trainer', version_base=None) def main(config): run_ppo(config) def run_ppo(config) -> None: # TODO(linjunrong.ocss884): this ENV is left for resolving SGLang conflict with ray devices # isolation, will solve in the future os.environ["ENSURE_CUDA_VISIBLE_DEVICES"] = os.environ.get('CUDA_VISIBLE_DEVICES', '') if not ray.is_initialized(): # this is for local ray cluster ray.init(runtime_env={ 'env_vars': { 'TOKENIZERS_PARALLELISM': 'true', 'NCCL_DEBUG': 'WARN', 'VLLM_LOGGING_LEVEL': 'WARN' } }) runner = TaskRunner.remote() ray.get(runner.run.remote(config)) @ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head class TaskRunner: def run(self, config): from verl.utils.fs import copy_to_local # 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_to_local(config.actor_rollout_ref.model.path) # instantiate tokenizer from verl.utils import hf_tokenizer, hf_processor tokenizer = hf_tokenizer(local_path) processor = hf_processor(local_path, use_fast=True) # used for multimodal LLM, could be none # 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 # reference model if config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss: role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker) mapping[Role.RefPolicy] = global_pool_id reward_manager_name = config.reward_model.get("reward_manager", "naive") if reward_manager_name == 'naive': from verl.workers.reward_manager import NaiveRewardManager reward_manager_cls = NaiveRewardManager elif reward_manager_name == 'prime': from verl.workers.reward_manager import PrimeRewardManager reward_manager_cls = PrimeRewardManager elif reward_manager_name == 'dapo': from verl.workers.reward_manager import DAPORewardManager reward_manager_cls = DAPORewardManager else: raise NotImplementedError compute_score = get_custom_reward_fn(config) reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=0, compute_score=compute_score, reward_fn_key=config.data.reward_fn_key, max_resp_len=config.data.max_response_length, overlong_buffer_cfg=config.reward_model.overlong_buffer) # Note that we always use function-based RM for validation val_reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=1, compute_score=compute_score, reward_fn_key=config.data.reward_fn_key, max_resp_len=config.data.max_response_length, overlong_buffer_cfg=config.reward_model.overlong_buffer) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) trainer = RayDAPOTrainer(config=config, tokenizer=tokenizer, processor=processor, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn) trainer.init_workers() trainer.fit() if __name__ == '__main__': main()