# 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. """ import hydra import ray from .entropy_ray_trainer import RayEntropyTrainer from .reward import load_reward_manager @hydra.main(config_path="config", config_name="entropy_trainer", version_base=None) def main(config): run_ppo(config) def run_ppo(config) -> None: 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", "WANDB_API_KEY": "YOUR_WANDB_API_KEY", } }, num_cpus=config.ray_init.num_cpus, ) runner = TaskRunner.remote() ray.get(runner.run.remote(config)) def merge_dict(a: dict, b: dict) -> dict: """Return a new dict that has `a` updated with `b` (b wins on conflicts). Example:: >>> d1 = {"x": 1, "y": 2} >>> d2 = {"y": 20, "z": 3} >>> new_dict = merge_dict(d1, d2) >>> print(new_dict) # {'x': 1, 'y': 20, 'z': 3} >>> print(d1) # {"x": 1, "y": 2} (unchanged) >>> print(d2) # {"y": 20, "z": 3} (unchanged) """ return a | b @ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head class TaskRunner: def run(self, config): # print initial config from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_to_local 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) print(f"{config.actor_rollout_ref.model.path}") # instantiate tokenizer from verl.utils import hf_processor, hf_tokenizer trust_remote_code = config.data.get("trust_remote_code", False) tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) 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 in {"fsdp", "fsdp2"}: assert config.critic.strategy in {"fsdp", "fsdp2"} from verl.single_controller.ray import RayWorkerGroup from verl.workers.fsdp_workers import ActorRolloutRefWorker, AsyncActorRolloutRefWorker, CriticWorker actor_rollout_cls = ( AsyncActorRolloutRefWorker if config.actor_rollout_ref.rollout.mode == "async" else ActorRolloutRefWorker ) 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.single_controller.ray.megatron import NVMegatronRayWorkerGroup from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker actor_rollout_cls = ActorRolloutRefWorker ray_worker_group_cls = NVMegatronRayWorkerGroup else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role role_worker_mapping = { Role.ActorRollout: ray.remote(actor_rollout_cls), Role.Critic: ray.remote(CriticWorker), } 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, } # 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 in {"fsdp", "fsdp2"}: 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 # use 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_kwargs = { "max_resp_len": config.data.max_response_length, "overlong_buffer_cfg": config.reward_model.overlong_buffer, } cfg_reward_kwargs = config.reward_model.get("reward_kwargs", {}) reward_fn = load_reward_manager( config, tokenizer, num_examine=0, **OmegaConf.merge(OmegaConf.create(reward_kwargs), cfg_reward_kwargs) ) val_reward_fn = load_reward_manager(config, tokenizer, num_examine=1, **reward_kwargs) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) from verl.utils.dataset.rl_dataset import collate_fn train_dataset = create_rl_dataset(config.data.train_files, config.data, tokenizer, processor) val_dataset = create_rl_dataset(config.data.val_files, config.data, tokenizer, processor) train_sampler = create_rl_sampler(config.data, train_dataset) trainer = RayEntropyTrainer( 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, train_dataset=train_dataset, val_dataset=val_dataset, collate_fn=collate_fn, train_sampler=train_sampler, ) trainer.init_workers() trainer.fit() def create_rl_dataset(data_paths, data_config, tokenizer, processor): """Create a dataset. Arguments: data_config: The data config. tokenizer (Tokenizer): The tokenizer. processor (Processor): The processor. Returns: dataset (Dataset): The dataset. """ from torch.utils.data import Dataset from verl.utils.dataset.rl_dataset import RLHFDataset if "custom_cls" in data_config and data_config.custom_cls.get("path", None) is not None: from verl.utils.import_utils import load_extern_type dataset_cls = load_extern_type(data_config.custom_cls.path, data_config.custom_cls.name) if not issubclass(dataset_cls, Dataset): raise TypeError( f"The custom dataset class '{data_config.custom_cls.name}' from '{data_config.custom_cls.path}' " f"must inherit from torch.utils.data.Dataset" ) else: dataset_cls = RLHFDataset print(f"Using dataset class: {dataset_cls.__name__}") dataset = dataset_cls( data_files=data_paths, tokenizer=tokenizer, processor=processor, config=data_config, ) return dataset def create_rl_sampler(data_config, dataset): """Create a sampler for the dataset. Arguments: data_config: The data config. dataset (Dataset): The dataset. Returns: sampler (Sampler): The sampler. """ import torch from torch.utils.data import RandomSampler, SequentialSampler # use sampler for better ckpt resume if data_config.shuffle: train_dataloader_generator = torch.Generator() train_dataloader_generator.manual_seed(data_config.get("seed", 1)) sampler = RandomSampler(data_source=dataset, generator=train_dataloader_generator) else: sampler = SequentialSampler(data_source=dataset) return sampler if __name__ == "__main__": main()